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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
×
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
×
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
×
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
×
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
×
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
×
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
×
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Suggested Citation:"Chapter 5 - Application of Methods." National Academies of Sciences, Engineering, and Medicine. 2014. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/22330.
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59 5.1 Introduction This chapter is the core of the Guidebook. It distills all of the theory, empirical factors, and modeling research from the pre- ceding chapters into a set of methods and guidelines to address planning questions related to non-motorized travel demand. The guidebook contains a set of analytic techniques, con- stituting a toolbox of options with different capabilities, accu- racy potentials, data needs, and technical skill requirements. Although a single all-purpose tool to anchor the guidebook would have been ideal, it was not realistic given the range of planning needs to be served versus the state of the practice (tools and data) from which the project started. Several of the tools in the set—in particular the new tour-generation/mode split and the GIS-accessibility approaches—could become fully inte- grated, all-purpose models. Such advancement is anticipated beyond the scope of the current project, as familiarity with the tools grows through application and they become integrated within transportation or land use planning software packages. Until universal tools become available, the collection of tools in this guidebook offer a credible means to address a wide range of planning questions related to bicycle and pedestrian travel behavior and demand. Table 5-1 provides a listing and short description of the tools in the guidebook. Collectively, these tools can address the following types of planning issues: • Land Use: Evaluate the impact of land use (density, mix, design) on bicycle or pedestrian trip generation and mode choice, as well as assess how increased non-motorized travel activity supports the viability of compact, mixed land use (AKA, smart growth) policies. • Facilities: Plan effective bicycle and pedestrian networks based on (1) maximizing accessibility to opportunities, (2) emphasizing connectivity, and (3) taking account of user preferences regarding facility type, buffering from traf- fic, steep grades, and efficient crossings. • Transit: Assess the importance of non-motorized access to/ from and accessibility (nearby opportunities) on the viabil- ity of transit. • Travel Markets: Address the differential demand for walk- ing and biking across different trip purposes. • Traveler Characteristics: Account for sociodemographic differentials when assessing bike/pedestrian demand poten- tial simultaneous with the accounting for quality of bike/ pedestrian opportunity afforded by both the travel network and the built environment which it serves. • Scenario Planning and Visioning: Support interactive land use and transportation planning among diverse stake- holders at the regional, corridor, subarea, neighborhood, or project level. The rest of this chapter will (1) guide the practitioner in understanding the capabilities of the individual tools, (2) help in determining which tool or tools to select for a particular application need, and (3) provide step-by-step guidance in accessing and using the selected technique. The material is organized into the following sections: • Section 5.2—Comparison of Tool Properties and Capa- bilities: The properties and characteristics of the suite of recommended tools are displayed in tables to provide a quick visual understanding of key properties and charac- teristics, alone and in relation to one another. • Section 5.3—Individual Tool Profiles: This section reor- ganizes the model information presented in the Section 5.2 tables into the form of individual model profiles, where all of the information for the given tool is condensed into a single fact sheet. • Section 5.4—Guidelines for Model Selection: This section provides suggestions and protocols on how to select the right tool or tools from the listings in Sections 5.2 and 5.3, fol- lowed by general guidelines on how to use the tools for plan- ning and analysis. C H A P T E R 5 Application of Methods

60 • Section 5.5—Guidelines for Use: This section provides step-by-step guidance for applying each of the tools. The appendixes to the Guidebook contain all key equations, elasticities where available, and calibration statistics. 5.2 Comparison of Tool Properties and Capabilities This section presents summary information on the prop- erties and characteristics of each of the methods, portrayed in table format to facilitate comparison across tools. Tables provide the following information: • Table 5-2: Model Type and Geographic Scale: Cites the form and distinguishing characteristics of each tool, the level of aggregation at which it operates, and the most suit- able geographic scales for its application. • Table 5-3: Modeling Steps Impacted: Lists the modeling steps or elements exercised by the model (e.g., auto own- ership, trip generation, distribution, mode choice, time of day and assignment, as well as trip purpose definitions and use of accessibility relationships). • Table 5-4: Planning Applications: Suggests suitability for use in a set of 11 illustrative planning applications. Suit- ability is denoted as being directly applicable (D), having Modeling Approach Source Characteriscs Tour Generaon/ Mode Split NCHRP 8 78 (Seale/PSRC data) Simple/complex tour generaon for 8 trip purposes (sociodemographic characteriscs, land use, local & regional accessibility) Mode choice (walk, bike, transit, auto) for 5 trip purposes (sociodemographics, land use, local & regional accessibility, Fully detailed walk and bicycle networks, physical aributes affect impedance GIS Accessibility Model NCHRP 8 78 (Arlington, VA/MWCOG data) Uses GIS layering to create accessibility scores for walk, bike, transit, and auto. Links mode choice with accessibility scores at trip origin and desnaon Esmates mode share at block level for HBW, HBO, NHB and WBO purposes Builds walk trip table (but does not assign) Highly visual presentaon Trip Based Model Enhancements NCHRP 8 78 (Seale/PSRC data) Strategic changes to tradional four step TAZ model to improve sensivity to land use and non motorized travel Sensizes auto ownership and trip generaon to land use characteriscs Performs pre mode choice to disnguish inter versus intrazonal trips Performs mode choice separately for intra zone (drive alone, shared ride, walk) and inter zone (drive, shared ride, transit, walk, bike) travel Pedestrian Demand Models PedContext and MoPeD (Univ. of MD/ Maryland DOT) Portland Pedestrian Model (PSU) Modified four step approach focused on esmang walk trips Walk trip generaon for several purposes at PAZ level Creates walk trip tables, assigns trips to walk network Bicycle Route Choice Models San Francisco County Transp. Authority; Portland State Univ. Models built from GPS recorded trip data to predict choice of route for bicycle riders Quanfies importance of route characteriscs (type facility, gradient, directness, traffic exposure) Facility Demand Models Santa Monica Bicycle and Pedestrian Models (Fehr & Peers) Seamless Travel Bicycle and Pedestrian Models (Alta Planning & UC Berkeley) Separate bicycle and pedestrian direct demand models Predict PM peak hour bicycle demand based on employment density, proximity to bike facilies, land use mix, and intersecons Predict PM peak hour walk demand based on employment density, proximity to shopping, PM bus frequency, and traffic speeds Table 5-1. Summary of NCHRP 8-78 guidebook bicycle/pedestrian planning tools.

61 a significant assisting role but probably unable to perform the entire task alone (A), having a partial but potentially useful role (P), or having no obvious role (N). • Table 5-5: Key Indicators: Lists the principal output measures and performance metrics generated by the res- pective model to support the planning applications in Table 5-4. • Table 5-6: Variable Sensitivities: Presents the generic types of factors (e.g., sociodemographics, land use, and transportation network) and specific variables in those categories to which the models are sensitive. • Table 5-7: Data Requirements: Summarizes the various types and sources of data needed by the respective tools for development, transfer, or validation. Disaggregate Tour based (Seale) GIS Based Accessibility (Arlington) Enhanced Trip Based (Seale) Pedestrian Model (Portland) Walk Models (PedContext & MoPeD) Bike Route Choice (SFCTA/PSU) Direct Demand (Various) Model Type Compr. Tour Generaon, Mode Choice Full GIS based Compr. Trip Generaon, Mode Choice, Distribuon Trip Gen, Inter versus Intra Zonal Distribuon, Mode Choice Context Based Index Method For Walk Trip Generaon Walk Trip Generaon, Distribuon, Assignment Explain Route Choice from Path Aributes Explain bike or walk link or intersecon counts through regression with context measures Aggregaon Level Parcel Block TAZ PAZ PAZ Link Intersecon or Link Geographic Scale Regional Y Y Y Corridor Y Y Y Y Y Y Subarea Y Y Y Y Y Y Y Project Site Y* Y Y* Y* Y Y Facility/Point Y* Y* Y* Y* Y Y * = Model outputs may be used for assignment in host model (given availability of a non-motorized network) Table 5-2. Model type and geographic scale. Disaggregate Tour based (Seale) GIS Based Accessibility (Arlington) Enhanced Trip Based (Seale) Pedestrian Model (Portland) 4 Step Walk Model (PedContext) 4 Step Walk Model (MoPeD) Bike Route Choice (SFCTA/PSU) Direct Demand (Various) Auto Ownership Y N Y N N Y N N Accessibility Y Y Y Y Y Y Y1 Y2 Trip/tour Generaon Y Y Y(W, B) Y(W) Y(W) Y(W) N N Trip/Tour Purpose Y Y Y Y Y Y Y N Distribuon/Trip Tables N Y N N Y Y N N Mode Choice Y Y Y Y (W, NW) N N N Y Time of Day N N N N Y N N Y Non Motorized Definion W, B W W, B W W W B W, B Assignment/Facil Volumes N N N N Y Y Y Y Notes: 1 Assist in valuing travel me/distance 2 Aggregate W, B = Walk, Bike W, NW = Work, Non-work Table 5-3. Modeling steps impacted.

62 Disaggregate Tour based (Seale) GIS Based Accessibility (Arlington) Enhanced Trip Based (Seale) Pedestrian Model (Portland) 4 Step Walk Model (PedContext) 4 Step Walk Model (MoPeD) Bike Route Choice (SFCTA/PSU) Direct Demand (Various) Regional Plan Development D A D A A A P P Scenario Planning/ Visioning D D A A A A P P Land Use/Smart Growth/TOD D D A A A A P P Mulmodal Corridor Studies D D A P A A A P Transit Planning A A A P A A A A Mulmodal Accessi bility & Equity D D A A A A A A Local Comp or Master Plans D D A A D D A P Site Planning & Traffic Impact Migaon A* A* A A D D A P Bicycle or Pedestrian Facility Planning A* A* P A* D D D A NMT Facility Priorizaon A* A* P A* D D A A Intersecon Acvity Levels for Safety Analysis A* A* N A* D D A D Applicability Codes: Notes: D = Direct role P = Paral role, can contribute * – Needs to be accompanied by assignment program A = Key assisng role N = Not an obvious role Table 5-4. Planning applications. Indicators Disaggregate Tour based (Seale) GIS Based Accessibility (Arlington) Enhanced Trip Based (Seale) Pedestrian Model (Portland) 4 Step Walk Model (PedContext) 4 Step Walk Model (MoPeD) Bike Route Choice (SFCTA/PSU) Direct Demand (Various) Mode Split (shares) Y Y Y Y N N N N Vehicle Trips Y Y Y N N N N N Transit Trips Y Y Y N N N N N Bike Trips Y N Y N N N N Y Walk Trips Y Y Y Y Y Y N Y VMT N N N N N N N N Bike Link Volumes N1 N N N N N Y Y Ped Link Volumes N1 N1 N N Y Y N Y Walk Intersecon Volumes N1 N1 N N Y Y N Y Notes: 1 – Would need to be coupled with route assignment model Table 5-5. Key indicators.

63 Disaggregate Tour based (Seale) GIS Based Accessibility (Arlington) Enhanced Trip Based (Seale) Pedestrian Model (Portland) 4 Step Walk Model (PedContext) 4 Step Walk Model (MoPeD) Bike Route Choice (SFCTA/PSU) Direct Demand (Various) Sociodemographic Age, Gender, F/PT Worker, Student, Rered, Income, Auto Ownership HHs by Auto Ownership (trip gen) HH size, Workers, Drivers, Income, Age/Gender, Employed HH size, Autos Income, Workers, Age Head, Children Income,, HH size, age, workers, children, autos Block level Households & Employment by type, DUs, Auto Ownership Gender, Commuter (SFCTA only) Varies: e.g., 0 vehicle HHs; density of persons < 18 Local Accessibility Purpose specific buffered acvity for W,B Purpose specific acvity sums for W, B NMT Accessibility Index Buffered Pop &Emp Block level walk accessibility to MFDUs, Total or Retail Emp Block to block network distance, exponenal decay None Proximity to major generators & aractors Regional Accessibility General and purpose specific logsums Purpose specific acvity sums for Auto, Transit SOV Accessibility Index None None None None Generally not included Built Environment Characteriscs Pop & Emp densies, Entropy, Intersecons, Transit stops Number Establishments or Employees by 4 digit NAICS within walking range Pop & Emp densies; Intersecons; Transit proxim; Parking Supply & $; Home loc indicator Pop & Emp densies, Transit proximity, Urban Infrstruct Block level Pop, Dwelling units (SF, MF); Floor area; Emp by type Pop & Emp densies, Intersecon density None Pop & Emp Density, LU mix, intersecons, transit proximity & availability, Impedance Logisc decay of travel distance Logarithmic decay of travel me Logisc decay of travel distance None Gamma decay of travel me Exponenal decay of walk network distance Imputed by individual factor values None Walk Facility Characteriscs Sidewalk coverage ; traffic speed Shortest me path; crossings None Sidewalk density, block size Sidewalk “Quality”. Crossings; Traffic vols & speed Road layer converted to sidewalk network with crossings None Intersecon design, traffic, signalizaon, facility type, Bicycle Facility Characteriscs Average rise, Cl I or II Paths, Wrong way %, Turns per mile Shortest me path; crossings None Included in pedestrian environment (PIE) index None None Facility type, slope, turns, wrong way, crossing AADT Facility type, nearness, proximity to traffic, turns & crossings W, B = Walk, Bike Table 5-6. Variable sensitivities.

64 Disaggregate Tour based (Seale) GIS Based Accessibility (Arlington) Enhanced Trip Based (Seale) Pedestrian Model (Portland) 4 Step Walk Model (PedContext) 4 Step Walk Model (MoPeD) Bike Route Choice (SFCTA/PSU) Direct Demand (Various) Travel Survey Y1 Y1 Y1 N Y1 Y1 Y6 N Parcel level Land Use Y Y1 Y1 N N5 Y1 N N Census populaon & employment Y Y2 Y Y Y Y N Y Transit system & stop locaons Y N4 Y Y7 N N N Y All streets network (GIS) Y Y Y Y Y Y Y Y Regional TAZ data & travel skims Y N Y N N N N N Walk link characteriscs Y8 N N Y Y Y N Y Bike Link characteriscs Y9 N N Y N N Y Y Crossings and intersecon locaon & characteriscs Y N N N Y Y Y Y Acvity counts N N N Y3 Y3 Y3 Y Y Notes: 1 – Needed for model calibraon or transfer 4 – Only if calculang transit accessibility 7 – Stops only 2 Need for applicaon 5 – Block level data is sufficient 8 – Sidewalk coverage, speed limits 3 – Need for validaon 6 – GPS rider data 9 – Grade, facil type, turns, wrong way – Table 5-7. Data requirements.

