Citation

Le M, Geedipally SR, Fitzpatrick K, Avelar RE. Transp. Res. Rec. 2020; 2674(9): 799-808.

Copyright

(Copyright © 2020, Transportation Research Board, National Research Council, National Academy of Sciences USA, Publisher SAGE Publishing)

DOI

10.1177/0361198120932161

PMID

unavailable

Abstract

Pedestrian fatal crashes in the U.S. have increased over the years. From 2007 to 2016, pedestrian fatalities increased 27% nationally, while all other traffic fatalities decreased 14%. On average, a pedestrian was killed every 1.5 h in traffic crashes in 2016. The Federal Highway Administration (FHWA) has been working with public agencies toward developing more data-driven approaches to identify and mitigate pedestrian safety issues. However, pedestrian exposure to risk is not readily available. The absence of pedestrian exposure data makes it challenging to identify and prioritize high-crash risk locations. Using Dallas, Texas, as a case study, researchers wanted to use exposure in relation to volumes--both vehicular and pedestrian volume--to determine pedestrian risk. Although the vehicular volume is extensively available, the pedestrian volume is seldom available. The objective of this study is to explore options for collecting or estimating pedestrian volume data, particularly at intersections with high pedestrian activity. Researchers successfully developed a direct-demand model that estimates pedestrian volumes at signalized and stop-controlled intersections. The final model showed that pedestrian volume: increases 4 times within downtown; increases 12% per school within 1 mi of intersection; increases 4.8 times per 1% increase in commercial/multi-family residential land uses within 300 ft of intersection; increases 4.7 times with presence of higher education, hospitals, or malls; and decreases 36% per 5 mph increase in the intersections' maximum posted speed limit. This research can help advance pedestrian safety analyses by providing a method of estimating pedestrian volumes for intersections by control type, particularly when volumes are infeasible to measure.

Language: en