Estimating Bicycling and Walking for Planning and Project Development: A Guidebook (2014)

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1 S u m m a r y This guidebook is the product of NCHRP Project 08-78, a multi-year research project tasked with developing improved methods for estimating bicycling and walking for plan- ning and project development. The project was in response to widely acknowledged needs for more robust and responsive analytic tools to support bicycle and pedestrian planning. These needs range from more realistic accounting for non-motorized travel in regional plan- ning to the design of mixed-use communities and multimodal corridors and, ultimately, to the design of efficient and safe non-motorized travel networks and individual facilities. Despite steadily growing interest in non-motorized travel, not only as serious transpor- tation modes unto themselves but because of the strong supporting role they play in the viability of transit and compact mixed-use development concepts, planning and analysis tools have not kept pace with demand. Although there has been considerable research on key factor relationships, this body of knowledge has not made its way into conventional practice. The goal of NCHRP Project 08-78 was to assess this knowledge, identify major gaps, and attempt to transform key lessons into serviceable planning tools. Planners and analysts have been seeking ways to address the following issues: • How to predict whether a person will choose walking or biking as travel mode. • How important the traveler’s sociodemographic characteristics (e.g., age, gender, income, education, and vehicle ownership) are in this decision versus other factors in the environment. • The relative appeal of walking or biking for particular trip purposes (e.g., travel to work/ school versus shopping, personal business, social activities, or recreation). • The degree to which travelers will choose to travel to a local opportunity by walking or biking versus driving to a more remote opportunity, and the effect of that choice on vehicle trip generation and vehicle miles of travel (VMT). • The role of non-motorized travel in the viability of compact, mixed-use (smart growth) development designs and transit-oriented development. • The importance of non-motorized access (at both trip ends) in the viability of transit. • The influence of non-motorized travel opportunities at the destination end of a trip in determining the mode that will be used for the initial trip (e.g., travel to work, shopping). • Determining the types and location of improvements to a bicycle or pedestrian network that will produce the greatest overall benefits. Current analytic options for estimating bicycle or pedestrian travel demand tend to fall into one of the following two categories: • Regional travel forecasting tools, such as are used by metropolitan planning organizations (MPOs), which are thorough but operate at a level of aggregation (traffic analysis zones [TAZs]) incompatible with the scale of non-motorized travel. Estimating Bicycling and Walking for Planning and Project Development: A Guidebook

2• Facility-demand models, which are constructed to directly explain count-based levels of user activity at intersections or on links through association with descriptive measures of the local environment. Given that neither group of tools addresses the types of planning and decision-making concerns listed above, NCHRP Project 08-78 was undertaken to provide such information. A thorough review of research and empirical findings on bicycle and pedestrian travel highlighted the importance of the following characteristics and factor relationships when attempting to explain or forecast non-motorized travel demand: • Recognizing an obvious but critical difference between biking and walking: although both are non-motorized modes and often combined as such in regional models, the distance range (0.7 mile average trip length for walk, 2.3 miles for bike), network needs, user char- acteristics, and trip purpose types are substantially different between the two modes. • The relationship between the built environment (land use) and travel network are extremely important, particularly for walking and biking. Walking and biking demand levels are heavily predicated on the number and variety of opportunities accessible within comfort- able travel distance/time envelopes. • Acceptable trip distances vary by trip purpose: travelers seem more willing to travel lon- ger distances for trips to work (about 1 mile for pedestrians, 4 miles for cyclists) than for personal business, shopping, or socializing (0.5 to 0.7 miles for pedestrians, 1.0 to 1.5 miles for cyclists). • Persons living in more compact, mixed-use settings tend to make more trips as simple tours (single-purpose one-stop journeys), while those in automobile-oriented settings make more multi-stop complex tours; the choice of walk, bike and transit as modes was found to be much more likely with simple tours. • The natural environment is of much greater consequence to non-motorized travelers than those traveling by automobile or transit: steep hills and topography that causes circuity in travel paths are barriers. Extremes in temperature, precipitation, and hours of daylight affect proclivity to walk or bike. • Personal safety is a major concern to non-motorized travelers, particularly in relation to exposure to motor vehicle traffic. In areas with higher traffic volumes or higher speeds, as in commercial areas, sidewalks and separated paths become more important consider- ations in the decision to walk or bike. • Sociodemographic differences are observed between motorized and non-motorized travel- ers, and between pedestrians and cyclists. In general, walking and biking rates peak in the youngest years, and then tail off with advancing age, although this is a trend more common in the United States than in other peer (western) countries. Although a somewhat higher percentage of women over 25 walk than men, male cyclists outnumber females by almost four to one (again a trend highly indigenous to the United States). Extensive review of these factor relationships suggested a fairly complex set of decisions being made concurrently, involving multiple factors and tradeoffs, with most being highly location specific. To account for these interrelationships in a way that captures their impor- tance to non-motorized travel and to make them accessible to planners as parameters in a planning analysis, a choice-based modeling framework was necessary. Choice-based implies that the travel behavior is the result of logical decision-making in which the traveler chooses rationally from a set of alternative modes and destinations in relation to the purpose of the trip, the array of mode and destination choices available in the particular environment, the sociodemographics of the traveler, and the intangibles of attitudes and preferences that are part of any framework that attempts to quantify human behavior.