65 5.3 Individual Tool Profiles This section condenses and supplements the information presented in the preceding tables into a separate fact sheet, or profile, for each method. The profiles describe the strengths and weakness of each technique, which should help users when selecting methods. Tour-Generation and Mode-Choice Models Description: This tool uses a highly disaggregated modeling approach—individual tour generation and mode choice at the parcel level—to account for the many factors that affect bicycle and pedestrian travel choice, particularly land use and network connectivity through measures of both local and regional accessibility. The tool offers insights on the importance of particular bicycle and pedestrian network characteristics in valuing travel time, which is critical for measuring accessibility and when designing effective network enhancements. The procedure may be applied in full tour-based form (proper model platform required), or used to enhance existing tour or trip-based models, either through application of the full models, individual elasticities, or the custom spreadsheet provided with the guidebook. Geographic Scale:  Regional  Corridor  Subarea  Project/Site  Facility/Point Planning Applications:  Scenario Planning  Smart Growth/TOD  Transit  Comp/Master Plans  Traffic Impact Mitigation  NMT Facility Planning  Safety Analysis  Equity Forecasting Elements:  Auto Ownership  Trip Generation  Distribution  Mode Choice  Assignment Indicators and Metrics:  Mode Shares  Walk Trips  Bike Trips  Vehicle Trips  Transit Trips  VMT  Walk Link Volumes  Bike Link Volumes  Intersection Volumes Trip Purposes  Work  School  Other  Recreation  Work-based  Non-home-based Model Relationships and Sensitivity: Land Use:  High  Medium  Low Non-Motorized Network:  High  Medium  Low Accessibility:  High  Medium  Low Sociodemographics:  High  Medium  Low

66 Data Requirements:  Travel Surveys  Parcel-Level Land Use  Census Population  All-Streets Network in GIS format & Employment  Bike Link Characteristics3  Walk Link Characteristics2  Regional Model TAZ data & Skims  Transit Stop Locations (for accessibilities) Tools & Expertise:  Travel Modeling  GIS Tools & Expertise  Data Management Strengths • Highly insightful into the choice of travel modes based on travelers’ assessment of local and regional opportunities and benefits and traveler/household needs (e.g., combining trips or chauffeuring passengers). • Deals directly with land use and network accessibility, at both the communitywide and regional level. • Distinguishes between traveler choice of simple versus complex tours, which are predicated on local land use and which have strong implications for mode choice for specific trip purposes (work, school, shop, work-based, and other). • Captures important physical attributes of bicycle or pedestrian networks that affect acces- sibility, (e.g., directness and trip length, slope, presence of sidewalks and Class I and Class II bikeways, and concentrations of population and employment). • Accounts for traveler socioeconomic factors (e.g., gender, work status, household size and composition, income, and vehicle availability). Weaknesses • Complete replication of the methods would require substantial resources in terms of data avail- ability, analytic expertise, software and (potentially) hardware investment, and so would be most appropriate for areas that already have or are contemplating an activity or tour-based model platform. However, transfers and partial applications may be done with considerably less effort. • The best application works within a tour- or activity-based model environment, based on definitional issues distinguishing tours from trips; however, this problem can be overcome with some simplification of assumptions. • Ideal application would require development and use of a synthetic population of individuals, given that the models are most relevant when applied to individuals as opposed to households (important individual characteristics are lost) or zones (aggregation affects accuracy). • To obtain estimates of area-specific or facility-specific use, additional tools are required for destination choice and route choice, coupled with validation of the resulting estimates. GIS-Accessibility Tool Description: This tool relies almost entirely on GIS tools and data to create relationships between land use activ- ity, accessibility to opportunities defined by the shape and service of the transportation networks, and mode choice. The tool focuses on a walk-accessibility score—similar to, but more informed than, the Walk Score program on the internet—to estimate walk potential and mode choice. Block- level walk trip tables are created, which can be assigned to a network (feature not included). 2 Sidewalk coverage; speed range of adjacent traffic 3 Class I or II bike lane; elevation gain, number of turns, fraction wrong way

67 Geographic Scale:  Regional  Corridor  Subarea  Project/Site  Facility/Point Planning Applications:  Scenario Planning  Smart Growth/TOD  Transit  Comp/Master Plans  Traffic Impact Mitigation  NMT Facility Planning  Safety Analysis  Equity Forecasting Elements:  Auto Ownership  Trip Generation  Distribution  Mode Choice  Assignment Indicators and Metrics:  Mode Shares  Walk Trips  Bike Trips  Vehicle Trips  Transit Trips  VMT  Walk Link Volumes  Bike Link Volumes  Intersection Volumes Trip Purposes  Work  School  Other  Recreation  Work-based  Non-home-based Model Relationships and Sensitivity: Land Use:  High  Medium  Low Non-Motorized Network:  High  Medium  Low Accessibility:  High  Medium  Low Sociodemographics:  High  Medium4  Low Data Requirements:  Travel Surveys  Parcel-Level Land Use5  Census Population  All-Streets Network in GIS format & Employment  Bike Link Characteristics  Walk Link Characteristics  Regional Model TAZ data & Skims  Transit Stop Locations (for accessibilities) Tools & Expertise:  Travel Modeling  GIS Tools & Expertise  Spreadsheet Mechanics Strengths • GIS approach in many ways is more intuitive and realistic than working with TAZ-based travel models; it accomplishes through geospatial relationships what requires considerable coding and computation in conventional models. • Calibration requires travel survey and GIS network data, but once calibrated, typical applica- tion is at a much simpler block level. 4 Trip generation only at present, not mode choice 5 Uses Dun & Bradstreet employment data for model calibration (employer by NAICs code, number employees, and latitude/longitude location)

68 • Accessibility framework implicitly and simultaneously accounts for both land use and network coverage/quality factors; provides a natural platform for collaborative community planning. • Separately accounts for four trip purposes: home-based work, home-based non-work travel, work-based, and non-home-based travel. • Both origin and destination accessibilities are considered when calculating mode-split. • Requires GIS tools and knowledge, but requirements are fairly standard. • Spreadsheet version of model provided with test data and examples. Weaknesses • Estimates walk travel only, not bike. • Does not account for sociodemographics directly in mode choice, but indirectly through trip generation. • Does not account for link characteristics (e.g., facility type or gradient), although these could be easily added to the calculation of link impedance. • Uses trip generation equations from MPO model to estimate total person trip generation, from which walk trips are then extracted/estimated. • Generates walk trip tables but does not include an internal assignment program to estimate facility volumes (access to external program required for this step). Seattle Enhancements to Trip-Based Model Description: This approach illustrates how sensitivity in traditional TAZ-level trip-based models can be strate- gically enhanced by introduction of land use and accessibility measures at the auto ownership, trip generation, and mode split steps. Instead of being discarded following trip generation, non-motor- ized trips are taken forward into mode-choice analysis by separation into groupings of intrazonal and interzonal trip types. Geographic Scale:  Regional  Corridor  Subarea  Project/Site  Facility/Point Planning Applications:  Scenario Planning  Smart Growth/TOD  Transit  Comp/Master Plans  Traffic Impact Mitigation  NMT Facility Planning  Safety Analysis  Equity Forecasting Elements:  Auto Ownership  Trip Generation  Distribution  Mode Choice  Assignment Indicators and Metrics:  Mode Shares  Walk Trips  Bike Trips  Vehicle Trips  Transit Trips  VMT  Walk Link Volumes  Bike Link Volumes  Intersection Volumes Trip Purposes  Work  School  Other  Recreation  Work-based  Non-home-based

69 Model Relationships and Sensitivity: Land Use:  High  Medium  Low Non-Motorized Network:  High  Medium  Low Accessibility:  High  Medium  Low Sociodemographics:  High  Medium  Low Data Requirements:  Travel Surveys  Parcel-Level Land Use  Census Population  All-Streets Network in GIS format & Employment  Bike Link Characteristics  Walk Link Characteristics  Regional Model TAZ data & Skims  Transit Stop Locations (for accessibilities) Tools & Expertise:  Travel Modeling  GIS Tools & Expertise  Data Management Strengths • Can be emulated for most urban area models in the United States. About 90% of MPOs employ trip-based as opposed to activity-based models. • Emulates a full travel decision process: vehicle ownership, trip generation, destination choice, and mode choice. • Estimates are sensitive to traveler demographics, including age, gender, income, and auto availability. • Mode choice and destination choice estimates are sensitive to localized built-environment factors at trip origin and destination, including development density, land use mix, local street connectivity, and transit availability. • Allows travel route assignment and facility-use estimates (but only for motorized modes and only for interzonal travel). Weaknesses • Effects of TAZ size on key relationships (accessibility, intra- versus interzonal trip-making) not controlled for. • Requires an extensive regionwide geospatial database with parcel-based land use activity, local streets and paths, parking supply and cost, and bus stops. • The equations presented in the guidebook are not directly transferrable to other regions, because they are implicitly tied to the zone size and structure of the Seattle region. Develop- ing models of this type for another region would require including a zone-size variable in the equations, such as acreage or total population and employment. • Cannot report full effect of land use mix and trip destination employment density on travel by mode because of the way the effects are subdivided in the model. • Obtaining estimates of area-specific or facility-specific use requires additional tools for intra- zonal destination choice and route choice, followed by count-based model validation. Portland Pedestrian Model Enhancement Description: This is not so much a stand-alone tool as a creative enhancement for a trip-based model to improve its sensitivity for pedestrian analysis. The enhancement estimates pedestrian trip gen- eration at a block level through a combination of buffered land use and transportation system variables into a pedestrian index of the environment (PIE).

70 Geographic Scale:  Regional  Corridor  Subarea  Project/Site  Facility/Point Planning Applications:  Scenario Planning  Smart Growth/TOD  Transit  Comp/Master Plans  Traffic Impact Mitigation  NMT Facility Planning  Safety Analysis  Equity Forecasting Elements:  Auto Ownership  Trip Generation  Distribution  Mode Choice  Assignment Indicators and Metrics:  Mode Shares  Walk Trips  Bike Trips  Vehicle Trips  Transit Trips  VMT  Walk Link Volumes  Bike Link Volumes  Intersection Volumes Trip Purposes  Work  School  Other  Recreation  Work-based  Non-home-based Model Relationships and Sensitivity: Land Use:  High  Medium  Low Non-Motorized Network:  High  Medium  Low Accessibility:  High  Medium  Low Sociodemographics:  High  Medium  Low Data Requirements:  Travel Surveys  Parcel-Level Land Use  Census Population & Employment  All-Streets Network in GIS format  Walk Link Characteristics  Bike Link Characteristics  Transit Stop Locations  Regional Model TAZ data & Skims  Activity Counts Tools & Expertise:  Travel Modeling  GIS Tools & Expertise  Data Management Strengths • Brings scale of analysis to a block level of detail. • Pedestrian trip estimates can be used directly for scenario purposes or combined with regional model outputs to compute/adjust mode split. • Can operate independently of regional model, but operates well to serve it with pedestrian information. • Accounts for interactions among built-environment variables as revealed through correla- tions to choice of walking mode (Portland case) accounting for block size and sidewalk den- sity, transit access, population and employment density and concentrations of grocery stores, restaurants, retail stores, services, and schools.

71 Weaknesses • Does not predict bicycle trips. • Deals only with walk (versus non-walk) trip generation. • Does not directly create a walk trip table or assign to facilities. Model of Pedestrian Demand (MoPeD) Description: MoPeD is a method for estimating pedestrian trip generation at a block level, creating trip tables, and assigning those trips to a grid. It is a simplified four-step process for walk travel that offers good spatial resolution, incorporation of land use and network accessibility factors, and the ability to exercise those factors in assessments of changes in land use or network needs/ improvements. Geographic Scale:  Regional  Corridor  Subarea  Project/Site  Facility/Point Planning Applications:  Scenario Planning  Smart Growth/TOD  Transit  Comp/Master Plans  Traffic Impact Mitigation  NMT Facility Planning  Safety Analysis  Equity Forecasting Elements:  Auto Ownership  Trip Generation  Distribution  Mode Choice  Assignment Indicators and Metrics:  Mode Shares  Walk Trips  Bike Trips  Vehicle Trips  Transit Trips  VMT  Walk Link Volumes  Bike Link Volumes  Intersection Volumes Trip Purposes  Work  School  Other  Recreation  Work-based  Non-home-based Model Relationships and Sensitivity: Land Use:  High  Medium  Low Non-Motorized Network:  High  Medium  Low Accessibility:  High  Medium  Low Sociodemographics:  High  Medium  Low Data Requirements:  Travel Surveys  Parcel-Level Land Use  Census Population  All-Streets Network in GIS format & Employment  Bike Link Characteristics  Walk Link Characteristics  Regional Model TAZ data & Skims  Transit Stop Locations (for accessibilities)

72 Tools & Expertise:  Travel Modeling  GIS Tools & Expertise  Statistical Analysis skills Strengths • Similar in structure to four-step regional models, but functions at pedestrian scale of geospatial analysis using block-size PAZs. • Can focus in detail on the neighborhood of interest. • Accounts for sociodemographic characteristics (at block level) (e.g., household vehicle owner- ship, head of household age, and household size and income). • Performs facility-use assignments and estimates 24-hour pedestrian volumes on sidewalks and intersections. • GIS platform and open-source analysis routines make the method more available to planners and analysts without proficiency in regional travel models. Weaknesses • Estimates walk trips only, not bike. • Does not account for regional accessibility effects in competing for local walk trips; auto or transit not factored in as alternative choices. • Accessibility is based on spatial buffering of blocks, not parcels, and is not network path based. • Assignment process is based on shortest-path all-or-nothing rule; does not account for path characteristics. • Land use influences limited to block-level households and dwelling units, employment mix (retail, service, other) and intersection density. Maryland PedContext Model Description: This tool was a precursor to MoPeD and is much more detailed (and demanding) in each of its steps in estimating walk travel generation and the distribution of walk trips to a network. However, it offers a much higher level of precision in interpreting land use, network characteris- tics, and accessibility, and potentially a higher level of accuracy of the facility volume estimates. Geographic Scale:  Regional  Corridor  Subarea  Project/Site  Facility/Point Planning Applications:  Scenario Planning  Smart Growth/TOD  Transit  Comp/Master Plans  Traffic Impact Mitigation  NMT Facility Planning  Safety Analysis  Equity Forecasting Elements:  Auto Ownership  Trip Generation  Distribution  Mode Choice  Assignment Indicators and Metrics:  Mode Shares  Walk Trips  Bike Trips  Vehicle Trips  Transit Trips  VMT  Walk Link Volumes  Bike Link Volumes  Intersection Volumes

73 Trip Purposes  Work  School  Other  Recreation  Work-based  Non-home-based Model Relationships and Sensitivity: Land Use:  High  Medium  Low Non-Motorized Network:  High  Medium  Low Accessibility:  High  Medium  Low Sociodemographics:  High  Medium  Low Data Requirements:  Travel Surveys  Parcel-Level Land Use  Census Population & Employment  All-Streets Network in GIS format  Walk Link Characteristics  Bike Link Characteristics  Transit Stop Locations  Regional Model TAZ data & Skims  Facility activity counts Tools & Expertise:  Travel Modeling  GIS Tools & Expertise  Data Management Strengths • Replicates much of the familiar four-step process, but specifically at the pedestrian scale. • Land use and trip generation represented at block-level geography. • Highly detailed treatment of walk network using GIS, creative enhancements to quantify utility of sidewalks and crossings. • Extensive use of walk-accessibility measures in walk trip generation. • Accessibility measures are calculated using actual network travel times. • Trips for six different trip purposes (both home and non-home-based). • Trip distribution using different (locally derived) impedance functions for each trip purpose. • Uses a stochastic (iterative) multi-path network assignment process (using weighted imped- ances) to estimate 24-hour pedestrian volumes by link and intersection. Weaknesses • Although PedContext uses commonly available software (e.g., ArcView GIS and the Citi- labs CUBE and VIPER programs), the custom package includes processing innovations that are proprietary (at present) to the PedContext software. However, each of the steps is well explained in the documentation and can likely be emulated if the user chooses not to acquire the PedContext software. • Deals only with walk trips, and does not account for role of regional accessibility in competi- tion for other modes/destinations. • Requires a reasonable understanding of network development and assignment protocols to most easily begin to emulate or use. • The detail in land use relationships is aggregated at the block level, though given the small size of the blocks, this may be sufficient for most applications.

74 Bicycle Route Choice Models Description: These two tools (SFCTA and Portland) both used GPS methods to compile route choice data on a large sample of bicycle trips, which were then used to develop models of route choice incorpo- rating such attributes as directness, facility type (sidewalk and Class I, II, III bike paths), gradient, turns, and traffic exposure. The results can be used to assign value to these attributes in facility planning or to inform full-scale planning models with measures of weighted travel impedance. Geographic Scale:  Regional  Corridor  Subarea  Project/Site  Facility/Point Planning Applications:  Scenario Planning  Smart Growth/TOD  Transit  Comp/Master Plans  Traffic Impact Mitigation  NMT Facility Planning  Safety Analysis  Equity Forecasting Elements:  Auto Ownership  Trip Generation  Distribution  Mode Choice  Assignment Indicators and Metrics:  Mode Shares  Walk Trips  Bike Trips  Vehicle Trips  Transit Trips  VMT  Walk Link Volumes  Bike Link Volumes  Intersection Volumes Trip Purposes  Work  School  Other  Recreation  Work-based  Non-home-based Model Relationships and Sensitivity: Land Use:  High  Medium  Low Non-Motorized Network:  High  Medium  Low Accessibility:  High  Medium  Low Sociodemographics:  High  Medium  Low Data Requirements:  Travel Surveys (GPS)6  Parcel-Level Land Use  Census Population & Employment  All-Streets Network in GIS format  Walk Link Characteristics  Bike Link Characteristics  Transit Stop Locations  Regional Model TAZ data & Skims  Activity Counts Tools & Expertise:  Travel Modeling  GIS Tools & Expertise  Statistical Analysis skills 6 Required only to supply the data for model calibration, not for application

75 Strengths • Not of great direct value as a planning tool, but for the unique relationships it supplies on valuation of facility and network design features • Quantifies values of physical attributes of alternative routes using actual (revealed preference) data on observed trip-making • Weights calculated in relation to route choice can be used for facility/network design or com- paring project improvement alternatives • Weighted attributes can be used to sensitize travel impedances to reflect importance of path characteristics on value of travel time (procedure was used to develop bike network skims for Seattle Tour-based model) Weaknesses • Deals with bicycle only. • Deals only with route choice and not with overall choice of bicycle as mode, nor choice of destination in relation to bicycle accessibility. • Does not predict facility volumes. Direct Demand Facility Volume Models Description: This class of tools includes many examples, most of which are custom-developed for a par- ticular site and planning question. The tools are designed to forecast demand levels for walk or bike at a point or intersection level, usually to support traffic safety studies, although they are also used to evaluate and prioritize projects. Geographic Scale:  Regional  Corridor  Subarea  Project/Site  Facility/Point Planning Applications:  Scenario Planning  Smart Growth/TOD  Transit  Comp/Master Plans  Traffic Impact Mitigation  NMT Facility Planning  Safety Analysis  Equity Forecasting Elements:  Auto Ownership  Trip Generation  Distribution  Mode Choice  Assignment Indicators and Metrics:  Mode Shares  Walk Trips  Bike Trips  Vehicle Trips  Transit Trips  VMT  Walk Link Volumes  Bike Link Volumes  Intersection Volumes Trip Purposes (generally not determined)  Work  School  Other  Recreation  Work-based  Non-home-based

76 Model Relationships and Sensitivity: Land Use:  High  Medium  Low Non-Motorized Network:  High  Medium  Low Accessibility:  High  Medium  Low Sociodemographics:  High  Medium  Low Data Requirements:  Travel Surveys  Parcel-Level Land Use  Census Population & Employment  All-Streets Network in GIS format  Walk Link Characteristics  Bike Link Characteristics  Transit Stop Locations  Regional Model TAZ data & Skims  Activity Counts Tools & Expertise:  Travel Modeling  GIS Tools & Expertise  Statistical Analysis and Spreadsheet Skills Strengths • Convenient method for estimating the impact of an individual investment or accessibility improvement along a specific corridor or neighborhood, such as a Complete Street project, on usage levels. • Avoids complexity and coarseness associated with TAZ trip-based models; does not require traditional transportation modeling skills to develop or apply. • Provides a way of gauging effects of residential and non-residential development projects on pedestrian and bicycle activity levels and capacity needs. • Based on observed local walking and biking behavior rather than on self-reported travel (surveys). • Provides estimates of activity for specific time periods (e.g., A.M. peak or weekend). Weaknesses • Does not systematically link activity levels to elements of the decision-making process (trip generation, mode or destination choice) but rather through correlation with environmental factors believed to be causal (development levels, major generators, transit activity/use levels, population or employment subgroups) • Generally does not account for characteristics of the traveler or trip (e.g., socioeconomic factors, trip purpose, origin or destination); the models generally attempt to project usage levels based on correlative relationships • Does not directly account for network accessibility characteristics in ascertaining the abso- lute or relative value of individual link or intersection improvements, although this is a potential enhancement that should be given further study.

