Much has changed in the 30 years since non-motorized modes were first included in regional travel demand models. As interest in understanding behavioral influences on walking and policies requiring estimates of walking activity increase, it is important to consider how pedestrian travel is modeled at a regional level. This paper evaluates the state-of-the-practice of modeling walk trips among the largest 48 metropolitan planning organizations (MPOs) and assesses changes made over the last five years. By reviewing model documentation and responses to a survey of MPO modelers, this paper summarizes current practices, describes six pedestrian modeling frameworks, and identifies trends.Three-quarters (75%) of large MPOs now model non-motorized travel, and over two-thirds (69%) of those MPOs distinguish walking from bicycling; these percentages are up from nearly two-thirds (63%) and one-half (47%), respectively, in 2012. This change corresponds with an increase in the deployment of activity-based models, which offer the opportunity to enhance pedestrian modeling techniques. The biggest barrier to more sophisticated models remains a lack of travel survey data on walking behavior, yet some MPOs are starting to overcome this challenge by oversampling potential active travelers. Decision-makers are becoming more interested in analyzing walking and using estimates of walking activity that are output from models for various planning applications. As the practice continues to mature, the near future will likely see smallerscale measures of the pedestrian environment, more detailed zonal and network structures, and possibly even an operational model of pedestrian route choice. A growing number of U.S. metropolitan planning organizations (MPOs) have incorporated walking and other non-motorized modes into their regional travel demand forecasting models (1). These models are used for long-range planning, allowing regions to analyze land use and transportation scenarios and projects for their impacts on walking and bicycling. Models that can forecast walking also have many other applications. In the short-term, they are useful for prioritizing non-motorized infrastructure investments. In the long run, models that are sensitive to how the pedestrian environment influences travel behavior could better predict mode shifts and the resulting impacts on motor vehicle emissions of greenhouse gases and other air pollutants. Regional pedestrian models can also inform traffic safety analyses and health impact assessments, providing needed estimates of the location and number of walk trips.Partly in response to these new policy demands and partly due to advances in computational power, travel demand models are becoming more sensitive to finer-grained representations of travelers themselves and the environments through which people travel. The growing number of implemented activity-based models is one example of this evolution. Two other major trends support these modeling enhancements. First, archived spatial data on the built environment ...

Trip generation is the first step in the traditional four-step trip-based transportation model and an important transport outcome used in evaluating the impacts of new development. There has been a long debate on the association between trip generation and the built environment, with mixed results. This paper contributes to this debate and approaches the problem with two hypotheses: 1) built environment variables have significant impacts on household total trip generation; and 2) built environment variables have different impacts on trip generation by purpose. This study relied on data from the Portland, Oregon, metropolitan area to estimate negative binomial regression models of household trip generation rates across all modes. Results show that the built environment does have significant and positive influences on trip generation, especially for total number of trips, total number of tours, and home-based shopping-related trips. Moreover, log likelihood ratio tests implied that adding built environment to the base model contributed significantly to improving model explanatory and predictability. These findings suggest that transportation demand models should be more sensitive to the effects of the built environment to better reflect the variations in trip making across regions.

This project focuses on making our measures, models, and methods more transferable to other locations. Specifically, we re-evaluate, compare and test our pedestrian index of the environment (PIE) measure using data resources more commonly available to planning agencies across the country. Next, we test the results of PIE and its input data in models of pedestrian mode choice for stability of estimation results within a region (intraregional) and between regions (interregional). This research is the next logical step in the MoPeD's enhancement and is critical to enabling its utility beyond the Portland region.The results of this project show that population density and pedestrian connectivity had the most consistent and strong relationship to walk mode choice across all of our regions, which echoed the long literature on this topic. However, the other components of the built environment included in PIE had more variability in their ability to explain walk mode choice. Employment density and its subset urban living infrastructure (ULI), intended to capture retail and service access, had less explanatory power and stability in the cities tested. Based upon these findings, we provide several guidelines for the construct of walkability indices, including variables and spatial scales.Our findings raise questions about the relationship between walking and the built environment within a region and thus, the intraregional transferability of one walkability index is suspect. Estimation results suggest that there may be different responses to the built environment in lower-density vs. higher density regimes and that these relationships may be nonlinear. However, smaller sample sizes of travel survey data in high density areas in all of the US cities tested pose limitations to drawing more confident conclusions from these results.The interregional comparisons of PIE and walk mode share between Los Angeles and Portland showed promise for the use of the index in different regions. In these two regions, model results showed a similar walk mode share for the same values of PIE constructed at the block group level. This provides initial support that the PIEbg construct may be transferrable between metropolitan regions, in part, due to population density's prominent role in PIE.