Estimating Bicycling and Walking for Planning and Project Development: A Guidebook (2014) / Chapter Skim
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From page 30... ...
30 4.1 Identification of Planning Needs and Assessment of Available Tools The goal of NCHRP Project 08-78 has been to provide planners and analysts needing to estimate the demand for bicycle or pedestrian travel with (1) a better understanding of the key underlying relationships and (2)
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From page 31... ...
31 travel demand, largely because of coarse scale of analysis attributable to the TAZ aggregation of land use. If these models are used to account for non-motorized travel, it is typically limited to the trip generation step; non-motorized trip productions and attractions are estimated, but they are then removed from the remainder of the analysis, which focuses on motor vehicle trips.
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From page 32... ...
32 Also among the tools in this genre that provide valuable insights on factor relationships but lack the structure to serve as complete or practical planning tools are • Aggregate demand methods that attempt to explain regional (or similar large area) activity levels of walking or biking based on aggregate population, employment, density, facility mileage, and even climate factors.
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33 offerings in the existing body of methods or focus of research. NCHRP Project 08-78 found that planning at this level is either done with a focused application of the respective regional model (albeit lacking sensitivity to land use and non-motorized travel)
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34 will embody some semblance of this holistic, choice-based behavioral structure. This is not to say that the facility-based methods have no value in bicycle/pedestrian planning.
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35 tour-based environment, the methods may also be used to enhance conventional trip-based models, and a spreadsheet version of the model can be used for simultaneous testing of any of the relationships in the models or for creating sketchplanning tools. GIS-Based Walk-Accessibility Model: Using data from the Metropolitan Washington (DC)
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From page 36... ...
36 accommodate such an enhancement when adequate data are available. This approach offers a new and intuitive way of interpreting modal choice that is very responsive to changes in the built environment (land use)
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37 commonly occur as multi-stop complex tours, for efficiency. The multi-stop tours are generally made by auto, while simple tours are more likely to be made by walking, biking, or transit.
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38 would benefit from improvements in local land use or network coverage that directly improve local accessibility, or from changes that might occur regionally (e.g., new highway or transit line, congestion delay, or changes in fuel prices) that would affect the desirability of longer trips by driving or transit.
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From page 39... ...
39 Planners as well as non-planners are familiar with Walk Score, the internet application that attempts to quantify the level of walkability for any given place on a scale of 1 to 100. This statistic is widely used to assess the richness of access to local activities and is even employed by the real estate industry as an added-value attribute when marketing properties.
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From page 40... ...
Walk Bike Transit Auto Walk Bike Transit Auto Walk Bike Transit Auto Walk Bike Transit Auto Walk Bike Transit Auto Constant 7.31 3.61 3.78 3.82 7.69 2.94 2.92 4.84 5.78 3.03 6 8.5 3.49 3.49 0.986 Income < $25k 0.379 1.14 2.38 0.647 0.813 Income > $100k 0.546 0.42 0.256 1.81 Male 0.337 0.676 0.32 0.711 1.96 0.72 Age <35 1.38 0.412 1.25 0.26 Age > 50 0.833 0.991 2.17 0.486 0.338 Zero car HH 4.69 5 3.09 3.6 Adults > Cars 1.21 1.16 0.799 0.417 Buffer 1 a ractions for purpose 0.403 0.423 0.262 0.36 Buffer 2 a ractions for purpose 0.22 Mode/des na on logsum with zero cars 0.245 0.289 0.0922 0.355 0.699 Mode/des na on logsum with full car own 0.154 0.0944 0.04 Buffer 1 household density 0.00026 Buffer 1 net intersec on density 0.0043 0.00007 0.00048 0.0101 0.00014 Buffer 2 net intersec on density 0.0087 0.0127 Buffer 1 average frac on rise 29.2 35.5 Buffer 2 average frac on rise 62.6 31.4 92.5 Buffer 2 frac on Class 1 bike path 2.4 3.15 Buffer 1 percent no sidewalk 1.04 1.38 0.769 2.96 1.12 1.6 3.89 Buffer 1 transit stops 0.737 0.291 0.121 0.296 0.312 Buffer 1mixed use index 0.716 0.454 1.36 0.791 0.559 Walked to work Bike to work 2 Transit to work 0.574 Car to work 1.67 Tour Complexity 1.45 1.08 0.781 2.21 2.18 0.314 1.33 0.628 0.693 1.3 1.59 0.361 1.61 2 0.677 Work BasedHome BasedWork Home Based School Home Based Social/Rec Home Based Other Table 4-4. Tour mode choice models with origin-only information (shown values are estimated coefficients, not elasticities)
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From page 41... ...