77 5.4 Guidelines and Suggestions for Model Selection and Use This section provides assistance in how to decide which of the various tools to use for a particular planning applica- tion, along with suggestions, caveats, and protocols to take into consideration when adapting or applying the given tool. Topics discussed include the following: • How to compare the capabilities of the guidebook tools, in relation to a choice-based behavioral framework, • Selecting the best approach for a particular geographic scale, • Trading off accuracy needs versus complexity and effort, • Ways to use the tools, and • Validation guidelines. Varied Tools for Varied Needs and Capabilities The user should view the tools in this guidebook as a hier- archy, beginning with the most comprehensive and tending to the most specific and focused—and potentially limited. Each comes with tradeoffs, articulated in the Strengths and Weaknesses assessment in the preceding model profiles. The more comprehensive tools will probably require more effort and expertise, but for particular planning or policy questions, they may be the only way to effectively address those issues. At the same time, some users will want to get as quickly as pos- sible to an answer—perhaps to support an impending or pre- liminary decision—who have neither the time nor resources for a full analysis and will want a simpler approach. To the extent possible, users should attempt to use one of the more comprehensive choice-based tools because of the demon- strated role of accessibility and how these tools coordinate land use and network relationships to employ accessibility consid- erations. If the tools at the top of the menu (tour-based or GIS- accessibility approach) cannot be used, then the next priority would be the model enhancement (trip-based enhancements or Portland pedestrian model) or the four-step pedestrian models (PedContext or MoPeD), which have a choice-based structure but may be easier to implement for some users. The facility demand (i.e., direct demand) tools can offer important convenience and utility to users, but their use should be con- fined to screening or preliminary analyses until such time as a more complete model may be brought to bear. An alternative to the direct demand methods may be to use either elasticity relationships from the choice-based models or strategically apply the special spreadsheet version of the tour-based model (presented in detail in Section 5.5). Despite the recommendation to use the comprehensive choice-based tools, several of these may not currently have the structure to perform a complete analysis, particularly if the ultimate goal is to estimate facility volumes for project planning or safety studies. In particular, neither the tour- based nor the GIS-accessibility methods currently allow the user to estimate link volumes of walk or bike trips. This is because neither is a stand-alone model in its current form; however, both support development of trip tables that can subsequently be assigned using standard distribution and assignment routines in a conventional transportation mod- eling package like CUBE or TransCAD. The PedContext and MoPeD tools already incorporate trip assignment in their design, although the MoPeD assignment process is somewhat simplistic and could be enhanced if desired. To help assimilate the model characteristics information presented in the preceding tables and profiles, Figure 5-1 highlights the differences and relative strengths of the meth- ods. The figure shows the seven tools aligned in relation to the steps that generally constitute a choice-based travel demand forecasting process. A comprehensive choice-based model would account for all dimensions of choice from trip generation to choice of mode, distribution/destination choice, assignment of trips to the travel network, leading finally to estimates of the number of trips at a given location at a given time. A white box in Figure 5-1 indicates that the model cur- rently performs this function directly; a shaded box indicates that the model could be used to support the step, but does not currently include the step in its own structure. The absence of a box indicates that the model was not designed or intended to address that aspect of the analytic process. Using this means of comparison, the guidebook tools may be differentiated as follows: • Tour-Based Generation and Mode-Split Model: This model performs trip (tour) generation and mode split in major detail, covering multiple purposes and four modes (walk, bike, transit, and auto). The model provides access to previously unquantified relationships among land use, network accessibility, and sociodemographics in explain- ing the decision to walk, bike, take transit, or travel by auto. The procedure could be replicated as a stand-alone model, but has greater immediate utility as a set of equa- tions that can be used to replace or revamp these functions in existing models. Hence, the procedure does not include the steps of distribution and assignment, which could be performed using those program utilities within the host model software. • GIS Walk Accessibility Model: Although this model is unusual because of its GIS orientation, its application steps are similar to a choice-based model. It performs overall person trip generation by purpose and then computes mode split. It performs distribution of walk trips (only) at a block level, but can transform the created walk trip

78 Tour Based GIS Walk Access Enhanced TR based PedContext & MoPeD Portland Walk Bike Route Choice Direct Demand Trip Gen (All) Trip Gen (All) Trip Gen (B/W only) Trip Gen (Walk only) Trip Gen (All) Mode Choice Mode Choice Mode Choice Mode Choice (walk/NW) Distrib (host model) Distrib Distrib (host model) Distrib Distrib (Dest. Choice) Assign. (host model) Assign. (host model) Assign. (host model) Assign. Assign. (host model) Assign. (Route choice) Facility Vols (host model) Facility Vols (host model) Facility Vols (host model) Facility Vols Facility Vols (host model) Facility Vols (host model) Facility Vols Figure 5-1. Modeling steps addressed by guidebook tools. tables to the TAZ level, at which point they can be used to adjust mode split for the other modes. The model does not perform assignment of walk trips, although it provides the trip tables and network information to support that procedure. The current packaging of the model is in an enhanced Excel workbook, though it is highly amenable to being integrated within a GIS scenario planning model. • Trip-Based Model Enhancements: This tool is not a stand- alone model, but a set of procedures and sample equations for improving the sensitivity of existing trip-based mod- els. The enhancements affect trip generation (which is per- formed only for walk and bicycle) and mode choice. Given that the approach was designed to function in a TAZ envi- ronment, the strategy for including non-motorized travel in mode split focuses on separating the non-motorized trips into intrazonal and interzonal categories, with intra- zonal trips offered the options of walk, auto driver or pas- senger, while for interzonal trips the options also include bicycle and transit. Interzonal motorized trips are then taken forward into distribution and assignment using the host model’s existing programs. • Portland Walk Model: Like the trip-based model enhance- ments approach, this tool can be used to improve the esti- mation of walk trips for a regional TAZ-based model or can be used as a stand-alone model. Its advantage is that its assessments are performed at a much finer geospatial scale (1.6 acre blocks versus TAZs). Trip generation consists only of productions, which are then mated with attractions to create trips using a destination choice model contained in the MPO’s host model. Productions are estimated for all trips and then split into walk and non-walk with a mode- choice model prior to destination choice. Users who do not employ destination choice models for trip distribu- tion may have difficulty applying this approach without customization. • PedContext and MoPeD Walk Trip Models: These are the most “complete” tools in the group, in the sense of taking the process from trip generation to assignment, and in the case of PedContext, allocating trips to totals (facility vol- umes) at crossings and key nodes. The limitation is that trip generation is done only for walk trips, although, as with the GIS walk accessibility and Portland walk mod- els, the resultant walk trip tables could be re-aggregated to TAZs and used to adjust TAZ mode splits for the other modes. PedContext is considerably more detailed than MoPeD, both in trip generation and distribution, offering an important tradeoff between complexity and accuracy. The GIS walk accessibility model might be used as an alter- native to provide the estimate of walk trips for distribution and assignment in these models.

79 • Bike Route Choice Models: These may be the most application-specific tools in that they are not designed to estimate demand, but mainly to inform route selection for cycle trips. As bicycle demand is sensitive to condi- tions associated with the travel network—directness, connectivity, safety, and hills—and these sensitivities vary by type of traveler and trip purpose, accurately rep- resenting these preferences is key to modeling bike travel. The coefficients of the two models in this group (SFCTA and Portland State) can be used to create more accurate measures of travel time or distance reflecting user values for facility attributes. • Direct Demand Models: Reflective of their name, these models deal directly with the task of estimating walk or bike activity levels on a given facility or at an intersection. The estimates are generated through a set of regression-derived relationships between observed counts and measures of context of the adjacent area served. This is not intended to be a top-down process as with the choice-based models. Accordingly, these models may lack sensitivity to some of the important interrelationships among land use, network accessibility, and sociodemographics that the choice-based methods attempt to capture. Their simplicity, however, makes them attractive for use in particular situations. Model Selection Criteria The following criteria can be used to decide what model or models to use: • Analysis objectives—What tasks are you trying to per- form and what answers will you require? The tables pro- vide information to help in this process, ranging from geographic scale and type of application (Table 5-2 and 5-4), to key metrics or indicators desired (Table 5-5), or particular variables for which sensitivity is desired (Table 5-6). • Resources—What data, computer tools, and expertise will you need to use the particular method, or conversely, what tools can you reasonably apply given your existing or achievable resources? Table 5-8 summarizes these needs. It may be possible to apply variations of most of the tools using simplified assumptions or borrowing from the tools in part (e.g., through elasticity relationships). • Accuracy tolerance/confidence level—How much is rid- ing on the answer? This is not necessarily an easy question, because “accuracy” may be viewed differently in different sit- uations. For example, if the answer supports a major invest- ment or program initiative that has expensive, long-term consequences (e.g., the remake of a downtown or a large new development project), the analysis should attempt to account for the complex interrelationships of land use, net- work accessibility, and sociodemographics. This would imply reliance on the more elegant choice-based models in the list. On the other hand, if the issue is estimating intersection vol- umes to assist in safety studies, less comprehensive methods may serve just as well—or better—in projecting incremental changes in demand from incremental changes in the context descriptor variables. Indeed, the more complex choice-based models excel in tying demand to known behavioral factors, but may be less precise in forecasting hourly link or intersec- tion volumes, while the less comprehensive facility models generally provide good correspondence with current counts but leave questions about predictive value in the case of more fundamental planning changes. Choosing an Approach—Analytic Objectives Table 5-8 offers a guide to selecting the appropriate tool to best serve the Analytic Objective criteria, which in this case Scale of Analysis Best Good Acceptable Regional Tour based GIS Accessibility Trip based Enhan. Portland Ped Model Tour based Elascies or Spreadsheet Corridor Tour based GIS Accessibility Portland Ped Model Trip based Enhan. Tour based Elascies or Spreadsheet Subarea GIS Accessibility PedContext Portland Ped Model MoPeD Direct Demand Tour based Elascies or Spreadsheet Project/Site GIS Accessibility PedContext MoPeD Tour based Elascies or Spreadsheet Direct Demand Table 5-8. Recommended approaches for different analytic objectives.

80 is represented by geographic scale. The scale has much to say about the appropriate level of detail and coverage that must be provided by the approach. So for example, at a regional level of analysis, issues are likely to concern projected levels, locations and type of growth, investments in transportation facilities, and impacts on overall mode split, VMT and congestion. For such analysis, the tour-based methods—alone or applied as modi- fications within existing trip-based models—offer the most sweeping combination of coverage and detail to process these relationships. The GIS-accessibility approach would provide excellent detail, but would probably have to be applied in mul- tiple locations, and the effects then translated to the regional model for overall effects. It could, however, play a vital role in regional visioning in support of regional plan development. The trip-based enhancements support region-level analysis, but are rated as “good” because the TAZ-level relationships would not be as incisive as the tour-based or GIS-accessibility models. The Portland pedestrian model was created to enhance the regional model, but is not as incisive as the tour-based or GIS-accessibility models. An “acceptable” approach for regional analysis would also be to use the elasticities from the tour-based models to enhance existing model relationships or the special spreadsheet version of the tour-based model. At the corridor level, the scale of analysis would suggest the same suite of tools in the best, good, and acceptable ratings. The exception would be a downgrading in the trip-based model enhancements methods, given that their TAZ resolu- tion would be less sensitive in addressing analysis issues at this finer geographic scale; the trip-based enhancements are not seen as being acceptable below the corridor scale. The GIS-accessibility approach was largely designed for the subarea scale of analysis and so is recommended as the best possible tool for applications in this category, which would include comprehensive plans, scenario planning, TOD and smart growth, and non-motorized network planning. The PedContext and Portland pedestrian models would also be very useful in this category, although perhaps less sensitive to the intricacies of accessibility than the GIS-accessibility tool. None of the three tools currently addresses bicycle travel; the spreadsheet version of the tour-based model could be useful in this regard. MoPeD is listed as an acceptable approach for the subarea model, mainly because its estimation of demand is more simplistic than the other tools, although it does offer creation of a trip table and assignment to a network, which is currently not possible without supplemental tools in the GIS-accessibility or Portland pedestrian models. Finally at the project/site level, the GIS-accessibility and PedContext models are seen as the best tools for estimating facility demand, given that they are choice based and take direct account of accessibility. The GIS-accessibility tool is constrained by its lack of an assignment routine, but this can be remedied through application of a conventional assign- ment method or emulation of the procedure in PedContext. MoPeD is regarded as a good technique for this application, although its assignment routine should be reviewed and enhanced if possible. The direct demand models—which are most commonly used for this application—are rated as only “acceptable” practice given their aggregate structure. Choosing an Approach—Accuracy Versus Complexity An important issue when choosing the right modeling approach is deciding between the accuracy level desired in the measures of performance and the amount of complexity involved in using the particular approach. Figure 5-2 provides an overview of the general level of accuracy achievable (expected reliability of the prediction) with each of the guidebook meth- ods, along with a sense of the level of effort associated with development and use of the tool. Comments associated with each rating may vary depending on the resources available and how the model is to be used. How to Use the Models The self-assessment of objectives, resources, and tolerances will enable the user to choose among four general approaches to using the models presented in the guidebook. Choices are as follows: • Adopt/Adapt—“Borrow” the models presented in the toolkit, but in conjunction with a process for calibration and validation to local conditions. Detailed instructions to guide adaptation are provided for the tour-based and GIS- accessibility models. • Emulate/Create—If the most appropriate model cannot be adapted to replicate local travel behavior surveys and match local empirical use data, create a similar local model by emulating the procedures described in the preceding chapter with local data. • Selective Enhancement—Several models in the com- pendium embody relationships not found in other con- ventional models and may be used to either attempt enhancements within existing model steps or to add or adjust particular variable relationships. This should be done with caution, however, with sensitivity testing to determine whether or not the effect on results falls within reasonable limits. • Pivot—For quick analysis of limited changes within lim- ited ranges and to produce general and relative findings within relatively relaxed accuracy tolerances, consider applying the elasticities generated by the various models or—in particular—using the special spreadsheet version of the tour-based model.

