Citation

Lin PS, Guo R, Bialkowska-Jelinska E, Kourtellis A, Zhang Y. J. Traffic Transp. Eng. Engl. Ed. 2019; 6(2): 162-174.

Copyright

(Copyright © 2019, Periodical Offices of Chang'an University, Publisher Elsevier Publishing)

DOI

10.1016/j.jtte.2019.02.001

PMID

unavailable

Abstract

In recent years, many departments of transportation in the US have invested more resources to enhance pedestrian safety. However, there is still a need to effectively and systematically address pedestrian safety issues in low-income areas. Statistics show that pedestrians are fatally injured at disproportionately higher rates in the nation's poorer neighborhoods. Low-income areas often are sectioned by high-volume/high-speed arterials, which compound the problem. In this study, pedestrian-vehicle crash data in low-income areas were analyzed from two aspects: how demographic factors, road environment, and land use types influence pedestrian crash occurrence (based on frequency data) and how demographic and other factors influence severity in a pedestrian crash (based on severity data). The crash frequency modeling results show that major influential variables for higher pedestrian crash frequency include four demographic factors (proportions of older adults, commuters using public transit or biking, people with low education level, and zero-car ownership), three road environmental factors (densities of traffic signals and bus stops and proportion of higher-speed roads), and three land use factors (densities of discount stores, convenience stores, and fast-food restaurants). The injury severity modeling results show that a dark-not lighted condition is the most influential variable for severe injury pedestrian crashes, and the number of impaired pedestrians and aggressive drivers also greatly increases the probability of severe injury. Based on the analysis results, this paper makes specific recommendations for both engineering countermeasures and pedestrian safety education/outreach plans that resonate with a given area's demographics to effectively improve pedestrian safety in low-income areas.

Language: en

Keywords

Countermeasures; Factors; Low-income area; Pedestrian crash