United States Department of Transportation
i
Translation of Driver-Pedestrian Behavioral Models at Semi-Controlled Crosswalks into a Quantitative Framework for Practical Self-Driving Vehicle Applications, Part B (Pedestrian Volume Analytics)
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2023-11-01
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[PDF-7.99 MB]
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Edition:Final Report Jan 2022 – Dec 2022
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Abstract:Widespread introduction of cameras installed for monitoring vehicle flow at intersections offers opportunities to leverage this infrastructure to acquire insights into the patterns and trends of pedestrian activities at these locations. This can serve as a valuable data source for both human-driven vehicles (HDVs) and connected and autonomous vehicle (CAV) operations. Data from such equipment help establish pedestrian movement performance and timing thresholds, thereby addressing a gap in the literature. The study leverages data from signalized intersection cameras to (1) prescribe durations for pedestrian walk-interval based on pedestrian volume and geometric features of the intersection, (2) investigate the factors that influence pedestrian demand patterns, and (3) predict pedestrian volumes and tie it to signal timing, to enhance service for all roadway users. The first part of the study provides guidance for walk time interval selection and presents four timing categories ranging from negligible to high volume and prescribes pedestrian walk interval time durations (based on the demand per cycle, storage area for pedestrians, and offset of the pedestrian push-button from the crosswalk). The second part of the study describes scalable techniques for pedestrian movement predictions.
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