81 Portland Pedestrian Model Similar to trip based model enhancements, but slightly more accurate since work at finer spaal level. Representaon of context through PIE index is useful, but not robust. Should not be difficult to develop. PedContext Model Rigorous model which should be fairly accurate. Limitaon is in not considering overall trip generaon and mode split. Model esmaon may represent above average level of effort and data. MoPeD Model Good choice based model structure, accuracy limited only by specificaon of models and assignment roune. Should not be difficult to develop, enhance or apply with moderate GIS data and skills. Bicycle Route Choice Models (e.g., SFCTA or PSU) Difficult to type, since these are not complete models but deal only with route choice aspect and only for bike. Are somewhat difficult to develop (GPS survey/data), although template exists. Accurate for their intended use. Direct Demand Models (e.g., Santa Monica Bicycle/Pedestrian) Requires stascal skills to develop, count and context data to support model esmaon. Not parcularly difficult to apply. Accuracy limited because of aggregate structure. Accuracy Re so ur ce s H M L H M L Accuracy Re so ur ce s H M L H M L Accuracy Re so ur ce s H M L H M L Accuracy Re so ur ce s H M L H M L Accuracy Re so ur ce s H M L H M L Tour Generaon & Mode Split Highest level of detail and accuracy of any method, very high data and experience required if develop from scratch. Much less demanding if use to modify exisng model or use elascies/spreadsheet. GIS Accessibility Not as demanding of data and modeling experse as tour based approach, but does require abilies with GIS data and tools. Accessibility approach and fine resoluon of GIS provides high sensivity. Trip Based Model Enhancements Not as accurate as the previous two methods because of TAZ aggregaon, but may be very convenient/serviceable way of using exisng models. Data needs and stascal skills to develop may be non trivial. Accuracy Re so ur ce s H M L H M L Accuracy Re so ur ce s H M L H M L Accuracy Re so ur ce s H M L H M L Figure 5-2. Accuracy versus resource requirements for guidebook tools.

82 Adopting one of the models presented here without local adaptation should only be done if the study community is reasonably similar to those in the examples with respect to the following: • Similarity of land use and infrastructure landscape based on regional and community descriptors such as topogra- phy, weather, sprawl characteristics, highway and transit infrastructure (e.g., lane miles per capita, or fixed-route miles and total transit revenue miles and per capita), and completeness of local street and path networks. • Similarity of the community with respect to socio- economics and demographics, presence in the community of unique travel generators such as colleges, major recre- ational or entertainment/social venues, and car culture (possibly exhibited in the region’s Census journey-to-work model shares). All models should be used with proper caution. They are simply equations correlating particular variables that seem to explain an important behavior or result, and the underly- ing assumption is that there is a causal relationship between the explanatory (independent) variables and the variable of interest. There is generally no way to confirm this causality, so look to these equations to statistically “infer” that a particular result will occur if the included variables are changed. Con- fidence in this approach is measured in three ways: (1) by a plausible structure in terms of the relationships reflected in the model, (2) through statistics reflecting the goodness of fit of the model and the individual variables, and (3) ultimately testing its predictive ability against observed behavior. The models in this guidebook are of two different types. The more comprehensive models attempt to predict behavior from an integrated structure that accounts for the individual, the setting, and the alternatives. Their primary output is an estimate of mode choice and trips by mode. Their validity is primarily shown in their ability to predict these choices. The other type of model attempts to directly predict activity levels, generally with a fairly aggregate level of context factors which show high correlation, but which may or may not be explana- tory. Validation of these models is generally seen in their abil- ity to reproduce the volumes measured in actual counts. Neither of these tests is entirely satisfactory, given that the choice-based models often do not attempt to predict point- level usage values, while the simpler context models may replicate counts adequately but not be able to show reason- able sensitivity to important decision-oriented variables. Two general rules should be applied when adopting and adapting the two types of models: • Facility-demand models should always be derived specifi- cally for local conditions. Such direct demand models are heavily customized to a specific array of local conditions, including unique trip generators, sociodemographics, and modal culture. • Choice models (including disaggregate tour-based, GIS- based accessibility, enhanced 4-step, and trip-based disag- gregate) should always be tested against local facility-use data if their use is extended to facility-demand estimation. A possible strategy when confronting this dilemma is to consider the choice-based and use-based models as valuable complements to one another. Direct demand (use-based) models can help address the problem of underrepresentation of walk and bike trips in travel surveys, as well as the fact that most pedestrian and cyclist destination and route choice models are relatively unproven. Direct demand models can also be strategically useful for helping validate choice models such as the tour-based, trip-based, and geospatial models, which fall into the comprehensive choice-based category. This symbiosis is likely to become more important as walk- ing and cycling models begin to be held to the same perfor- mance standards as other transportation models for other modes. Such additional requirements can be expected to accompany uses of models for facility-specific improvement proposals and impact assessments and to justify potentially controversial policy decisions and expenditures. Figure 5-3 illustrates how these two classes of models might be integrated and thereby strengthened; Figure 5-4 profiles how they might be used for cross calibration and validation. 5.5 Guidelines for Use Tour-Generation and Mode-Choice Approach This approach was designed to • Use the most advanced current methods in travel demand modeling (activity-based (AB) and tour-based (TB) struc- tures) to try to capture the scale and nuance of non- motorized travel. • Work with parcel/point-level or block-level data instead of zonal aggregations. • Account for the practice of grouping trips into home- based and work-based tours—simple and complex— which are strongly influenced by land use and transpor- tation accessibility and is an important determinant in choice of mode. • Help the following types of users in the following situations: – Those who are developing or thinking of developing an AB or TB modeling platform to replace an existing trip- based model, – Those who have an existing AB or TB model and wish to enhance its capability to address non-motorized travel,

SED Variables Parcel, Buffer Data Network Skim Data Pedestrian Counts o Income o Employment o Distance, slope o Intersecons o Family size o Desinaons o Impedances: o Segments o Age o Intersecons Width, Condition o Partition: o Work status o Bus stops Safety percepon esmaon sample o Student status o Sidewalks o Node Centrality, Reach validation sample Yes No Perform correlation analysis among SED, parcel buffer, skim data and counts, and factor analysis to combine independent variables into higher level indicators. Regress on ped counts in esmaon sample to develop predicve models with statiscally high explanatory power. Direct Demand Model (Likelihood of Facility Use) Validate with respect to ability to esmate empirical demand using validaon sample Valid Choice/ UtilizationModel Ped Use by Network Facility Compare with Network Ped Counts (Calc overall % deviation, and Root Mean Square Acceptable deviations? Choice Model (Generation and Distribuon of Travel) Choose among Seattle, Arlington, Balmore, Portland methods for esmang trip/tour generaon, mode choice, destination choice (see Table 5 1)* *Some methods require person trip input from other sources. Esmate ped travel by Origin or by O/D pair Figure 5-3. Integration of direct demand and choice models. Neither Widespread Local Only Choice Model Compare with Network Ped Counts (Determine overall % deviation, and RMSE) Regress on modal trip gen and destination choice residuals to compute calibraon adjustments to variable coefficients for SED, buffer data, network skim disulies. Consider range of available elascies for each variable to constrain adjustments to within verifiable ranges. Choose either: a) disaggregate tour based [Seaˆle]; or b) GIS based accessibility [Arlington]; or c) enhanced 4 step [Seaˆle]; or d) disaggregate trip based [Balmore or Portland] methods*. *Somemethods require exogenous input and processing from other sources for certain steps (see table 5 1). These may include regional models for person trip generaon or separate route choice models such as San Francisco or Portland for network assignment. Esmate ped travel by O/D pair, predicted through a sequence of trip/ tour/ destination/ mode choice steps. Widespread or localized deviations? Valid Choice Model (consisting of trip/ tour, destination, mode and route choice steps) Ped travel by O/D Pair and Network Facility Perform network assignment produced by stochastic or locally derived route choice models such as those developed in SF, Portland. Regress on assignment model residuals to compute calibration adjustments to variable coefficients for distance, directness, slope, safety/security impedances. Consider range of published elascies for each variable to constrain adjustments to within verifiable ranges. Figure 5-4. Calibration and validation of walk choice models.

84 – Trip-based model users who wish to enhance their models for bike-pedestrian analysis, and – Persons seeking better understanding or key relation- ships between land use, network accessibility, and bicycle or pedestrian demand for policy or educational purposes. Scale of analysis • This approach would be most readily applied at a regional level of analysis. Such use would be easiest for those with existing AB/TB models in place or under development, although the methods can also be extended to trip-based models if appropriate steps are taken. • Another common application would be within a corridor or subarea, in which case the study area would be treated as a “window” in the modeled region, with provision to maintain SED and trip flow consistency between study area and the remaining region. • Finer level site or project-level analyses may be possible using the starting conditions provided by the land use/scenario base developed above. • Sketch-planning analyses can be performed through use of elasticities or the special spreadsheet model. Data, tools, and expertise needed • If developing or enhancing an AB/TB model and wishing to emulate the approach used with the Seattle/Puget Sound data for estimating bicycle and pedestrian demand: – Travel survey data with full household and individual sociodemographic characteristics plus information on the purpose and mode for each trip and latitude/ longitude location for each trip end. The survey should include walking and bicycle trips. (If estimating new models, larger samples will generally be required than are needed to transfer and recalibrate models first esti- mated elsewhere.) – A synthetic population of households/individuals, con- trolled to match Census/ACS population distributions at an appropriate spatial scale (e.g., Census block groups). – Parcel-level or block-level land use information in geo- spatial format. – An all-streets network in GIS format, enhanced to include all bicycle and pedestrian facilities, and with link- level information on characteristics (e.g., facility type and grade) used to create weighted impedances for each link. – Buffered measures of accessibility, land use character- istics, street grid, and transit access for each parcel (for walking and biking using respective networks). Such measures are typically calculated in a GIS or similar spatial programming tool. – Regional accessibility as measured through composite (logsum) measures across possible modes and destina- tions (mainly influenced by the times and costs for the auto and transit modes). – High-level expertise with AB/TB models and GIS. • If attempting to apply the tour-based approach within existing an AB/TB or trip-based model (using new mode choice models to augment trip tables): – Parcel-level or block-level land use data (as above). – A synthetic population of households/individuals (as above). – Bike (and pedestrian) networks and skims (as above). – Buffered land use and accessibility measures (as above). – Composite accessibility measures (logsums, as above). – Working familiarity with AB/TB modeling concepts (or senior-level expertise with trip-based models, if that is the platform) GIS modeling tools, data, and skills in their use. – Ideally, some survey data and/or count data on walk and bike trips to validate model outcomes. • If attempting to post-process results from a trip-based model or conduct sketch-planning analysis: – Sufficient information to apply either elasticities or the provided spreadsheet model, – Bicycle (and pedestrian) networks (with weighted imped- ances if possible), and – The ability to buffer land use and built-environment char- acteristics with GIS and the corresponding networks. Suggestions for Adaptation and Use AB/TB model development or enhancement Because this topic is technically complex and beyond the general scope of this guidebook, detailed instructions are not included here. Users should refer to Appendix 1 of the Con- tractor’s Final Report for detailed technical documentation on the models and their development process. In general, incorporation of detailed behavioral responses of cyclists and pedestrians to infrastructure and land use should contain at least the following elements: • Use of detailed land use data at the parcel/point-level or block-level. Buffering of land use and street network/inter- section data around each parcel/point or block, ideally using on-street distances for buffering rather than crow- fly distances. • Use of a network for the bicycle mode that incorporates designated bicycle facilities, as well as other key factors such as elevation gain. • Ideally, use of a bicycle route choice model such as those developed for San Francisco or Portland, or, at least, use of a generalized distance function in bike network path- building to select appropriate paths to use in mode choice and other model components.

85 • Ideally, use of a separate pedestrian network that includes all local street segments and intersections, as well as coding of sidewalks and elevation gain. • Use of detail on where transit stops are located, in buffer- based measures and, ideally, in defining transit walk access and egress times for each O-D. Application of TB models to enhance an existing AB/TB or trip-based model The expectation of users in this category is that they want to take advantage of the new bicycle and pedestrian mod- els developed in the NCHRP research, but do not wish to embark on a comprehensive model development or enhance- ment process. Rather, their goal is to access the set of relation- ships captured in those models and supplement those in their existing model. This would involve adaptations of the mode choice and possibly the tour-generation models within the current tour/trip generation, distribution/destination choice and mode-choice model steps. Application would be more direct in an AB/TB model plat- form, but with some creativity can be used in a trip-based model. This type of enhancement could be done at a few lev- els as follows: Update of origin-destination (O-D) mode-choice models: One of the primary effects of improvements in infrastructure or land use is to attract shorter trips from other modes to walk and/or bike. This can be incorporated into an existing mode- choice model run for trips or tours with known origin and destination, by incorporating some or all of the variables used in the O-D level mode-choice models presented in this report. In general, this would involve the following steps: 1. Select a “basis variable” in both the existing mode-choice model and the one from this report for the corresponding tour/trip purpose. Walk distance or auto travel time are good candidate variables. 2. Add any new variables that can be supported by the avail- able input data into the model utility functions, giving them the same relative coefficient values as in the “trans- ferred” model from this report. So, the coefficient to use in the model will be the basis variable coefficient in the existing model, multiplied by the ratio of the new variable coefficient divided by the basis variable coefficient in the transferred model. 3. After all new variables have been added, apply the model to the base year data and (re)calibrate the mode-specific constants so that the mode shares still match any calibra- tion target mode shares (e.g., the shares used to calibrate the original existing mode-choice model). In general, this type of model update is preferable to apply- ing the elasticities provided later in this section in a post hoc manner. In contrast to estimated model coefficients, elas- ticities are essentially a model output, rather than an input, and thus are more sensitive to the network supply and com- petitive balance between the modes specific to the region in which they are derived. Although the models in this report were estimated at the tour level, they can be used to update either tour-level or trip- level mode-choice models. Although in a behavioral sense it has been seen as superior to model mode choice at the tour level, there is no reason to expect (and no experience in prac- tice) that the relative values of the coefficients are markedly different in models estimated at the tour level versus the trip level. These models include variables that may not be available in the local data to apply the model. In addition to specific infrastructure and land use variables, this may also include socioeconomic variables such as age and gender not available in household-level aggregate models. Transferring some variables is likely to be worthwhile, even if some of the variables in these models are not applicable, given that the alternative is to ignore all of the variables. A caveat to this recommendation is that it may not be worthwhile to attempt to update an existing model that uses fairly large zones (e.g., much larger than Census blocks) and/ or sparse networks to represent the walk and bike modes. In that case, the data that the models are applied to would be different in scale and accuracy to the data used in estimation and would not be accurate enough to give meaningful or reli- able results. The above discussion assumes that the original mode- choice model already included the walk and bike modes, at least in some rudimentary way. If the original mode-choice model only included motorized modes, it is still possible to use the update procedure outlined above. In that case, how- ever, it will also be necessary to make some adjustment to the trip/tour-generation model process so that it does not exclude non-motorized trips or tours at that earlier stage. (This point is discussed further below.) Update of origin-only (“pre-”) mode-choice models: Some trip-based and TB model systems generate trips across all modes, but then use a two-stage process to represent mode choice. Before distributing trips across destinations, a “pre- mode-choice” model is sometimes used to split the generated trips between the motorized and non-motorized models, and then only the motorized trips are used in the subsequent distribution/destination choice and origin-destination mode- choice models. If one wishes to retain this approach, it is possible to use the “origin-only” versions of the mode-choice models presented in this report and use that model for the corresponding trip/tour purpose, knowing only the socio- demographic characteristics of the traveler (segment) and the characteristics surrounding the residence location. The general transfer/enhancement procedure is the same as that outlined

86 above for the O-D mode-choice models, except in this case there is no ubiquitous variable such as auto travel time to use as the basis variable. If there is no candidate basis variable, the best option may be to simply use this complete model (or at least all of those variables applicable) in place of the existing pre-mode-choice model and calibrate it to the same observed shares to which the existing model was calibrated. If one maintains (or adopts) the approach of using an origin-only mode-choice model before trip distribution/ destination choice, there is still the option of subsequently distributing and assigning the bicycle and/or pedestrian trips to the appropriate network. The attraction variables for dis- tribution/destination choice would be the same as when dis- tributing trips for other modes, but the impedance variable would be mode-specific. For bicycle, the generalized distance along the best path to each possible destination would be an appropriate impedance variable, insofar as it is consistent with the path-specific impedance measures used for path- building in bike trip assignment. Distribution and assignment models were not estimated with the Seattle tour-generation/mode-choice models, but such models can be estimated and applied in a conventional modeling package such as CUBE or TransCAD. Both friction factors and attraction variables can be adapted to include walk- and bike-specific attributes. Update of trip or tour-generation models: Compared to mode-choice models, a wide variety of methods are used to generate tours and trips in existing models, ranging from sim- ple cross-classification tables in 4-step trip-based model sys- tems to complex full-day activity pattern models in advanced AB model systems. It is not possible to outline one way of using the NCHRP tour-generation/complexity models that will be applicable in most cases. Trip generation models in most trip-based models are not sensitive to accessibility effects (i.e., there is no feedback from the mode-choice and distribution models). In many cases, it may be adequate to update the mode choice (and perhaps the distribution models) to better represent walk and bike demand factors and leave the trip generation models unchanged. Another option, applicable in a trip-based or TB context, may be to use the tour-generation elasticities from this study in a post-processing step to adjust the tours or trips resulting from the generation model. In some regards, this is similar to apply- ing the well-known “5-D’s” post-processing approach, except that in this case the elasticities are applied prior to distribution and mode choice and isolate only the tour and trip generation effects. Because the generation effects are typically second- order changes much smaller than the shifts in mode shares or trip distances, incorporating this type of model update is of less importance than updating the other models described above. For more substantial model updates, it would be possible to attempt to transfer the tour-generation/complexity model from this report. However, this particular model form may be incompatible with the structure of the existing model sys- tem. It may be more efficient and useful to use the type of residence-level land use and accessibility measures used in the various Seattle/Puget Sound models in this report as new, additional variables in re-estimating or re-calibrating one’s existing tour-generation models. Post-Processing, Sketch Planning, and Sensitivity Testing There will be many occasions when users have neither time nor resources to develop a complete modeling structure for analyzing bicycle or pedestrian travel issues or where the level of importance associated with the answer does not justify extensive model development. In this case, sketch-planning or elasticity methods may be used to factor the basic results gen- erated by a trip-based model or to support a sketch-planning analysis of the relative importance of particular attributes or suitability in a given environment. Two approaches exist for this category of user: elasticity methods and an interactive spreadsheet approach developed expressly for this guidebook. Elasticities An important product of this research is the calculation of elasticity relationships from the various models. Elasticities are a unit-less quantity that represents the percentage change in the dependent variable in a statistical equation that occurs in response to a percentage change in one of the indepen- dent (explanatory) variables, while everything else is held constant. Unlike the estimated coefficients in the model, the elasticity is a pure measure of the impact of the predictive variable that can be compared with the other variables, with- out controlling for the magnitude of the measure itself. Elas- ticities may be positive or negative and exhibit a wide range in values, although the most important range lies between 0 and 1. Variables whose elasticity is greater than or equal to 1 (or –1) are said to be “elastic,” in that they produce a change in the dependent variable greater than or equal to the change in the variable itself. Conversely, variables whose elasticity is less than 1 or greater than –1 are said to be inelastic, because they produce a change in the dependent variable less than proportionate to the change in the explanatory variable. Elasticities can be used to help educate users on the relative importance of particular variables, either in model design or project design. Elasticities can also be used to tweak results from conventional models that do not account for such fac- tors or to create sketch-planning models for simpler plan- ning tasks. The Seattle-derived TB model provides elasticities relating mode choice for walk, bike, and even transit to • Walk and bike accessibility, • Regional accessibility,

