Assessing the transferability of the Pedestrian Index of the Environment (PIE) walkability measure for pedestrian modeling
Corresponding Author: Patrick Singleton, Portland State University
Presented By: Jaime Orrego, Portland State University
There have been important advances in non-motorized planning tools in recent years, including the development of the Model of Pedestrian Demand (MoPeD). MoPeD uses demographic and built environment information to predict the number and destination of walking trips at a small spatial scale (grid cells with 1/20-mile sides), and can work within or alongside a regional travel demand model (Clifton et al., 2016). To date, MoPeD has been piloted with success in the Portland region using data unique to Portland’s metropolitan planning organization. However, there is increasing interest from local governments and planning agencies within and outside of Portland and Oregon about adapting MoPeD and other pedestrian modeling tools for use in their own jurisdictions. These agencies seek to increase walking activity and create more walkable places, and they desire to apply these tools for a variety of planning and forecasting purposes (safety analyses, health impact assessments, etc.), not only for regional demand modeling. Unfortunately, other regions often do not have uniform access to the same kinds of pedestrian environment data as in Portland, particularly at such a fine-grained scale.
Important challenges remain in model development that must be overcome if these tools are to achieve widespread application. Among the most critical needs are the availability and standardization of model inputs, particularly measures of the built environment. In this work, we assess techniques for making MoPeD’s measures, models, and methods more transferable to other locations. Specifically, we analyze, re-evaluate, compare, and test MoPeD’s Pedestrian Index of the Environment (PIE) measure using data resources more commonly available to planning agencies across the country. In the process, we balance data availability, scale, computational capacity, and theoretical soundness. This work is a vital step towards re-estimating MoPeD’s pedestrian trip generation and destination choice models using the new PIE variable, yielding an updated pedestrian model for testing in other regions and in urban/suburban contexts.
Clifton, K. J., Singleton, P. A., Muhs, C. D., & Schneider, R. J. (2016). Representing pedestrian activity in travel demand models: Framework and application. Journal of Transport Geography, 52, 111–122. http://dx.doi.org/10.1016/j.jtrangeo.2016.03.009