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From page 59... ... 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 preceding chapters into a set of methods and guidelines to address planning questions related to non-motorized travel demand. Read the entire page →
From page 60... ... 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. Read the entire page →
From page 61... ... 61 a significant assisting role but probably unable to perform the entire task alone (A) , having a partial but potentially useful role (P) Read the entire page →
From page 62... ... 62 Disaggregate Tour based (Seale) GIS Based Accessibility (Arlington) Read the entire page →
From page 63... ... 63 Disaggregate Tour based (Seale) GIS Based Accessibility (Arlington) Read the entire page →
From page 64... ... 64 Disaggregate Tour based (Seale) GIS Based Accessibility (Arlington) Read the entire page →
From page 65... ... 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. Read the entire page →
From page 66... ... 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) Read the entire page →
From page 67... ... 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. Read the entire page →
From page 68... ... 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. Read the entire page →
From page 69... ... 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. Read the entire page →
From page 70... ... 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. Read the entire page →
From page 71... ... 71 Weaknesses • Does not predict bicycle trips. • Deals only with walk (versus non-walk) Read the entire page →
From page 72... ... 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. Read the entire page →
From page 73... ... 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. Read the entire page →
From page 74... ... 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 incorporating such attributes as directness, facility type (sidewalk and Class I, II, III bike paths) Read the entire page →
From page 75... ... 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 comparing 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) Read the entire page →
From page 76... ... 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. Read the entire page →
From page 77... ... 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 application, 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. Read the entire page →
From page 78... ... 78 Tour Based GIS Walk Access Enhanced TR based PedContext & MoPeD Portland Walk Bike Route Choice Direct Demand Trip Gen (All) Read the entire page →
From page 79... ... 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 conditions associated with the travel network -- directness, connectivity, safety, and hills -- and these sensitivities vary by type of traveler and trip purpose, accurately representing these preferences is key to modeling bike travel. Read the entire page →
From page 80... ... 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. Read the entire page →
From page 81... ... 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. Read the entire page →
From page 82... ... 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 topography, 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. Read the entire page →
From page 83... ... 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. Read the entire page →
From page 84... ... 84 – Trip-based model users who wish to enhance their models for bike-pedestrian analysis, and – Persons seeking better understanding or key relationships 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. Read the entire page →
From page 85... ... 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 bufferbased measures and, ideally, in defining transit walk access and egress times for each O-D. Read the entire page →
From page 86... ... 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) Read the entire page →
From page 87... ... 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. Read the entire page →
From page 88... ... 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. Read the entire page →
From page 89... ... 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. Read the entire page →
From page 90... ... 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. Read the entire page →
From page 91... ... 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 information 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) Read the entire page →
From page 92... ... 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 interactive 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. Read the entire page →
From page 93... ... 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. Read the entire page →
From page 94... ... 94 Table 5-15. Estimated modes shares by distance, trip purpose and tour complexity. Read the entire page →
From page 95... ... 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) Read the entire page →
From page 96... ... 96 Mode-choice relationships are then derived from the accessibility scores. This is done by dividing the overall sample of trips with accessibility information into "categories" (ranges of value) Read the entire page →
From page 97... ... 97 • Trip Generation: Estimate total productions and attractions for each block in the sample using either trip generation rates obtained from the local MPO model or default values provided. • Walk-accessibility: Using network analysis methods, calculate walk travel times between land units (e.g., parcels or blocks) Read the entire page →
From page 98... ... 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) Read the entire page →
From page 99... ... 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. Read the entire page →
From page 100... ... Figure 5-8. Import travel survey and trip end accessibility data. Read the entire page →
From page 101... ... 101 requirements -- is provided within the tool. Up to 9,000 trip records can be accepted. Read the entire page →
From page 102... ... Figure 5-9. Calculation of distance (travel time) Read the entire page →
From page 103... ... Figure 5-10. Setup distributions. Read the entire page →
From page 104... ... 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) Read the entire page →
From page 105... ... 105 Figure 5-11. Model relationships. Read the entire page →
From page 106... ... 106 condition (with the path added to the network) Read the entire page →
From page 107... ... Figure 5-13. Summary of results. Read the entire page →
From page 108... ... 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) Read the entire page →
From page 109... ... 109 Figure 5-14. Example study area outputs mapped to census blocks: walk mode split. Read the entire page →
From page 110... ... 110 Figure 5-15. Example study area outputs mapped to O-D pairs: distributed walk trips. Read the entire page →
From page 111... ... 111 Figure 5-16. Example study area outputs: TAZ trip tables. Read the entire page →
From page 112... ... 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 models have been updated to reflect census block group geographic 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/resolution 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, distribution, and mode split. • Modified tools can be used for regional, corridor, or subarea types of analyses. Read the entire page →
From page 113... ... 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) Read the entire page →
From page 114... ... 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. Read the entire page →
From page 115... ... 115 model reviewed in Chapter 4 and included in the list of models 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) Read the entire page →

From page 59...

... 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 preceding chapters into a set of methods and guidelines to address planning questions related to non-motorized travel demand.