87 Model Home based Work Home based School Home based Recreaon Home based Shop/PB Work based Walk mode choice Network distance 1.07 1.10 .97 .97 .48 Bike mode choice Network distance .60 .65 .41 .75 .47 Bike path distance .08 .02 .03 .03 .02 Bike lane distance .07 .04 .04 .04 .03 Wrong way distance .007 .002 .003 .005 .008 Turns per mile .10 .10 .06 .12 .10 Average rise .29 .22 .19 .27 .14 Table 5-9. Tour mode-choice model elasticities. • Land use characteristics at origin or destination, • Walk and bike transportation network characteristics, and • The effect of the above characteristics on tour complex- ity (simple or complex), which strongly impacts choice of mode. The following tables present some of the more important elasticity relationships derived from the Seattle TB research. Table 5-9 presents elasticities demonstrating the importance of network travel distance and path characteristics to walk or bike mode choice for five trip purposes. Key findings are that • Walk mode share is elastic or nearly elastic with respect to distance for all purposes except work-based travel. • Although still sensitive to distance, bike is less elastic than walk, with elasticities ranging from a low of –0.41 for home-based recreation to –0.75 for home-based shopping and personal business. • In addition to bike network distance, other path charac- teristics influence bike choice, such as the distance for the part of the trip made on a bike path or lane, the portion of the route this is the wrong way, number of turns per mile, or hilliness as measured by the average elevation rise for the trip. Average rise carries much more weight in the bike decision than the other characteristics, running second only to overall distance. Table 5-10 shows how these elasticities increase with length of trip. The longer the trip, the greater the negative effect on choice of walk or bike. The values shown in the table are for home-based work tours only, but the increasing effect of distance on non-motorized mode choice is reflected in all purposes. Table 5-11 presents elasticities relating mode choice to land use variables. In general, these elasticities show that • Walk mode choice increases with higher employment den- sity (work only) and higher intersection density (personal business and work-based), but declines with increases in grade (percent rise) and absence of sidewalks. Walk choice also declines if the tour is complex rather than simple. The highest single sensitivity, –0.77, is in response to grade for work trips. • Bike mode choice increases with land use entropy and intersection density (all work only), and the intersection density value is almost elastic (0.90). Existence of a Class I bike path is important for both work and school travel, while grade is an extreme negative factor for work trips. One way distance band All tours 0 1 miles 1 3 miles 3 6 miles >6 miles Walk mode choice Network distance 1.07 .42 2.37 n/a n/a Bike mode choice Route distance .60 .12 .33 .59 1.14 Bike path distance .08 .001 .03 .07 .17 Bike lane distance .07 .003 .02 .07 .15 Wrong way distance .007 .001 .005 .008 .012 Turns per mile .10 .03 .07 .10 .15 Average rise .29 .03 .15 .28 .59 Table 5-10. Elasticities for work tours by distance band.

88 Model Home based Work Home based School Home based Recreaon Home-based Shop/Personal Business Work based Walk mode (using walk buffer = 1 mi) Desnaon total employment .21 Origin + Desnaon avg. intersecon density .23 .17 Origin + Desnaon avg. fracon rise .77 .03 .11 Origin only avg. fracon rise .16 Origin only percent no sidewalk .18 .19 .22 Complex mul stop tour .20 .12 .03 .05 .02 Bike mode (using bike buffer = 2 mi) Desnaon mixed use entropy .02 Origin + Desnaon fracon Class 1 bike path .37 .31 Origin intersecon density .90 Origin avg. fracon rise .82 Complex mul stop tour .32 .17 .08 .16 .06 Transit mode (using walk buffer = 1 mi) Origin transit stop density .85 .10 .72 0.32 0 Desnaon transit stop density .37 .10 .72 1.21 2.09 Desnaon total employment .32 Origin intersecon density .11 Origin pct. no sidewalks .14 .70 Desnaon pct. no sidewalks .21 Complex mul stop tour .20 .13 .25 .09 .07 Table 5-11. Mode-choice elasticities in relation to land use characteristics. Bike choice declines ever more significantly than walking when the tour is complex (for all purposes). • Transit mode choice is affected by transit stop density (in relation to the walk network) at both origin and destina- tion for all trip purposes. Intersection density and employ- ment density are important positive factors for work trips, and absence of sidewalks has a negative effect for school and social/recreational travel. As with both walk and bike, transit choice is also reduced with the decision to make a complex tour. The elasticities in these tables may be used to pivot from known levels of walking or bicycling to estimate incremen- tal changes resulting from a single variable of influence. For example, using the elasticities in Table 5-10, improving route directness of streets or bike paths that reduces trip distance between homes and workplaces by 10% would be expected to induce a 3.1% increase in the likelihood of making the home-to-work trip by bicycle. A similar change that reduces trip distance between homes and schools by 10% would be expected to lead to a 4.4% increase in the likelihood of making the home-to-school trip by bicycle. Such pivot analysis should be performed with care, taking account of only one variable change at a time, and account- ing for each of the affected trip purposes individually. The degree of change examined should also be relatively small. Users should avoid situations where the change, for example, in distance is more than a 50% increase or decrease. This is because the elasticities above are only stable for incremental changes near the regional mean value of the variable being tested. Tour-Generation/Mode-Choice Spreadsheet In addition to these simple elasticities, the tour-gen- eration and mode-choice models have been adapted into a spreadsheet created expressly for the guidebook and included on CRP-CD-148. Like the elasticities, the spread- sheet has various purposes, from allowing users to interact more dynamically with the relationships to using the rela- tionships to create model enhancements of sketch-planning tools. The value the spreadsheet has over the elasticities is that it allows for testing changes in multiple variables at one time, thereby exposing synergies or conflicts that may

89 exist in those models among key variables. For example, one can test • Whether network improvements work better or about the same when implemented in conjunction with changes in land use. • Which travel market segments are most influenced by changes in either land use or network characteristics. The model is presented as a series of Excel spreadsheets, which includes working versions of both the tour-generation/ complexity models and the tour mode-choice models. The file contains the following screens as individual worksheet tabs • Tour Generation/Complexity by Purpose: Shows the basic structure and estimated coefficients for the tour-generation models. • Tour-Generation Calculations: Takes the Tour-Generation/ Complexity model above and offers it in an interactive, computational format. • Tour Generation for Work-Based Other Travel. • Mode-Choice Model: Shows the basic structure and esti- mated coefficients for the tour mode-choice models, which incorporate four modes and five trip purposes. • Mode-Choice Calculations: Like the tour-generation model in the second tab, this worksheet contains an interactive, computational version of the mode-choice models. • Tabulation Sheet: A convenience sheet for storing results of the mode-choice models for later comparison. • Distance = 0.5 (etc.): To properly assess mode choice across several very different modes, it is necessary to compare the modes on common ground with regard to trip length. Thus, this worksheet has set up the computational version of the mode-choice models to examine mode choice when the average one-way trip distance is 0.5 miles. Subsequent spreadsheets have been similarly set up for one-way trip lengths of 1, 2, 3, 4, and 5 miles. Tab: Tour Gen Models: There are several ways to work with the spreadsheet. Entering the first tab shows the structure and coefficients estimated for the Tour-Generation/Complexity model. The model has the following overall structure: Number Tours Made Number Tours by Purpose Complex or Simple The model first calculates the probability that a tour will be made and then whether a second, third, or fourth tour will be made. A determination is then made as to whether the tour will be simple or complex (multi-stop), which is also a prob- ability calculation. The tour(s) are then allocated to trip pur- pose, of which the choices are work, school, escort, personal business, shop, and social/recreational. The result of this step is a determination of the total number of simple and complex tours for a given person across the stated trip purposes. Variable definitions are provided to the right of the page in the spreadsheet. Tab: Tour Gen Calcs The second tab in the spreadsheet enables the user to actu- ally use the model. The structure is as shown in the following diagram: PRODUCT= XCOEFFICIENTS MEANS In the first series of boxes at the left side of the worksheet, shaded in blue, are the models just viewed in Tab 1, with the estimated coefficients. The second series of boxes, shaded in peach, are identical in form and contain the “input data” used to run the models. In this case, the means for the sample used to develop the models have been entered as the basis for the test, but this is also where the user would enter his/her assumptions when working with the model. Finally the set of boxes highlighted in green are the product of the coefficients times the means, and thus fuel the calculations. Under the primary green boxes containing the tour genera- tion, complexity, and purpose computation, the user will find another set of other tables also highlighted in green. These tables are not intended for user access/use—they perform key computations in the overall model. They have been included

90 and annotated to help the user understand and follow what is happening at each step. Variable definitions are included at the right of the master model spreadsheet in Tab 1. At the very top of the worksheet is the following sum- mary box: Test Base Net Change Pct Change 1.1950 1.1950 0.0000 0.00% 0.9234 0.9234 0.0000 0.00% 2.1184 2.1184 0.0000 0.00% 56.4% 56.4% 0.0% 0.0% Primary Effects Summary Total Tours Total Simple Tours Total Complex Tours Percent Simple Tours all number of tours declines from 2.118 to 2.049 as entropy increases from 0.422 to a maximum of 1.0, while the percent- age of tours which are simple increases from 56.4 to 59.2%. The user can attempt any variety of assessments in a similar manner, with the advantage of having the full model active in a spreadsheet being that multiple variables can be tested simultaneously—unlike simple elasticities—thereby realistically accounting for interactions and synergies. To assist the user in testing assumptions, a backup of the origi- nal values loaded into the table of means is presented at the bottom of the spreadsheet under the working tables. If the user wishes to restore the input tables to the original values, simply copy the original values in the backup tables to the working tables. Another illustration of the tour-generation models is to examine the tour-generation rates and distribution by pur- pose for major sociodemographic travel groups. Table 5-13 conveys total tours generated and breakdown by purpose forecast for ten traveler profiles, exercising combinations of age category, work or student status, and presence of chil- dren in the household. Any such combination can be tested by the user and then subjected to different assumptions about the conditions under which the travel decision is being made (entropy, buffer activity, and logsums). Tab: WB Tour Gen This tab presents a separate tour-generation model that deals specifically with work-based (WB) tour generation. This calculation is only engaged if the individual traveler made a trip to work in the initial tour-generation analysis. The approach and procedures are otherwise the same, although the variables and coefficients in the models are different. Tab: Origin-Only MC Model The next tab contains the set of models that predict mode choice for the estimated tours by purpose and complexity. This particular version incorporates only the land use and travel network characteristics at the trip origin, as opposed to the entire trip (tour), or origin-destination, which is pre- sented later. Although this version of the model is less infor- The summary box conveys the number of tours calculated and the proportion that are simple versus complex. A major outcome from this part of the model is in the proportion of tours estimated to be simple one-stop tours (56.4% in this case), because these are the tour types most likely to be made by walking, biking, or transit. The more characteristics an area has that make it “urban,” the more likely that projected tours will be simple. The key variables in the model that reflect the effect of urban design on tour type are the land use entropy (in both tour number and complexity equations), the purpose-specific buf- fer measures in the purpose models, and the logsum measures in each of the models. In general, higher values of the entropy and purpose-specific buffer measures indicate areas with more “urban” characteristics, while the logsums are more likely to reflect opportunities outside the local area and present a draw for longer distance trips, of which a higher proportion will be in complex tours and hence more likely to be made by auto. As an illustration of how this worksheet can be used, Table 5-12 is the result of testing different values of Origin Entropy in the model (appears both in Tour Generation and Complexity) to examine sensitivity of both number of tours and the percentage of tours which are simple to level of entropy at the tour origin. The model projects that the over- Origin Entropy Simple Tours Complex Tours Total Tours Percent Simple 0.422 1.195 0.923 2.118 56.4% 0.5 1.197 0.911 2.108 56.8% 0.6 1.201 0.895 2.096 57.3% 0.7 1.204 0.880 2.084 57.8% 0.8 1.208 0.865 2.073 58.3% 0.9 1.211 0.850 2.061 58.8% 1.0 1.214 0.835 2.049 59.2% Table 5-12. Likelihood of simple or complex TB on origin entropy.

91 mative than the origin-destination version, it has value in the set of tools because of the following: • There are application situations where the only informa- tion available is in relation to the trip origin (travel surveys provide detailed information on the traveler’s residence location, but much less on other trip ends). Many of the Density, Diversity, Design, Destinations (4Ds) models that incorporate land use characteristics are limited to resi- dence trip production end only in their specifications. • Although the destination of a tour for purposes like work or school may be known and made part of the choice computation, for most other trips, the destination is not known and is one of the choices being made along with choice of mode. For these trip purposes, the origin-only model can estimate NMT productions, which then can be distributed to candidate destinations based on relative opportunities. Separate mode-choice models are presented for five tour purposes: home-based work, home-based school, home-based (social)/recreation, home-based personal business, and work- based other. If one wishes to connect the tours from the tour-generation models to the purposes specified in the mode- choice models, the conversion is as follows: • Home-based work = home-based work • Home-based school = home-based school • Home-based recreation = home-based recreation • Home-based other = home-based personal business, shop- ping, meal, and escort • Work-based other = work-based other The key “policy” variables that influence choice of mode follow. For Walking: • Buffered attractions for the respective purpose (within “walk” Buffer 1) • Household density in Buffer 1 • Intersection density in Buffer 1 • Percent rise in gradient in Buffer 1 • Percent of facilities with no sidewalks in Buffer 1 • Mode/destination logsum for zero-car households For Bicycle: • Buffered attractions for the respective purpose (within “bike” Buffer 2) • Intersection density in Buffer 2 • Percent rise in gradient in Buffer 2 • Fraction of facilities that are Class 1 bike path in Buffer 2 • Mode/destination logsum for zero-car households For Transit: • Intersection density in Buffer 1 • Percent rise in gradient in Buffer 2 • Percent of facilities with no sidewalks in Buffer 1 • Number of transit stops in Buffer 1 • Mixed land use index in Buffer 1 • Mode/destination logsum for zero-car households The user can modify any or all of these factors (using the orange table) and estimate the effect on mode-split for any trip purpose. Tab: Variable Defs MC Model This tab provides a definition of all variables used in either the origin-only or origin-destination mode-choice models. Purpose Adult, FTW, Kids Adult, FTW, No Kids Adult, PTW, Kids Adult, PTW, No Kids Adult, NW, Kids Adult, NW, No Kids HS/Univ Student, NW HS/Univ Student, PTW Child, 5 15 Rered, > 50 Work 82.0% 89.4% 52.0% 71.0% 5.9% 12.3% 14.9% 50.4% 0.1% 9.0% School 0.6% 0.6% 2.3% 2.8% 1.3% 2.4% 70.7% 47.0% 78.6% 1.2% Escort 9.3% 1.7% 30.2% 6.9% 59.9% 21.2% 2.8% 0.5% 13.8% 16.4% Pers. Busn. 2.8% 2.7% 5.6% 6.7% 12.1% 22.2% 4.1% 0.7% 2.7% 28.0% Shop 2.8% 2.8% 5.9% 7.2% 12.6% 23.8% 3.5% 0.6% 1.8% 23.7% Meal 1.3% 1.7% 1.5% 2.4% 3.7% 8.9% 1.5% 0.2% 1.2% 11.4% Ent/Rec 1.1% 1.2% 2.2% 3.0% 4.5% 9.2% 2.5% 0.5% 1.9% 10.3% Total Tours 2.988 2.763 3.272 2.935 2.715 1.988 2.933 3.811 2.643 1.724 Table 5-13. Total daily tours and distribution by purpose for different demographic segments.