3 The key challenges in devising such an approach were as follows: • Operating at a spatial scale fine enough to articulate the factors and conditions affecting pedestrian and bicycle travel opportunities and comparison of alternatives. • Directly accounting for the interplay between the shape of the built environment (e.g., number, type, and mix of activities) and the decision to walk or bike. • Accounting for the quality and accessibility of the bicycle and pedestrian travel networks, including differences in utility of travel on specific links across the networks based on physi- cal characteristics (e.g., facility type, separation from traffic, crossings, and slope/gradient). • Representing mode and destination choices from the perspective of the individual trav- eler, rather than as spatial aggregations of households in traffic analysis zones (TAZs). • Accounting for destination and mode as simultaneous choices. • Translating bicycle and pedestrian trip generation into trip flows and assigning those flows to the travel networks to produce estimates of demand at a facility level. A recurrent theme in the methods developed or adopted by the research team and included in the guidebook is “accessibility.” A central premise in a choice-based analytic framework is that alternatives are evaluated in relation to the “utility” they represent to their travelers. Accessibility is an effective measure of utility—it enumerates the oppor- tunities of a particular type (e.g., employment, retail, and health care) available to the traveler by a given mode. What makes accessibility a particularly useful measure is that it reflects both the activities available in the given land use patterns and the ease with which those activities can be reached over the respective modal travel network. Building models around the concept of accessibility provides a solid basis for explaining choice behavior and its inclusion in travel demand models enables planners to investigate both land use and transportation facility factors. Another element common to the NCHRP Project 08-78 planning methods was the use of geographic information systems (GIS). To measure accessibility for non-motorized travel modes, it is critical to push the level of geospatial resolution to a finer level than is present in TAZ models. The advancement of GIS tools and data has made it possible to create this fine-grained resolution and bring the necessary detail into such planning. Each method in the guidebook relies on GIS to some degree, which may be the principal technological factor enabling the analysis of bicycle and pedestrian behavior. The planning tools in this guidebook include entirely new methods, as well as existing methods found to have useful properties for particular applications. The tools developed as part of NCHRP Project 08-78 are as follows: • Tour-Generation and Mode-Split Models: In conjunction with the Puget Sound Coun- cil of Governments’ efforts to develop a new tour-based model structure for the Seattle region, research team members took advantage of new data and tools to develop a set of pedestrian and bicycle models, including a procedure for generating tours (as opposed to trips) by purpose, and a pair of modal-split models that predict walk, bike, transit, and automobile choice for five tour purposes. The variables included in these models provide access to a broad spectrum of sociodemographic, land use, and transportation network characteristics, and accessibility in estimating (separately) bicycle and pedestrian demand, as well as the effect on transit use of non-motorized accessibility. Although immediately suited to use in an activity- or tour-based environment, the methods may also be used to enhance conventional trip-based models, and a spreadsheet version of the model (available on CRP-CD-148) can be used for simultaneous testing of any of the relationships in the models or for creating sketch-planning tools.

