Julia B. Griswold
Robert J. Schneider
California Department of Transportation (Caltrans).
Publications and Resources:
Griswold, J. B., Medury, A., Schneider, R. J., & Grembek, O. (2018). Comparison of pedestrian count expansion methods: land use groups versus empirical clusters. Transportation research record, 2672(43), 87-97.
Medury, A., Griswold, J. B., Huang, L., & Grembek, O. (2019). Pedestrian Count Expansion Methods: Bridging the Gap between Land Use Groups and Empirical Clusters. Transportation Research Record, 0361198119838266.
Schneider, R.J., L.S. Arnold, and D.R. Ragland. “A Methodology for Counting Pedestrians at Intersections: Using Automated Counters to Extrapolate Weekly Volumes from Short Manual Counts,” Transportation Research Record: Journal of the Transportation Research Board, Volume 2140, pp. 1-12, 2009.
Count expansion methods are a useful tool for creating long-term pedestrian or cyclist volume estimates from short-term counts for safety analysis or planning purposes. Expansion factors can be developed based on the trends from automated counters set up for long periods of time. Evidence has shown that the activity patterns can vary between sites so that there is potential to create more accurate estimates by grouping similar long-term count trends into factor groups. There are two common approaches to developing factor groups in pedestrian and cyclist count expansion studies. The land use classification approach has the advantage of being simple to apply to short-term count locations based on attributes of the surrounding area, but it requires assumptions by the researchers about which characteristics correlate with different activity patterns. Empirical clustering approaches can potentially create more distinct clusters by effectively matching locations with similar patterns, but they do not present an easy way to apply the resulting factor groups to appropriate short-term count sites.
Our research in non-motorized direct demand modeling has necessitated the development of novel expansion factor techniques to maximize the availability of a wide range of count durations. We have conducted research on both land use and clustering-based factor groups. More recently, our work integrates the two approaches and takes advantage of the benefits of both by using objective measures of the surrounding land use to model membership in the empirical cluster groups.