Walk Bike Transit Auto Walk Bike Transit Auto Walk Bike Transit Auto Walk Bike Transit Auto Walk Bike Transit Auto Constant 1.07 2.92 4.74 0.91 4.12 1.6 2.96 4.53 3.15 1.81 3.74 5.61 3.34 8.82 8.05 Income < $25k 0.863 0.961 0.36 3.02 0.0615 0.702 0.468 Income > $100k 0.412 0.447 0.669 0.0075 0.498 1.15 0.121 1.57 Male 0.534 0.859 0.186 0.578 1.71 0.0012 0.0356 2.05 0.325 0.119 0.842 0.215 1.16 Age <35 1.45 0.398 0.36 0.0084 0.285 0.458 2.25 Age > 50 0.863 1.26 0.518 Zero car HH 4.7 5 3.32 4.32 10 Adults > Cars 1.4 1.27 0.976 0.633 0 Route choice generalized distance 0.113 0.277 0.0874 0.276 0.331 Distance (over network) 0.942 1.45 1.6 1.87 1.88 Pct Class 1 path Pct Class 2 path Frac on wrong way Turns/mile Frac on rise Dest Buffer 1 Tot Emp 3.80E 05 2.70E 05 Dest Buffer 2 Tot Emp Dest Buffer 2 Emp Density 3.70E 07 Orig+Dest Buffer 1 Avg Intersec on Density 0.005 0.0111 Orig Buffer 1 Intersect Density 1.50E 04 Orig Buffer 2 Intersect Density 0.0061 Orig+Dest Buffer 2 avg Frac on Class 1 Path 4.97 3.01 Orig+Dest Buffer 1 Avg Frac on Rise 61.3 9.85 15.6 Orig Buffer 1 Frac on Rise 36.2 Orig Buffer 2 Avg Frac on Rise 77.8 Orig Buffer 1 Transit stops 0.539 0.334 0.608 0.214 Dest Buffer 1 Transit Stops 0.179 0.268 0.825 0.606 1.73 Orig Buffer 1 Pct.
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From page 42... ...
42 focused on Arlington County, VA. Given that Arlington is part of the Washington, DC, region, its selection provided access to the resources of both the County and the MWCOG, including a recent (2008)
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43 Figure 4-4. Mode choice in relation to walk-accessibility score -- home-based work travel.
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44 also shown in each graph, illustrating both a logarithmic relationship in each curve and a high R2 value reflecting goodness of fit. Table 4-7 shows that projected walk mode split for homebased work trips increases from about 1% at the lowest walk-accessibility level at the origin to 14% at the highest accessibility location, while transit share also increases from 30% to 50% and auto share declines from 65% to 35%; at the destination end, the increase in walk share is somewhat less (3% to 9%)
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45 Perhaps as important as the effect of walk-accessibility on walk mode share is the effect that higher walk-accessibility has on transit share, particularly at the destination end. This may be due simply to destinations being more walk accessible to transit users, but may also provide evidence that travelers are more likely to use transit if they do not have to be dependent on personal vehicles once they reach their primary destinations.
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46 changes in overall walking levels -- by origin-destination block pair and by trip purpose. A complete documentation of the development of the walk-accessibility model is also provided as Appendix 2 of the Contractor's Final Report.
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47 sensitize as many of the steps in the process to important land use effects as possible. The primary limitation posed by most trip-based models when trying to analyze non-motorized travel is the aggregation inherent in the use of TAZs.
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48 regional models are beginning to incorporate context factors when predicting household vehicle ownership (see Atlanta, Austin, Los Angeles, and Portland examples in Table 4-2)
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49 Intra- Versus Interzonal Destination Choice: The typical trip-based model does not carry non-motorized trips beyond trip generation. With the downsizing of TAZs, greater opportunity exists to begin to include non-motorized trips into the destination choice and mode-choice determinations.
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50 PedContext Model PedContext is the more detailed of the two pedestrian models. It features a land use allocation step, pedestrian travel generator, a distribution module, and a stochastic assignment procedure to allocate the estimated pedestrian trips to the walk network.
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51 MoPeD is similar to PedContext in the following ways: • Census TIGER network line files were enhanced to represent the full pedestrian network, accounting for the connectivity and impedances associated with sidewalks and crosswalks. • The spatial units of analysis are PAZs, which are in the form of blocks and block faces.
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52 to be the highest-weighted attribute, followed by transit access. If each attribute were to realize its maximum value (5)
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53 Once the pedestrian trip tables were determined, nonpedestrian trips could then be aggregated up to the TAZ-level trips and passed to the regional model for further analysis. This is very similar to how the walk-accessibility model developed for Arlington operates.
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54 Class I, II, and III facilities in explaining choice of route. It also accounts for different trip purposes (work versus non-work)
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55 information on how these different characteristics are weighed by the traveler by converting those preferences into quantitative factors influencing perceptions of travel time or distance, so, if the planning question is to determine what improvements would make one path better than another, these tools would be directly relevant. However, these tools do not attempt to predict whether a bike trip will be made, which destination will be chosen over another, or whether the bike mode will be chosen over another for that destination.
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From page 56... ...
56 but in terms of "transitions" from one space to another. The approach requires coding of a detailed network, which is then treated as a "graph." Topological methods are used to characterize the properties of the network (graph)
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57 The Seamless pedestrian model is of the following form: P 1.555 0.723 ED 0.526 PD –1.09 R R 0.516AM 2( ) = + + = where PAM = Morning peak pedestrian count ED = Employment density within 0.5 mile PD = Population density within 0.25 mile R = Presence of retail within 0.5 mile So the model predicts that A.M.
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From page 58... ...
58 specified (variables included) and the specific location for which they were developed.
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