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The Pedestrian Index of the Environment (PIE): Representing the Walking Environment in Planning Applications

Patrick A. Singleton, Robert J. Schneider, Christopher D. Muhs, Kelly J. Clifton

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Abstract

This paper introduces a new area-based measure for pedestrian planning and modeling, the Pedestrian Index of the Environment (PIE). It is based on estimates of the probability that a trip within a defined geographic zone will be made by walking using empirical relationships with the built environment. The PIE was calculated by applying weights to six built environment measures: comfortable facilities, block size, access to parks, people per acre, sidewalk density, transit access, and pedestrian-friendly businesses. The built environment measures were calculated at quarter-mile (0.4 km) or one-mile (1.6 km) search radii for 264 ft x 264 ft (80 m x 80 m) raster grid cells throughout the entire region. Weights were generated by using household travel survey data to regress the probability of walking on each individual built environment measure. The PIE improves upon existing pedestrian environment measures because, unlike many of them, the underlying associations between walking activity and the individual factors were developed empirically. This makes it possible for practitioners to “unpack” the changes in the physical environment required to achieve a unit change in the PIE and better assess walkability. The small scale at which the PIE operates is also more appropriate for walking, especially because it can capture short trips. While originally developed to create a practical predictive method for estimating walk trips in regional travel demand forecasting models, PIE may also have applications as a standalone pedestrian planning tool for researchers and practitioners.

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What this paper is about

This paper introduces a new area-based measure for pedestrian planning and modeling, the Pedestrian Index of the Environment (PIE). It is based on estimates of the probability that a trip within a defined geographic zone will be made by walking using empirical relationships with the built environment. The PIE was calculated by applying weights to six built environment measures: comfortable facilities, block size, access to parks, people per acre, sidewalk density, transit access, and pedestrian-friendly businesses. The built environment measures were calculated at quarter-mile (0.4 km) or one-mile (1.6 km) search radii for 264 ft x 264 ft (80 m x 80 m) raster grid cells throughout the entire region. Weights were generated by using household travel survey data to regress the probability of walking on each individual built environment measure. The PIE improves upon existing pedestrian environment measures because, unlike many of them, the underlying associations between walking activity and the individual factors were developed empirically. This makes it possible for practitioners to “unpack” the changes in the physical environment required to achieve a unit change in the PIE and better assess walkability. The small scale at which the PIE operates is also more appropriate for walking, especially because it can capture short trips. While originally developed to create a practical predictive method for estimating walk trips in regional travel demand forecasting models, PIE may also have applications as a standalone pedestrian planning tool for researchers and practitioners.

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Available abstract

This paper introduces a new area-based measure for pedestrian planning and modeling, the Pedestrian Index of the Environment (PIE). It is based on estimates of the probability that a trip within a defined geographic zone will be made by walking using empirical relationships with the built environment. The PIE was calculated by applying weights to six built environment measures: comfortable facilities, block size, access to parks, people per acre, sidewalk density, transit access, and pedestrian-friendly businesses. The built environment measures were calculated at quarter-mile (0.4 km) or one-mile (1.6 km) search radii for 264 ft x 264 ft (80 m x 80 m) raster grid cells throughout the entire region. Weights were generated by using household travel survey data to regress the probability of walking on each individual built environment measure. The PIE improves upon existing pedestrian environment measures because, unlike many of them, the underlying associations between walking activity and the individual factors were developed empirically. This makes it possible for practitioners to “unpack” the changes in the physical environment required to achieve a unit change in the PIE and better assess walkability. The small scale at which the PIE operates is also more appropriate for walking, especially because it can capture short trips. While originally developed to create a practical predictive method for estimating walk trips in regional travel demand forecasting models, PIE may also have applications as a standalone pedestrian planning tool for researchers and practitioners.

Key concepts: Pedestrian, Walkability, Transport engineering, Built environment, Index (typography), Mile, Raster graphics, Scale (ratio)

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