92 Tab: Origin-Only MC Model Calcs As with the tour-generation models, this tab takes the origin-only mode-choice model and arranges it in an interac- tive version to illustrate calculation and enable user testing. The interactive version of the mode-choice model is presented in a format similar to the tour-generation calculation. Coefficients in the blue table are applied to the model inputs in the orange table, with the product of the two appearing in the green table. At the bottom of the products (green) table is a summary of the calculated results, indicating the expected mode share for each of the five purposes, distinguished by whether the tour is simple or complex. Three sets of results are shown in the lines 100 to 102 in the worksheet. One shows the expected mode shares by pur- pose if the tour is a simple one-stop tour. The simple-tour scenario is communicated by inserting a value of zero for the tour complexity variable in line 61. As expected, the shares of walking, biking, and transit are higher for the simple-tour case than when the tour is complex (specified when the tour complexity value is set to 1). The third set of shares corre- spond to the percentage of complex tours found in the model calibration sample, which are shown as the default values provided in the table on line 61. To work with the mode-choice models, enter assumptions in the orange table only. To replace the default values in the orange table at any time, a backup copy is provided at the bottom of the worksheet. Tab: O-D Mode-Choice Model This tab introduces the version of the mode-choice models that operate on full origin-destination information. The mod- els are similar in structure to the origin-only models, with four modes and five trip purposes included. The basic structure of the individual models also includes sociodemographic char- acteristics of the traveler, characteristics of the built environ- ment (household and employment density, purpose-specific activity in the buffer, intersection density, transit proximity, and land use mix), as well regional accessibility represented through logsums. There is also a provision to differentiate between simple and complex tours. Variables unique to the origin-destination mode-choice models include measures of conditions at the trip desti- nation, measures of trip length (distance and/or distance- related travel time), and characteristics of the journey for bicycle that include relationships for type of bike facility, slope/gradient, turns per mile, and fraction of journey wrong way on directional streets. The inclusion of side- walk coverage (percent buffer with no sidewalks) also shows important relationship with both walk and transit trip-making. Also, the inclusion of travel time for auto and transit modes, and cost in the form of parking cost and fare, provides an important set of policy variables for analysis with this set of models Variable definitions are presented in the earlier tab: Vari- able Defs MC Model. Tab: O-D Mode-Choice Model Calcs This tab presents the interactive version of the O-D mode- choice model, following the same structure and format as with the origin-only model. The color coding of the tables— blue, orange, and green—follows the same function and order. To work with the models, direct changes to the value in the orange table. To restore the default values to this table, a backup version has been stored at the bottom of the work sheet. Because the origin-destination models incorporate trip distance, which greatly improves their explanatory power and application flexibility, an additional burden is placed on the user to be aware of the assumptions related to distance. The four modes in the mode-choice models operate over different distance ranges. Although the distance range for auto is virtually unlimited, and transit has great range (lim- ited primarily by system coverage), such is not the case for bicycling or walking. The mean distances for each mode from the tours modeled in the travel survey sample are correspondingly different, even for the same trip purpose. Hence, if one were to try to estimate mode shares for the sample of all trips without controlling for distance, the process would be trying to make an apples-versus-oranges comparison. In the sample calculations shown in this tab, these sample mean values are in fact used, and so the results shown must be regarded as biased because the distances are average, and not for common distance bands. Within the models, certain limits are imposed on trips by the various modes to account for their “availability” as realistic modes. At the bottom of the table of calculations (green “products” table) are shown the available Mode Choice Model: Product X = Mode Choice Model: Means Mode Choice Model: Coefficients

93 percent. As repeated Table 5-14 below, it can be seen that for work tours, for example, there are 4,483 cases where Auto is available as an alternative, but only 3,664 where Transit is available; 4,414 where Bicycle is available, and only 794 where Walk is available. The reason for the low number for walk trips is that walk trips were not assumed to be viable beyond 5 miles, while transit was not considered viable for very short trips because it cannot be connected to the network. To account for this effect on the mode-choice calculation, the composite utility values used to calculate the probabilities of selection are reduced by the corresponding “fraction avail- able.” Although this correction is not a perfect solution for the mismatch in average travel distances, it produces a more real- istic estimate of the potential mode shares (which are shown at the bottom of the table for both simple and complex tours). The more appropriate way to deal with this phenomenon is to break the choice process down into common distance bands. The computations are then made—more correctly— for similar trip length assumptions. This process plays out in a series of individual interactive worksheets, listed as indi- vidual tabs, focused on distance bands of 0 to 1 mile one- way trip distance, 1 to 2 miles, 2 to 3 miles, 3 to 4 miles, 4 to 5 miles, and over 5 miles. The means in the tables for each dis- tance band are reflective of the observations from the calibra- tion sample. This culling of the sample into distance groups does not eliminate the need to account for “available” cases, the adjustments for which are again shown at the bottom of the table of calculations. Tab: Tabulate Results This final worksheet provides a common location for the mode-choice estimates made from the different model con- figurations. These results are placed here manually simply for convenience to the user to study patterns and compare differences; they are not automatically fed by the respec- tive interactive model worksheets. However, if users wish to use this worksheet as a common place to store and ref- erence their own scenario results or create an active link, they should feel free to do so. Table 5-15 shows predicted mode shares by purpose and distance band, illustrating the differences associated with both purpose and simple versus complex tours. The user is urged to become familiar with this spread- sheet tool and its capabilities. It is anticipated that it will be a powerful tool for sensitivity testing, factoring methods, and sketch-planning approaches. Guidelines for Use: GIS Walk Accessibility Approach This approach was designed to • Quantify the combined effects of land use and travel net- work level of service on pedestrian travel demand. (This approach is also applicable for bicycle, but insufficient sur- vey data on bike trips prohibited full development.) • Rely substantially on GIS tools and data to create “acces- sibility” relationships, which may then be used to explain/ forecast non-motorized travel demand. • Calculate a walk-accessibility score (similar to Walk Score), which then serves as a means for estimating the number and percentage of trips in an area that will be made by walking (versus auto or transit; insufficient data to incorporate bike). • By changing either the land use (type and location of activ- ities) or the travel network, changes in walk-accessibility can be calculated and converted to changes in number of walk trips and mode-split. • Walk trip tables can be assigned to the respective walk net- work in a separate assignment program (not part of this tool). Scale of analysis • The characteristics of this tool make it most appropriate for applications at a subarea or site level. The most effective size Table 5-14. Number and percent of trips available in sample by purpose and mode. Purpose Cases Walk Bicycle Transit Auto Work Number Available 794 4414 3664 4483 Fracon Available 0.1771 0.9846 0.8173 1.00 School Number Available 757 1220 695 1327 Fracon Available 0.5705 0.9194 0.5237 1.00 Recreaon Number Available 744 1438 794 1516 Fracon Available 0.4908 0.9485 0.5237 1.00 Other Number Available 1326 2432 1457 2567 Fracon Available 0.5166 0.9474 0.5676 1.00 Work Based Number Available 353 430 195 476 Fracon Available 0.7416 0.9034 0.4097 1.00

94 Table 5-15. Estimated modes shares by distance, trip purpose and tour complexity. Home Based Work Distance Simple Tour Complex Tour (Rd Trip) Walk Bike Transit Auto Walk Bike Transit Auto 0 1 mile 44.6% 8.4% 0.4% 46.6% 20.3% 6.1% 0.4% 73.2% 1 2 miles 13.2% 10.5% 6.0% 70.3% 4.6% 5.8% 4.4% 85.1% 2 3 miles 1.4% 10.0% 7.9% 80.7% 0.5% 5.0% 5.3% 89.2% 3 4 miles 0.0% 8.9% 8.0% 83.1% 0.0% 4.4% 5.2% 90.3% 4 5 miles 0.0% 7.8% 7.5% 84.7% 0.0% 3.9% 4.9% 91.2% >5 miles 0.0% 2.0% 5.9% 92.1% 0.0% 0.9% 3.7% 95.3% Home Based School Simple Tour Complex Tour 0 1 mile 42.9% 3.5% 0.4% 53.2% 5.9% 0.6% 0.3% 93.2% 1 2 miles 7.2% 2.8% 5.7% 84.3% 0.6% 0.3% 3.0% 96.1% 2 3 miles 0.2% 1.5% 8.8% 89.5% 0.0% 0.2% 4.3% 95.5% 3 4 miles 0.0% 0.8% 8.8% 90.4% 0.0% 0.1% 4.3% 95.6% 4 5 miles 0.0% 0.5% 8.2% 91.3% 0.0% 0.1% 3.9% 96.0% >5 miles 0.0% 0.0% 6.9% 93.1% 0.0% 0.0% 3.3% 96.7% Home-Based Social/Rec Simple Tour Complex Tour 0 1 mile 71.9% 0.7% 0.1% 27.3% 23.5% 0.4% 0.2% 75.9% 1 2 miles 12.0% 1.9% 2.3% 83.8% 1.6% 0.4% 2.6% 95.4% 2 3 miles 0.4% 1.9% 4.1% 93.6% 0.0% 0.4% 4.1% 95.4% 3 4 miles 0.0% 1.7% 3.5% 94.8% 0.0% 0.3% 3.6% 96.1% 4 5 miles 0.0% 1.5% 3.8% 94.7% 0.0% 0.3% 3.9% 95.8% >5 miles 0.0% 0.7% 2.4% 97.0% 0.0% 0.1% 2.4% 97.5% Home Based Other Simple Tour Complex Tour 0 1 mile 46.2% 1.8% 0.1% 52.0% 16.3% 0.4% 0.0% 83.2% 1 2 miles 3.7% 1.6% 1.0% 93.7% 0.9% 0.2% 0.5% 98.4% 2 3 miles 0.0% 0.9% 1.3% 97.7% 0.0% 0.1% 0.7% 99.2% 3 4 miles 0.0% 0.5% 1.3% 98.3% 0.0% 0.1% 0.7% 99.3% 4 5 miles 0.0% 0.2% 1.1% 98.6% 0.0% 0.0% 0.6% 99.4% >5 miles 0.0% 0.0% 0.8% 99.2% 0.0% 0.0% 0.4% 99.6% Work Based Simple Tour Complex Tour 0 1 mile 91.2% 0.0% 0.0% 8.8% 40.8% 0.0% 0.0% 59.2% 1 2 miles 18.9% 0.0% 1.4% 79.7% 1.5% 0.0% 0.4% 98.1% 2 3 miles 0.3% 0.0% 1.7% 98.1% 0.0% 0.0% 0.4% 99.6% 3 4 miles 0.0% 0.0% 1.8% 98.2% 0.0% 0.0% 0.4% 99.6% 4 5 miles 0.0% 0.0% 1.8% 98.2% 0.0% 0.0% 0.4% 99.6% >5 miles 0.0% 0.0% 1.4% 98.6% 0.0% 0.0% 0.3% 99.7%

95 would be an area of about 30 to 40 census blocks, or 3 to 6 TAZs. • Ideal scale is linked to walk distances—what can be reached within 15–30 minutes of walking time (or about 1–2 miles). • Larger areas such as corridors may be better addressed if broken into several smaller areas. Data, tools, and expertise needed • For initial model calibration: Recent household travel sur- vey data, with trip ends coded to parcel, block face, or other fine-grained geography (for initial model calibration), plus point-level employment data from sources like Dun & Bradstreet or InfoUSA. • For model application: Census block-level data (popula- tion, households, employment [LEHD]). Users can choose other land units, such as parcels, grid cells, or even TAZs (for very coarse analysis), as long as data are available to support walk-accessibility score calculations and trip gen- eration routines. These formulas are customizable within the tool. • All-streets network obtained through NAVTEQ or TIGER. This may be augmented with non-motorized facilities, centroid connectors (e.g., connecting block centroids to multiple block faces), custom evaluators or other elements to obtain a rich pedestrian analysis network. At a mini- mum, the all-streets network should be used. • ArcGIS with Network Analyst and expertise to create paths and overlays. (Network analysis steps take place as inde- pendent exercises, the outputs of which may be fed into the tool. The current GIS-accessibility tool is a spread- sheet model and does not perform these GIS operations, although guidelines are provided on the process.) • Trip generation rates or equations from regional model (defaults are available within the tool, but may not be appli- cable depending on the land use data to be used in analyses). • Modal trip tables from regional model (not necessary if mode-split analysis is not required). Overview of Use The walk-accessibility model involves both a setup and an applications phase. A spreadsheet version of the model (WALC TRIPS XL) has been provided with the guidebook, which can be used for composing and evaluating scenarios (see CRP-CD-148). Both test data and an application scenario taken from Arlington County, VA, have been provided with the model. Users are encouraged to familiarize themselves with the tool using the pre-loaded data and scenario before attempting to develop the model for their own use. Basic steps in preparing and applying the model follow. These steps are described generically below and illustrated with a flowchart to create a clear picture of what the model is doing and what is required of the user at each step. Once these basic steps are defined, directions are then provided for replicating the steps with the spreadsheet model. 1. Model Setup The model setup process is profiled in Figure 5-5. This phase of the tool facilitates the analytical processes described in Chapter 4 to allow users to develop model relationships based on local data, rather than relying on the default rela- tionships derived from Arlington, VA. However, developing local relationships can be computationally intense and time- consuming. Users can skip these steps and apply the default relationships to a local planning problem. Preparing the model for use in a given area requires devel- oping accessibility relationships, derived from a combination of the following data resources: • Local travel survey data that contains trip-level information on mode, purpose, travel time or distance, and geographic identification of each trip end (exact latitude/longitude, parcel, or block face). • Socioeconomic data (SED) depicting population and employment data at a parcel, block, or other fine geo- graphic level. • GIS travel networks reflecting all streets and potential paths usable by cyclists or pedestrians. It is necessary to compute modal accessibilities for all modes being considered in the analysis (currently only walk). This is done for each trip end through the following steps: • First, a distance-decay relationship is developed that explains the willingness to travel by the given mode in rela- tion to the travel distance, or more accurately, travel time. This is done by preparing a distribution of trips by travel time for each mode being considered in the analysis (sepa- rately by purpose), and then fitting a curve to that rela- tionship (offered by Excel), which mathematically defines the rate at which demand declines as travel time increases (this is usually a represented in a logarithmic relationship, where utility for a destination falls rapidly but then at a slower rate as distance (time) increases). • Walk accessibilities: For each unique trip end, a GIS ana- lyst will ascertain the number of attractions that can be reached from the given trip end reference point by walk- ing along the pedestrian network. The attractions may be population or employment (by type), the location and identity of which are captured from Census block data or proprietary sources like Dun & Bradstreet. Destinations are discounted by their respective impedance (weighted travel time) as measured over the actual network, further discounted by the distance-decay rate, and then summed to a total accessibility “score” for a given location.

