A Pedestrian Exposure Model for the California State Highway System

Abstract: 

For this study, we developed one of the first statewide pedestrian exposure models, using log-linear regression to estimate annual pedestrian crossing volumes at intersections on the California State Highway System. We compiled a database of more than 1,200 count locations, one of the largest ever used to create a pedestrian volume mode. We initially evaluated 75 explanatory variables for the model. The final model is based on the three land-use variables (employment density, population density, number of schools), four roadway network variables (number of street segments, intersections with principal arterial and minor arterial roadways, and four-way intersections), and the U.S. Census Bureau’s American Community Survey journey-to-work walk mode share that are readily available or fairly easy to create using basic geographic information system analysis. The resulting pedestrian volume model was used to estimate annual crossing volumes at more than 12,000 intersections along the California State Highway System. This is one of the first statewide pedestrian volume models, and California may represent the largest jurisdiction to date to adopt a single, system-wide pedestrian volume model.

Pedestrian volume data are important for safety analysis because they can be used as a basic measure of exposure at a specific location. For example, the risk of pedestrian crashes for people traveling along state highways can be estimated as the number of pedestrian crashes per million pedestrian crossings. Further, pedestrian volume is a crucial variable to include in safety performance functions because it is one of the strongest predictors of total pedestrian crashes at a given location. By controlling for pedestrian exposure, the remaining safety performance function variables can more accurately identify which roadway design features or other characteristics of a location have the most potential to reduce pedestrian crashes. Volume data can also be used to identify how common pedestrian activity is on the state highway system, indicating the importance of designing roadways for safe and convenient pedestrian access.

It is impractical to count pedestrians at every roadway intersection in a large jurisdiction. For example, California has a 15,000-mi state highway system (SHS). This problem can be addressed by applying statistical models to estimate volumes at specific locations across the entire system.

This paper describes the process to develop a pedestrian exposure model for the California SHS. First, as described in the following section, we conducted a literature review of previous pedestrian models, the methods used, and potential explanatory variables. In the Model Development section, we describe our explanatory variables, count data processing, and the model estimation process. The final section explains how we applied the model to the SHS and summarizes the annual volume estimates.

Publication date: 
April 3, 2019
Publication type: 
Journal Article