Citation

Soto F, Kitali AE, Asif Raihan M, Alluri P. Transp. Res. Rec. 2022; 2676(9): 677-692.

Copyright

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

DOI

10.1177/03611981221088196

PMID

unavailable

Abstract

The objective of this study was to identify pedestrian crash hotspots and assess the built environment to identify factors that increase the probability of a roadway corridor becoming a hotspot. Therefore, this research attempted to identify the pedestrian hotspots and then answer the question, do locations with a ?specific built environment? have a higher probability of becoming a pedestrian crash hotspot? The study used 4 years of pedestrian crash data (2011 to 2014) from Miami-Dade County, one of the top three leading counties in the United States with the highest pedestrian fatalities. Pedestrian crash hotspots were first identified in the ArcGIS environment, integrating spatial analysis. The Bayesian complementary log-log (cloglog) model was then used to develop a hotspot risk prediction model, in which the likelihood of a corridor becoming a hotspot was linked to the built environment, and to demographic and socioeconomic factors. The density of bus stops, shopping centers, healthcare facilities, hotels, alcohol sales establishments, households without vehicles, traffic volume, presence of sidewalks, and presence of medians were found to significantly increase the likelihood of a corridor becoming a pedestrian crash hotspot. The methodological framework and findings of this study could be used while developing site-specific proactive measures to improve pedestrian safety.

Language: en