96 Mode-choice relationships are then derived from the accessibility scores. This is done by dividing the overall sam- ple of trips with accessibility information into “categories” (ranges of value) based on the shape of the distribution of the sample (constant increment, constant sample number, number of deviations from the mean). The percentage of trips by mode is then tabulated for each category, by pur- pose, and for each trip end as an origin and a destination. A curve is fitted to the shape of the distribution of mode share-by-accessibility range, which may then be used to cal- culate mode split in relation to a given accessibility score that would be generated in a planning scenario. Finding the best fit for these curves is often an iterative process. The analyst should consider the goodness of fit of the curve, the sample sizes created within each accessibility category, and the typi- cal accessibility attributes expected for a given trip purpose and end combination (e.g., the accessibility scores for home- based work origins will often be very different from home- based work destinations). 2. Model Application Once the model has been set up for the given area, use for analysis may begin. Again, the typical application environ- ment for this method would be a community or subarea of perhaps 1 to 2 square miles, encompassing 4 to 6 TAZs and perhaps 30 to 40 census blocks. Interest in defining such a setting could involve questions related to new development proposals, interest in modifying or testing the performance of the local transportation (especially non-motorized) net- works, or transit service or access improvements. The steps in application are as follows, with illustration provided by Figure 5-6: • Define Study Area: The user defines the study area of inter- est, generally an activity area ranging from one to several TAZs in size. Ideally, the area definition will be consistent with TAZ and census block group boundaries to facilitate sharing of information and later modifying vehicle trip tables to account for changes in mode split. Follow proto- cols to delineate the “study area” primary area of analysis, the “walkshed” surrounding network of blocks likely to share walk activity (productions and attractions) with the study area, and the “catchment area,” the area serving as the spillover for the “walkshed.” • Create Land Use Data Master File: Populate the defined system of blocks with SED information from the socio- economic data file prepared earlier. Record employment by type, population, and households by auto ownership level. Figure 5-5. Walk-accessibility model setup phase. 0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 0 20 40 60 80 100 Minutes CALCULATE DISTANCE DECAY RATES TRAVEL SURVEY DATA - Trip ID - Mode - Purpose - Travel me - O & D geog ID SOCIOECONOMIC DATA (block or parcel level) - Populaon/HHs - Dwelling Units by type - Employment by NAICS TRAVEL NETWORKS (link-level GIS format) - Mode - Weighted Impedance (travel me) CALCULATE ACCESSIBILITIES (by Trip Loc ID) - Mode - Purpose - Type Aracon - Funconal relaonship MODE SPLIT RELATIONSHIPS (by mode, purpose, O & D) - Paron accessibilies into categories - Tabulate trips by mode into specified categories - Graph relaonships, fit to curve, define equaon Y = 0.300679 * exp(-0.05237x)

97 • Trip Generation: Estimate total productions and attrac- tions for each block in the sample using either trip genera- tion rates obtained from the local MPO model or default values provided. • Walk-accessibility: Using network analysis methods, cal- culate walk travel times between land units (e.g., parcels or blocks) in the analysis area. For this analysis, land units in the study area and walkshed should be included as origins; all land units, including those in the catchment area, should be included as destinations. This is done off line by a GIS analyst using provided protocols and the information in the walk network and the SED information for each block. Walk travel time skims are then used to support the calculation of walk-accessibility scores in the scenario analyses (a routine for the latter is included in the model). • Walk Trip Productions and Attractions: Walk mode shares are calculated for each block based on the walk- accessibility score and the mode-choice relationships devel- oped during model setup. This is done for each trip purpose (home-based work, home-based other, work-based other, and non-home-based) and for the block as an origin or destination. These shares are then used to determine the portion of total person trip productions and attractions expected to be made by walking. STUDY AREA WALK SHED CATCHMENT AREA SOCIOECONOMIC DATA (block or parcel level) Populaon HHs by auto ownership Employment by NAICS LAND USE DATA (by block) Basic SED: Employment (ret, off, other) Populaon Households (0, 1, 2+ autos) Trip Generaon: Total Producons by purpose Total Aracons by purpose Walk mode share (from accessibility) Each block as O & D By purpose Walk Trip Generaon: Walk Producons by purpose Walk Aracons by purpose TRAVEL NETWORKS (link level GIS format) Mode Weighted Impedance (travel me) TRIP GENERATION RATES (local MPO or default) Producons and Aracons Purpose (HBW, HBO, NHB, WBO) TRAVEL TIME SKIMS Walk impedance between each block pair WALK TRIP DISTRIBUTION (trip tables, by purpose) Number walk trips occurring: Within SA and between SA and WS Walk trips within WS and between WS and CA are not distributed (data for balancing trips are incomplete). TRIP ASSIGNMENT Assign walk trips to network (oponal, not provided by current model) MODE SPLIT Aggregate walk trips by TAZ to TAZ Adjust auto and transit trips in proporon to number of walk trips SCENARIO TESTING Changes to land use Changes to network WALK ACCESSIBILITIES Calc accessibilies for each block: By trip purpose Figure 5-6. Walk-accessibility model—application phase.

98 • Create Walk Trip Table: A block-level walk trip table can be formed by performing trip distribution on the walk productions and attractions and the travel time skims (a procedure is provided in the model). This is done for each purpose. • Walk Trip Assignment: This walk trip table could be “assigned” to the walk network to assess facility-demand volumes, although given that there are trip tables for four different trip purposes, the tables would have to be com- bined into a single trip table for a given time of day in order to be able to perform a credible assignment. An assignment procedure is not provided in the model, but one is suggested in the Ped Context model featured as another tool; users with conventional transportation planning software, such as TP+, can probably use such utilities in those programs. • Impact on Auto and Transit Trips: The effect on trips by other modes can be determined by subtracting the walk trips from the corresponding auto and transit trip tables produced by the MPO travel model. To do this it is neces- sary to aggregate the block-level walk trip table to a TAZ level and then reduce the trips by auto and transit for the same origins and destinations. The model provides help with this translation between blocks and TAZs. • Scenario Testing: To evaluate changes in land use or net- work coverage/connectivity, the user enters changes in the boxes highlighted in Figure 5-6. The user can specify various land use and network scenarios (e.g., existing and several proposed futures). For land use changes, revised population or employment assumptions would be commu- nicated to the appropriate census blocks in the study area, as indicted by the line. For network changes, new or modified link information would be related to the computerized net- work file as indicated by the line. New accessibilities would then be computed for each land use/network combination, and the rest of the steps in the flowchart repeated. Use of Spreadsheet Model The walk-accessibility model is offered in the form of an Excel spreadsheet (CRP-CD-148) to enable the largest number of users to access and use it. In spreadsheet form, the workings and interrelationships of the model are also made more transparent. Access to GIS data and basic skills in performing network analysis in GIS are required because of the emphasis of the approach on accessibility, which is a very spatial commodity. Eventually, it will probably be more effective to package the model in a GIS-friendly planning model, such as Community Viz, where the user can make more direct use of the file management utilities and gain the benefit of GIS visualization capabilities. Such treatment would also offer more interaction among stakeholders dur- ing the planning process. A user’s guide is presented below as Exhibit 5-1, to aid readers in understanding and applying the WALC TRIPS XL model.

99 Exhibit 5-1. WALC TRIPS XL User’s Guide The WALC TRIPS XL model opens with the following master screen as shown in Figure 5-7. The screen partitions the processes of the model into four basic parts: • The left side of the screen contains directions on model setup, with press button access to the respective sheets and tables inside the spreadsheet. • The top left of the screen deals with input of required data to set up the model, while the bottom left accesses steps in processing that data into the necessary relationships. • The right side of the screen deals with model application and scenario testing. • The top right of the screen helps to manage input of data for the selected subarea, while the bottom right provides help with processing the data and running scenarios. Model Setup: The model setup steps assist users in developing custom relationships for model application. Analysts using the default relationships can skip these steps and move directly to the model application track. Travel Survey Data Following the generic steps discussed in the preceding section, the user enters the necessary travel survey data for the area or region under study. A visual of this screen and its counterpart (Location Accessibility Data) is shown as Figure 5-8. The model is designed to read the data fields shown from the source travel survey host file, assuming that they are of the same format. Guidance on importing data—including content, organization, and formatting (continued on page 101) Figure 5-7. WALC TRIPS XL master screen.

Figure 5-8. Import travel survey and trip end accessibility data. Input: Travel Survey Records TRIP_ID O_LOCATION* D_LOCATION* MODE TT GENPURP GENPURP2 O_TAZ D_TAZ O_ACCESS D_ACCESS 22110090202 40043725 764385669 9 80 HBW HBW 3630 1475 - 8,283 Import formatted survey records from file… 21017990107 782989672 18356978 2 65 HBO OBH 9999 1407 - 404 21024610107 18500920 18452244 2 60 HBO OBH 3091 1437 - 833 21025850103 41557859 18452582 2 60 HBW WBH 9999 1429 - 485 Please be sure the records to be imported 21026290407 90806626 18419364 2 45 HBO OBH 9999 1443 - 55 are properly formatted. Changes cannot 21026630103 90806626 839188423 2 60 HBO OBH 9999 1478 - 4,955 be undone. Limit 9,000 records. 21046500103 33760509 762698472 2 75 HBW WBH 3468 1454 - 10 WalkLnEmp 9 21048350107 113333595 18357125 2 90 WBO OBW 3569 1490 - 1,157 8 21049190102 40045565 762763281 2 120 HBW HBW 3647 1478 - 6,831 21051230202 90804583 128072676 2 90 HBW HBW 3595 1501 - 5,363 21055350203 33758985 824175449 2 60 HBW HBW 3473 1493 - 3,874 21059300202 18483511 18356699 2 75 HBW HBW 2978 1490 - 965 21180430106 18359969 18409014 2 45 NHB NHB 1511 312 - 236 21233340114 18420046 18358853 2 8 HBO OBH 1470 1505 - 2,901 21237220104 782989672 18420442 2 90 HBW WBH 9999 1473 - 5,757 21264400103 90806626 18359117 2 20 WBO OBW 9999 1524 - 762 21316910102 18551249 824175449 2 60 HBW HBW 3059 1493 - 3,874 21317080202 18551612 18356656 2 75 HBW HBW 3061 1496 - 2,732 21344940202 117267415 18355718 2 75 HBW HBW 2955 1536 - 3,361 21465980202 18500566 24716924 2 60 WBO OBW 3053 1475 - 7,319 21540030203 90806626 762643309 2 130 HBO OBH 9999 1431 - 1,121 21543370204 720277390 18358410 2 61 HBW WBH 3472 1491 - 3,281 21564330203 782989672 18420754 2 60 WBO OBW 9999 1475 - 7,982 21610720103 18484659 824176776 2 68 HBW HBW 2996 1496 - 2,204 21619080202 18555121 762764133 2 90 HBW HBW 3088 1458 - 7,361 21811990104 782989672 18356684 2 75 HBW WBH 9999 1530 - 1,395 21914310103 782989672 18422105 2 120 HBW WBH 9999 1483 - 3,470 21916420102 18488079 762764135 2 75 HBO OBH 3016 1415 - 7,305 21922180106 837590319 18421364 2 45 HBO OBH 3091 1474 - 3,755 Choose Accessibility Scores from the "Input_Location Access" tab AutoPolyCnt AutoPolyEmp BikePolyCnt BikePolyEmp TranExpCnt TranExpEmp WalkLnCnt WalkLnEmp CBG_AutoSLD Walk:AutoCnt Walk:AutoEmp Walk:CBG Import survey records Return to Main Menu Apply Selected Manage Accessibility Data Input: Accessibilty Data for Trip End Locations LOCATIONID* AutoPolyCn AutoPolyEmpBikePolyCntBikePolyEmp TranExpCnt TranExpEmp WalkLnCnt WalkLnEmp Import location accessibility data from file… 101896938 12101.319 165160.93 1.867444 72.298863 147.50373 3267.3576 3.4357349 185.239103 10863156 5384.3592 66326.332 751.822951 10747.1321 31.997074 513.06479 72.419457 1392.59507 108632420 5460.9639 65218.931 335.915272 3114.65535 2.3805199 7.7528217 4.5900191 15.451236 Please be sure the records to be imported are properly formatted 108632689 9622.4457 121816 1736.47962 24305.5758 272.48845 4010.0733 95.914781 1789.16724 such that LOCATION ID fields in this table and the SurveyData table 108632709 8189.1347 96946.661 1296.52364 15142.994 111.88172 1578.9289 61.663302 851.04706 match. Changes cannot be undone. 108632738 6284.6303 76799.179 763.601587 10444.7816 13.206351 126.5843 37.719928 334.69507 108633268 5251.2568 60672.628 476.612969 4728.4943 18.612856 111.21634 105.52928 950.178877 108635041 907.6714 8239.1411 114.078957 728.364176 1.0492873 8.3734928 5.0838086 20.7177344 108635648 2057.2141 22505.183 464.94535 194.86263 8.8867516 110.77444 24.641299 151.198331 108635818 11672.922 163065.34 1504.86434 5624.0141 110.00362 1293.1193 67.766139 953.803485 108635827 11687.518 161469 1400.76468 18614.3759 256.43914 3372.9752 100.95006 1651.21188 108635829 11456.328 159150.5 1403.63607 18324.025 236.08094 2894.1961 104.72207 1367.21733 108635851 9839.2814 132749.24 1353.32057 15320.1746 70.394498 656.58744 92.423754 757.530503 108636275 2913.21 35397.233 266.772646 2956.88938 11.609699 92.102777 26.580879 151.574962 Return to Main Menu Import Accessibility Data Select Active Accessibility Data 1. Use the “Import Survey Records” funcon to populate the white-shaded columns to the right with local travel survey data. Make sure the data are properly organized and formaed. 2. Use the “Import Accessibility Data” funcon to load accessibility scores associated with each trip end locaon. The field headings from the loaded data will appear in the menu on the Travel Survey Records page. 3. Select an accessibility heading from the list and click on “Apply Selected” to acvate that field as the accessibility score for further analysis. The blue-shaded fields will look up (see black dashed lines) the accessibility values associated with the origin and desnaon of each survey trip record.

101 requirements—is provided within the tool. Up to 9,000 trip records can be accepted. The blue-shaded columns on the far right of the table are the accessibility values computed for the corresponding trip ends—origin and destination for each trip. The menu on the bottom left allows the user to access this information from a sepa- rate file, wherein any number of accessibility measures may have been calculated by a GIS analyst for the study in response to requests from the project planner. This allows analysts to experiment with various constructions of the accessibility calculation (e.g., focusing on retail employment versus total employment) seamlessly when developing model relationships. Location Accessibility Data Using the trip end geographic identification information in the trip file, accessibilities are calculated in an off-line GIS process, which overlays the transportation network onto a layer of trip attractions and computes an acces- sibility score for the given mode and trip purpose. Those results are stored in this file of the spreadsheet and can be called on in the previous Travel Survey Data file and merged with the respective trip data. The annotations describe the interrelated workings of this page with the Travel Survey Data page. Distance Decay To calculate the accessibility scores, it is first necessary to determine distance-decay rates. A separate spread- sheet process for this task is accessed from the main menu under Analyze Data—Decay Rates (or using spreadsheet tab). The rates should then be used in calculating accessibilities. To support this procedure, as shown in Figure 5-9, the model pulls up the trips by mode and distance information from the Travel Survey database and posts the information as a table of the distributions for each mode. The model then allows the user to graph any of these distributions and fit a curve that best characterizes the shape of the distribution (log, linear, exponential, power, and binomial functions are offered). Generally, the curve with the highest R2 is selected, and its mathematical function saved to a file under the tab “Relationships.” The saved formula will be used in the model application steps later. Setup Distributions This screen (Figure 5-10) takes the calculated accessibilities and provides a visual basis for dividing the data into categories. These categories create “bins” for approximating modal shares in the next step. In the sample of trips each trip represents an observation, and that observation corresponds to a given mode— auto, transit, walk, or bicycle. The principal assumption behind this model is that choice of walk mode is directly related to the walk-accessibility score, both at origin and destination. However, to determine walk “mode share,” it is necessary to compare the number of walk trips made in a given walk-accessibility “range” with the number of trips made by all modes within the same range. This screen provides the user with statistical information on the distribution of all trips by walk-accessibility. By examining the shape of this distribution, the user can select a set of accessibility ranges that best subdivides the data for comparing differences in mode shares. There is a space at the bottom of the worksheet to specify the range categories for this sorting process. Eleven categories are required, ten of which will be active in the mode- split analysis (the eleventh category, representing the highest accessibility band is assumed to house outlier values). Users can either use the statistical breaks shown in the top of the worksheet by clicking on the “apply standard deviation breaks” button or enter their own category markers that they believe are more appropriate. Generally, the manual breaks will yield better results, and users are encouraged to work among the “Setup Dis- tributions,” “View Distributions,” and “Mode Split” tabs to find the distribution breaks that best describe walk trip-making by purpose and end. Exhibit 5-1. (Continued) (continued on page 104)

Figure 5-9. Calculation of distance (travel time) decay rate. 1. Use the menus to view distance decay pa erns by mode and experiment with different decay curves. 2. For walk trips, click on the “Acvate Selected Decay Curve” bu on to use the current decay formula in the WALC TRIPS XL model.

Figure 5-10. Setup distributions. 1. The distribuon of accessibility scores is ploed in the chart with descripve stascs provided to inform the user about the general accessibility profile of the region. 2. The user defines accessibility “bins,” ranges within which paerns of walk trip-making are similar. Different bins can be setup for different trip purposes and trip ends, allowing users to explore the ranges that are most appropriate for a given purpose/end combinaon. Although only one set of bins can be acve at any one me, the breaks for different purposes and ends can be stored in the scratch workspace provided for reference.

