California SHS Pedestrian Exposure Model
Julia B. Griswold
Robert J. Schneider
California Department of Transportation (Caltrans).
Publications and Resources:
Griswold, J. B., Medury, A., & Schneider, R. J. (2011). Pilot models for estimating bicycle intersection volumes. Transportation research record, 2247(1), 1-7.
Griswold, J. B., Medury, A., Schneider, R. J., Amos, D., Li, A., & Grembek, O. (2019). A Pedestrian Exposure Model for the California State Highway System. Transportation Research Record, 0361198119837235.
Schneider, R.J., L.S. Arnold, and D.R. Ragland. “A Pilot Model for Estimating Pedestrian Intersection Crossing Volumes,” Transportation Research Record: Journal of the Transportation Research Board, Volume 2140, pp. 13-26, 2009.
Schneider, R.J., T. Henry, M.F. Mitman, L. Stonehill, and J. Koehler. “Development and Application of the San Francisco Pedestrian Intersection Volume Model,” Transportation Research Record: Journal of the Transportation Research Board, Volume 2299, pp. 65-78, 2012.
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-mile state highway system (SHS). This problem can be addressed by applying statistical models to estimate volumes at specific locations across the entire system.
Direct Demand Modeling Process
Our research on pedestrian exposure estimation has yielded the development of one of the first statewide pedestrian exposure models. This model was estimated within a direct demand modeling framework. While the scale of this model is unique, including both urban and rural locations across a large state, the significant explanatory variables are consistent with previous models at the city or regional level. A considerable effort went into compiling and processing pedestrian volumes as the dependent variable and testing a large number of potential explanatory variables. Yet, the final model is based on 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 American Community Survey journey-to-work walk mode share that are readily-available or fairly easy to create using basic GIS analysis. We applied the model to more than 12,000 intersections across the California SHS. The estimated annual pedestrian volumes can be used in future Caltrans and local safety studies to better understand the risk to pedestrians on the SHS.
In SafeTREC’s prior research in this realm, we developed direct demand models of pedestrian and bicycle exposure for Alameda County and of pedestrian exposure for San Francisco.