Citation

Rimu AJ, Deb S, Islam M, Etminani-Ghasrodashti R, Pande A. Transp. Res. Rec. 2022; 2676(11): 250-261.

Copyright

(Copyright © 2022, Transportation Research Board, National Research Council, National Academy of Sciences USA, Publisher SAGE Publishing)

DOI

10.1177/03611981221092386

PMID

unavailable

Abstract

Vulnerable road users (VRUs, i.e., pedestrians and bicyclists) have seen an alarming rise in fatalities in recent years. School-age pedestrians in lower-income neighborhoods may be particularly at risk. This paper proposes a data-driven safe-systems approach to develop safety countermeasures for areas near elementary schools serving disadvantaged populations. A review of past literature on child-pedestrian training programs confirms that videos, lectures, and website-based training can provide children with vital information to improve their cognitive abilities relevant to walking safely. However, to improve pedestrian behavior on the road, children need to be safely exposed to traffic environments and practice interactions with traffic. Therefore, the use of virtual reality (VR) is recommended as a platform to introduce children to traffic interactions. Furthermore, the review of existing child-pedestrian training programs showed that most existing training programs (VR-based or otherwise) have an ad-hoc selection of roadway and traffic environment scenarios. Moreover, none of the training programs are designed to address safety issues faced by children in low-income neighborhoods specifically. To address these issues, this study gathered and analyzed crash data for VRUs around schools located in two metropolitan areas: Dallas County in TX and Tampa Bay in FL. Analysis of crash data identified the most prominent factors leading to most crashes as well as disproportionately more severe crashes. A VR-based child-pedestrian training program that involves children interacting with designed environments grounded in local crash data from disadvantaged areas is recommended for a more effective and equitable training platform.

Language: en