4• GIS-Based Walk-Accessibility Model: Using data from the Metropolitan Washington (DC) Council of Governments (MWCOG) for Arlington County, VA, the research team developed a method for estimating walk trip generation and mode split that relies exclu- sively on GIS tools and data. The method uses geospatial overlay and network path-building procedures that are readily available in GIS to calculate measures of accessibility to or from any point by any mode and by type of attraction. The measures are similar to the popular Walk Score, but much more comprehensive in their calculation. By comparing the modal accessibilities, the model can estimate mode split and create walk trip tables by purpose. The current model does not perform network assignment of the walk trips; however, users probably can apply such features in their existing transportation planning software to do so. Because of insufficient data, the current model does not forecast bicycle demand, although the structure will readily accommodate such an enhancement when adequate data are available. This approach offers a new and intuitive way of interpreting modal choice that is responsive to changes in the built environment (land use) or the travel networks such as would occur in corridor or subarea planning, using generally available data and with relative independence from the respective regional travel model. • Enhancements to Trip-Based Models: Research team members also used the Seattle Puget Sound Regional Council (PSRC) data to create a template for systematically enhancing a conventional TAZ/trip-based regional model to improve its sensitivity to land use and non-motorized travel. Advanced statistical methods were used to create enhancements to the Auto Ownership, Trip Generation, Trip Distribution, and mode-choice steps in the existing PSRC regional model. Measures of automobile and non-motorized accessibility play a major role in these enhancements. Although pedestrian and bicycle mode choice are still constrained by the TAZ structure, the methods improve on the current process by introducing a “pre-mode split” step, which first divides trips into intra- versus inter- zonal groups, and then performs a mode-split step specific to those groups. Although the enhanced regional model may not be as fluid as the tour-based or GIS-accessibility approaches in overcoming TAZ aggregation issues, it takes advantage of the new smaller TAZs adopted by many metropolitan planning organizations (MPOs) and provides con- siderably more sensitivity in existing models. In addition to the tools developed directly by the NCHRP Project 08-78 research team, other tools, identified from existing practice, were found to merit inclusion in the guide- book. These are as follows: • Walk Trip Generation and Flow Models: The PedContext and MoPeD models developed through the University of Maryland’s National Center for Smart Growth offer a method for estimating walking trips and facility volumes at a subarea or neighborhood level. Both methods follow a variation of the four-step process, but operate at a much finer level of spatial resolution–block-size pedestrian analysis zones (PAZs). Both methods gener- ate estimates of pedestrian productions and attractions, create walk trip tables through a trip distribution process, and then assign the walk trips to the local walk network to estimate link and intersection activity levels. The difference in the methods is the degree of detail (e.g., trip purposes, equations, and assignment), with MoPeD being the less detailed of the two. Also, MoPeD uses open-source software, while PedContext is not fully open-source. The limitation of both tools is that they only generate walk trips and do not estimate effects on overall trip generation and mode choice—unlike the new GIS Walk-Accessibility model. • Portland Pedestrian Model: A third (and fairly recent) pedestrian demand estimation model is included in the guidebook because it is an interesting hybrid of the PedContext/

5 MoPeD models and the Seattle trip-based model enhancements. The procedure was devel- oped by Portland State University for Metro, the Portland, Oregon, MPO, to improve the pedestrian mode-choice capabilities in Metro’s existing trip-based model. The resulting procedure can either be used as an enhancement to the regional model or a stand-alone pedestrian planning tool. This model also uses PAZs as the analysis unit and estimates walk trip productions by purpose for each PAZ. Productions are not converted to trips through conventional trip distribution, but through use of Metro’s destination choice model. The trip tables thus formed can be reconstituted and used to adjust the motorized trip tables generated at the TAZ level. In addition to accessibility, a key role in trip generation is a “pedestrian index of the environment” (PIE) which shows good sensitivity in differentiat- ing areas by their land use and accessibility characteristics relevant to walking. • Facility Demand: Two very different types of models are presented in this category: route choice and direct demand. The route choice models apply solely to bicycle use and consist of tools developed by the San Francisco County Transportation Authority and Portland State University, both using GPS data collection methods to track bicycle trip-making. These data were then