Citation

Vasudevan V, Tiwari A, Chakroborty P. Traffic Injury Prev. 2022; ePub(ePub): ePub.

Copyright

(Copyright © 2022, Informa - Taylor and Francis Group)

DOI

10.1080/15389588.2021.2007527

PMID

35020500

Abstract

OBJECTIVE: Understanding pedestrian road crossing behavior is essential from the perspectives of traffic flow and pedestrian safety. Limited research is available on pedestrian behavior in low- and middle-income countries. The main objective of this study is to understand pedestrian-vehicle interactions during midblock crossings in heterogeneous traffic conditions. Specifically, this study aims to understand whether pedestrians alter their crossing behavior depending on the type of approaching vehicles.

METHODS: To better understand pedestrian road crossing behavior at midblock crossings, an instrumented vehicle collected data from Kanpur, a large city in Uttar Pradesh, India. Because light detection and ranging provides point clouds at high frequency, an algorithm was developed to identify and track vehicles and pedestrians. Specifically, 2 types of interactions at midblock crossings were studied: car-pedestrian and motorized bike-pedestrian. The walking speed profiles and trajectories of the pedestrians were analyzed.

RESULTS: The results show that pedestrians are more willing to engage in risky road crossing behavior in front of motorized bikes than in front of cars. Pedestrian walking speed profiles were unaffected by motorized bikes, but for cars, pedestrians tended to increase their speed in the first half of road crossing and then decrease in the second half.

CONCLUSIONS: Pedestrian crossing speed profiles play an essential role in understanding pedestrian midblock crossing behavior. The speed data for pedestrians at various points of crossing are challenging to capture, but this study shows that LiDAR can be used to capture detailed pedestrian movements. The findings from this study demonstrate the importance of considering vehicle heterogeneity when analyzing pedestrian risk exposure and designing pedestrian crossing facilities.

Language: en

Keywords

LiDAR; naturalistic driving data; Pedestrian midblock crossing; pedestrian walking trajectory; walking speed profile