104 View Distributions The results of the binning process can then be viewed in the View Distributions screen, which provides the selected distributions in both tabular and graphic format, by origin or destination, trip purpose, and mode. Users can select a specific distribution of interest (e.g., HBW walk trips based on origin accessibility scores) to quickly zoom to that distribution in a new window. Mode Split The binned trip data from the Distributions task are then analyzed in the Mode Split screen to establish a rela- tionship between accessibility level and mode share. The mode shares for each accessibility group are plotted and fitted to a curve, similar to the distance-decay procedure earlier. The interface is virtually the same as the Decay screen, except that users can look at a particular trip purpose and trip end when analyzing mode-split patterns. For each purpose and end, the curve that best describes the walk mode share can be saved to the “Model Rela- tionships” page to activate the local accessibility relationship in the model application stages. Relationships This sheet serves as a common storage site for all computed relationships, including the Distance-Decay functions, Mode Split, and also Trip Generation. These trip generation rates are used later in the model application process. The default rates shown in Figure 5-11 have been taken from the MWCOG travel model; however, the user is encouraged to acquire equivalent rates for the local analysis area. Model Application: The right side of the model intro screen guides the application of the WALC TRIPS XL model, beginning with specification of the study area geography. For illustration, an example of an application performed on data for the Shirlington area of Arlington County is included on CRP-CD-148 with the model. Input Land Use Data Activating this tab off the main menu brings the user to a table for entry of the land use data for the area that will be placed under study. The interface is similar to the import pages of the model setup phase. The land use data will be in the spatial form of census blocks in the example, although users may select alternative small-scale geogra- phies for which they have data when running their own applications. The model is set up to read in a prepared file, to which the user applies a name corresponding to the land use scenario (e.g., “existing”). Guidance is provided within the tool about the content, organization, and format of imported data. Generally, there will be one land use file that describes base conditions, and then one or more scenario files (up to five files total in current version). There are 494 census blocks making up the example analysis area (41 TAZs), including the “catchment area.” Input Study Area Walk Skims The set of census blocks that define the study area and walkshed area serve as both potential origins and destina- tions, while blocks in the catchment area are included as destinations only. To quantify the ease of travel among all potential walk trip pairings, the walk network is super imposed on the census block geography, and network analy- sis procedures are used to define the shortest travel time path between all pairs. These are saved as a travel time skim matrix. The analysis should be rerun for all networks to be analyzed. For example, if a new shared-use path facility is planned, the network analysis should be run in the base condition (without the new path) and in the plan Exhibit 5-1. (Continued) (continued on page 106)

105 Figure 5-11. Model relationships. 1. Review model relaonships as defined through the model setup steps. 2. Manage trip generaon formulas. The field headings in the list are read from the Land Use Data page in the model applicaon phase. Users should take care to provide data in the format shown here when using the default relaonships in the model applicaon steps.

106 condition (with the path added to the network). This will produce two different walk time matrices that can be imported as “network scenarios” in the WALC TRIPS XL model. As with the Land Use input step, the model antici- pates access to and incorporation of these files from an external source, and multiple (up to 5) network scenarios can be defined. Specify and Run Scenarios This screen (Figure 5-12) is where the user specifies the land use and travel network conditions that will be run through the model as scenarios. For illustration, the screen is showing use of the base land use and base travel network to create an estimate of current conditions. To assess alternative scenarios, the user pairs any of the stored land use and network configurations using the features in the spreadsheet and the model calculates the new results. Up to ten combined land use and transportation scenarios may be defined. Summary of Results Each of the model runs of individual scenarios is stored at a summary level in the Scenario Results screen. The con- tents of this screen are shown in Figure 5-13 and include a summary of the average WALC accessibility value for Figure 5-12. Scenario setup screen. Exhibit 5-1. (Continued) (continued on page 108)

Figure 5-13. Summary of results.

108 the scenario, the number of walk productions and attractions by trip purpose, and the number of complete trips by purpose (distribution of Ps and As using the skims). Trips can be displayed for the study area only or the study area plus the surrounding walkshed. Update TAZ Trip Tables Users can use the update TAZ trip table routine to assemble the distributed walk trips (block to block flows) into estimates of pedestrian flows between TAZ pairs. Export Output Data Users can use the Export Output interface to export the results of the scenario analyses to tabular formats that can be used for additional analysis, mapping, and visualization. Figures 5-14 through 5-16 provide examples of the exported outputs for the Shirlington study area (the “combo” scenario represents a new development plus new network links, or Scenario 4 from Figure 5-13). Figure 5-14 portrays the walk mode share estimates for HBO trips under two scenarios. Other “land unit level” outputs include the WALC score at each land unit (e.g., parcel or block), total trips generated at each land unit by purpose, and unbalanced walk productions and attractions generated by each land unit by purpose. Figure 5-15 shows the “skim level” outputs. The exported table can be joined to lines that represent the point-to- point flows between each potential O-D pair in the land unit fabric. The joined data can then be referenced to map desire lines for pedestrian travel. In Figure 5-15, thicker lines represent larger numbers of pedestrian trips, and the arrows indicate the direction of travel (arrows point to the “destination” end of the O-D pair). In this way, users can represent the results of the WALC TRIPS XL spreadsheet’s trip distribution routine, which are also used to develop the TAZ trip tables shown in Figure 5-16. The trip tables are shown as they appear in the WALC TRIPS XL interface; however, they can also be exported to an unformatted matrix for additional work, such as updating trip tables in the regional travel demand model or mapping walk trip productions at TAZ origins. Exhibit 5-1. (Continued)

109 Figure 5-14. Example study area outputs mapped to census blocks: walk mode split.

110 Figure 5-15. Example study area outputs mapped to O-D pairs: distributed walk trips.

111 Figure 5-16. Example study area outputs: TAZ trip tables.

112 Guidelines for Use: Trip-Based Model Enhancements This approach was designed to • Provide users of conventional trip-based models with ways of improving the sensitivity of their models to land use and non-motorized travel through selective enhancements • Take advantage of research on the 4Ds methods to relate land use effects to trip-based TAZ models • Take advantage of smaller TAZ sizes as trip-based mod- els have been updated to reflect census block group geo- graphic scale and detail • Take non-motorized travel beyond trip generation into mode split and distribution by performing a pre-mode split separation into intra- and interzonal destination choice • Assist the following types of user: – Those with conventional trip-based models being asked to increase sensitivity to land use and non-motorized travel, but not considering a shift to an AB model – Those needing to analyze policies such as smart growth or transit investment and requiring more detail/resolu- tion on land use and non-motorized travel for regional planning, scenario planning, or visioning exercises Scale of analysis • It is expected that these enhancements would be made overall to the regional model, given that they involve adjustments in auto ownership, trip generation, distribu- tion, and mode split. • Modified tools can be used for regional, corridor, or sub- area types of analyses. Data, tools, and expertise needed • Familiarity with trip-based models and knowledge of their construction, sensitivities and application • GIS data and skills to develop measures and relationships • Sufficient statistical analysis skills to replicate the models in the examples or attempt to recalibrate them to local con- ditions Suggestions for Adaptation and Use: Table A-2 of Appendix A contains detailed information on each of the equations, their coefficients and statistical valid- ity, and (for most) elasticities. The relationships addressed include the following: • Vehicle ownership (model and elasticities) • NMT generation (model and elasticities) • Intrazonal versus interzonal trip-making (model and elasticities) • Intrazonal mode choice for HBW (model and elasticities) • Intrazonal mode choice for HBO (model and elasticities) • Intrazonal mode choice for NHB (model and elasticities) • Interzonal mode choice for HBW (model and elasticities) • Interzonal mode choice for HBO (model and elasticities) • Interzonal mode choice for NHB (model and elasticities) • Destination choice for HBW (models only) • Destination choice for HBO (models only) • Destination choice for NHB (models only) The reader should see the appendixes to the guidebook to view and assess any or all of the models or examine their sen- sitivities as represented in the elasticities. The reader also can refer to Appendix 3 of the Contractor’s Final Report which contains the model report which describes all of the model development and data issues in full detail. In terms of applying the findings and products of the Seattle model enhancements, the following options are recommended: • Adoption versus Emulation: The models shown are believed to be sufficiently unique to the Seattle region and the way in which some of the measures were developed (highly detailed parcel-level GIS network buffering) that direct application is not recommended. Instead, it is suggested that the user attempt to recreate the models with their own data. In the process, an effort should be made to make zone size (or area) into a controlled variable. • Pivot Analyses: The user may wish to examine the elastici- ties to gain a sense of the relative importance of the many implied relationships. Caution should be applied to the wholesale use of any elasticity presented without consider- ation of its relationship to other models in the chain of rela- tionships extending from Auto Ownership to Destination Choice because of possible interdependencies with those other models. To err on the side of caution, the elasticities from the Seattle enhancements work should be seen as indi- cators rather than robust relationships that can be directly transferred to another location without adequate proofing and sensitivity analyses. Hence, the emulation approach is the most strongly recommended of these approaches to use the Seattle model enhancements results. Guidelines for Use: Portland Pedestrian Model This approach was designed to • Enhance the pedestrian sensitivity in a trip-based model; pedestrian trips are estimated, then existing trip tables are adjusted and the remaining steps in the four-step process completed. • Assess the effects of land uses or transportation system components that are attractors of pedestrian travel (e.g., mixed-used developments or transit stations).

113 • Provide a relatively quick way of estimating the potential for pedestrian travel without requiring information or assistance from a regional model. • Create and test the value of an index (PIE) capable of rep- resenting the effects of the pedestrian scale built environ- ment on walking propensity. Scale of analysis • Most suitable application is at a neighborhood or subarea level (units are PAZs, roughly equivalent to blocks) • Results can be used to modify regional model predictions of mode split at all levels. Data, tools, and expertise needed • Travel survey data • GIS data and skills to develop built-environment measures • Sufficient statistical analysis skills to replicate the models in the examples, or attempt to recalibrate them to local conditions Suggestions for Adaptation and Use • Adoption versus Emulation: The researchers would not recommend direct use of the Portland Bike Share Model, given that its basic PIE index was developed from fairly site-specific data on built-environment characteristics and then processed and valued through Metro’s Context Model. Instead, the researchers would recommend follow- ing the steps used to develop the Portland model—as well as how it is used to inform the regional model and adjust pedestrian trips—using local data. • Pivot Analyses: The model relationships shown in Table A-3 of Appendix A can provide insights into the variables used in the models and their relative importance; however, elasticities have not been developed. Guidelines for Use: Model of Pedestrian Demand (MoPeD) This approach was designed to • Provide estimates of walk activity levels at intersections for safety exposure analysis • Reflect the role of land use and network coverage in gener- ating and assigning trips Scale of analysis • Neighborhood or subarea level • Analysis is scaled to PAZs, which are about the size of cen- sus blocks Data, tools, and expertise needed • Parcel and block-level land use data • GIS data and skills to develop buffered measures of land use • Familiarity with trip generation, distribution, and assign- ment routines in four-step models Suggestions for Adaptation and Use • The equations developed in this project, presented in Table A-5, address generation and distribution of walk trips. • The MoPeD project created software to assist in network development, creating PAZs, creating the land use mea- sures, and performing trip generation, distribution and assignment. It is recommended that users review the background report to gain a better understanding of the nature, strengths, and limitations of the model, to see if it is appropriate to answer their specific questions. The Maryland PedContext tool, the forerunner of the MoPeD model, offers considerably greater detail, though with potential limitations in acquiring the full model. In this event, it may be preferable to consider accessing and adapting the MoPeD model and enhancing it to begin to provide the additional detail exhibited in the PedContext report. Guidelines for Use: Maryland PedContext Model This approach was designed to • Provide reliable estimates of pedestrian volumes on links and at intersections to support safety analysis • Incorporate the influence of land use and network accessibility • Work independently of the regional trip-based model Scale of analysis • Neighborhood or subarea • Block-level geography • Sidewalk level network detail Data, tools, and expertise needed • Familiarity with trip-based models, particularly network preparation, land use allocation, trip generation, distribu- tion, and assignment steps • GIS data and skills to develop measures and relationships • Parcel and block-level land use data • Pedestrian network in GIS format; sidewalk information (observation or aerial photos) • Counts

114 Suggestions for Adaptation and Use • Adaptation/Transfer: The relationships in these models are probably not suitable for direct transfer. • Replicate/Emulate: The software package can be acquired through the Maryland State Highway Administration. Otherwise, the user can access the report and attempt to replicate the process used to develop PedContext and cre- ate their own utility programs to create and manage the models illustrated in Table A-4. • Pivot: No elasticities are associated with the PedContext work, so there appears to be little opportunity to extract transferrable relationships from the existing models. Guidelines for Use: Bicycle Route Choice Models This approach was designed to • Quantify the importance of particular attributes of a bicy- cle network in relation to choice of route—using observed behavioral data obtained through GPS recording. • Help design better bike systems by knowing the value of particular design attributes. • Discern differences that may be attributable to rider gen- der or trip purpose. • Provide additional input to determinations of bike acces- sibility and mode choice. Scale of analysis • Individual route to entire network • Regional to project level Data, tools, and expertise needed • For application, a detailed GIS rendition of bike-relevant travel network, with information on facility type, gradient, directness, crossings/delay, adjacent traffic, and so forth • To calibrate to a given site, a GPS survey of riders • To account for gender or purpose, a corresponding supple- ment to the GPS survey • Statistical skills to replicate existing models with local data • Counts Suggestions for Adaptation and Use • Adapt/Transfer: The Seattle TB model made direct use of the SFCTA model to develop bike skims weighted by physical attribute and separately for gender and work/non- work. Transfer should be possible with sensitivity testing against local data. • Replicate/Emulate: Given that the models (SFCTA and Portland) are available and probably transferable, it may not be necessary to go through a comprehensive replica- tion, which would require a potentially demanding GPS survey. Much depends on how different the new area is and how important it is to establish site-specific parameters. • Pivot: It is reasonable to use the relationships in Tables 5-16 and 5-17 to condition design criteria for bicycle networks and to provide weights for calculating impedances in networks. Guidelines for Use: Facility-Use Direct Demand Models This approach was designed to • Answer questions about facility use or needs that could not be addressed with traditional trip-based regional models because of limitations related to scale and ad hoc treat- ment of non-motorized modes. • Address the need for estimates of walk activity on links and at intersections for safety analysis and design. • Address the need for estimates of bicycle activity to sup- port questions on bike network design and to support decisions on facility needs. • Provide a better connection between the context of the given built environment and non-motorized travel behav- ior and demand. Scale of analysis • Subarea or corridor; potentially an individual site or project facility • Number of trips at particular locations, generally for spe- cific day of week/time of day period Data, tools, and expertise needed • High-level GIS data on land use and transportation networks • GIS data and skills to develop measures • Sufficient statistical analysis skills to create new models or replicate the models in examples Suggestions for Adaptation and Use Given the many tools that fall into this category, there is no one “best practice” example. However, the Santa Monica Table 5-16. SFCTA Model—marginal rates of substitution.

115 model reviewed in Chapter 4 and included in the list of mod- els in the toolkit as representative of this class of tools, is a good example. Table A-8 contains a summary of its equations (walk and bike models). Adaptation/Transfer: In general, these models should almost always be developed from scratch for the given site. Because they are linked to local context and activity levels (counts), they do not transfer well from area to area. For this reason, the researchers recommend use of direct demand models only under all four of the following circumstances: • They are well calibrated to existing conditions within the specific area and on the specific facilities under study, • They contain variables and variable sensitivities relevant to the decisions for which they will be used (e.g., terrain if an action under consideration is to reroute bike lane clas- sification to streets that involve hills), • They are not transferred from one region or study area to another, and • They are subjected to double-ended validation, replicating not only pedestrian or bicycle counts but demographics and choice characteristics from regional traveler surveys. Replication/Emulation: The user is advised to review any of the documented models in the main report and in par- ticular the subset cited in Chapter 4. The objective should be to find a model approach that seems to be most suited to the local setting, data, and problem being addressed. Criteria would include interest in walk or bike travel; travel market/ time of day being addressed; special provision for large or unique generators, such as universities, transit stations/lines, business or commercial districts; and the degree of detail in the available land use, demographic, and network data to sup- port creation of the variables of interest. Any direct demand type model will require high-quality volume count informa- tion; this information may need to be supplemented with user surveys if it is desired to account for sociodemographic traits, trip purpose, or origin-destination bearing. Pivot: As with the limitations for direct transfer, it is unlikely that relationships captured in existing models can be used as elasticities or adjustment factors in other loca- tions; however, one might borrow such relationships from the choice-based or route choice models in the toolkit to help design or sensitize a new model. A potential function for these models that may grow in importance is in collaboration with choice-based models, such as described at the end of Section 5.3. Corresponding estimates from the two types of models can be used to cross- check and validate each other in a double-ended validation. This process can also be used to identify potential enhance- ments to either tool that could lead to improved predictive power and accuracy. Table 5-17. Portland bike route choice model—relative attribute values.

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TRB’s National Cooperative Highway Research Program (NCHRP) Report 770: Estimating Bicycling and Walking for Planning and Project Development: A Guidebook contains methods and tools for practitioners to estimate bicycling and walking demand as part of regional-, corridor-, or project-level analyses.

The products of the research include a guidebook for practitioners on a range of methods for estimating bicycling and walking activity and a CD-ROM containing a GIS Walk Accessibility Model, spreadsheets, and the contractor’s final report, which documents the research and tools that operationalize the methods described in the guidebook.

The CD-ROM is also available for download from TRB’s website as an ISO image. Links to the ISO image and instructions for burning a CD-ROM from an ISO image are provided below.

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