analyzed to determine the importance of factors such as type of facility, slope/gradient, directness, and exposure to traffic. Neither method predicts overall bicycle travel demand, but both methods offer insight on how travelers value these physical characteristics when choosing a route—information that is important in network design and in calculating accessibility. The direct demand models predict walk or bike facility use and volumes based on observed counts and context-driven regression models. The examples presented are taken from the City of Santa Monica (developed by Fehr and Peers) and San Diego, the result of the Caltrans-sponsored Seamless Travel Study performed by Alta Planning & Design and the University of California at Berkeley’s Traffic Safety Center. Network simulation was reviewed in the form of the Space Syntax model, but is not included in the recommended tools because it is proprietary and, hence, it was also difficult to be precise about how the models work. However, the approach is described in the guidebook and in the final report, including example applications in Oakland, California (pedestrian) and Cambridge, Massachusetts (bicycle) for those wishing to pursue this approach further. The guidebook describes each model in sufficient detail to convey a basic understanding of structure, key characteristics and variable relationships, strengths, and appropriate uses. Users then have guidance on comparing and choosing among the methods in relation to respective planning application needs and available resources. For the three new methods, step-by-step instructions are provided on how to adapt and use the tools, with options rang- ing from replication with local data to selective application with existing tools, and even use of elasticities for factoring and sketch-planning approaches. The two special spreadsheet versions of the tour-based and the walk-accessibility models (available on CRP-CD-148) are expected to be among the most popular products of the research and the guidebook. The tour-based model spreadsheet allows the user to per- form sensitivity analyses of a wide range of variables found to affect pedestrian and bicycle demand, including the following: • Traveler characteristics: age, gender, work/student status, income, vehicle ownership and competition, children. • Accessibility: attractions of a given type (employment, schools, retail, food service, entertainment/recreation) within 1 mile (walk), 2 miles (bike) or regionally (all modes). • Land use: household or employment density, mix of uses (entropy), intersection density, transit stop density, distance to nearest transit stop.

6• Transportation: mode-specific network distance/travel time for walk & bike, slope/gradient, sidewalk coverage, Class 1 or Class 2 bikeway coverage and directional efficiency (turns per mile, one-way streets), auto travel time and parking cost, transit in and out-of-vehicle time and fare. Base data are provided for each of the models in the spreadsheet, allowing the user to test assumptions involving any of the above variables—individually or in any simultaneous combination—and instantly see the effect on trip (tour) generation and mode-split for any of five different trip purposes. The walk-accessibility model spreadsheet also provides ready access for various users and use applications, with sample data and scenarios supplied. To apply the spreadsheet to one’s own situation, however, will require technical ability to create the various relationships in GIS, as well as access to basic land use and transportation network information. None of these skills or data requirements is outside what might be expected in a modern planning agency. Individual or small agency users will either need to possess the skills and data to set up the model or will need to collaborate with a larger planning entity (e.g., an MPO) to assist with some of the technical procedures. The guidebook is more limited in its accommodation of bicycle travel. The Seattle tour- based model includes bicycle as a separate mode throughout its structure and thus provides access to variables important to bicycle planning practitioners (e.g., transportation facility characteristics and network performance). The Seattle-derived trip-based model enhance- ments methods also incorporate bike throughout their structure, albeit at a TAZ level of aggregation, but they provide practical utility for a range of analytic uses and users. The other models featured in the guidebook are limited to pedestrian travel, either by origi- nal design or limitations in data. The walk-accessibility model developed from Arlington, Virginia, data could incorporate bicycle as a discrete modal choice, but would require a larger and more diverse sample of bicycle trips from travel surveys than was available to the research team. It is hoped that this guidebook will provide major new capabilities to the planning and practitioner community, not only those specifically involved in bicycle and pedestrian plan- ning but for land use/community planning, transit, policy evaluation and project prioritiza- tion. It is expected that this field of study will continue to evolve, and with it the capabili- ties of the modeling tools. This guidebook and the research will help existing practice and establish directions for future enhancement.