Citation

Alogaili A, Mannering F. Anal. Meth. Accid. Res. 2022; 33: e100201.

Copyright

(Copyright © 2022, Elsevier Publishing)

DOI

10.1016/j.amar.2021.100201

PMID

unavailable

Abstract

This study explores the differences between day and night pedestrian-injury severities in vehicle-pedestrian crashes over a five-year period using data from the state of Kansas. Separate statistical models (random parameters logit models with possible heterogeneity in the means and variances of the random parameters) were estimated for day and night crashes to examine different pedestrian injury severity outcomes (no visible injury, moderate injury, and severe injury). Likelihood ratio tests were conducted to explore the temporal stability of the model estimations over different times of day and years. Many variables affecting injury severities were considered in model estimation including time and location of accidents, in addition to information on environmental, roadway, crash, vehicle, driver, and pedestrian characteristics. The findings indicate that the factors affecting pedestrian injury severities did change over time but that there is a clear day-night difference in the resulting injury severities of pedestrians, with nighttime crashes consistently resulting in more severe injuries overtime. This suggests policies and technologies that seek to essentially replicate daytime conditions (improved illumination, infrared pedestrian detection in vehicles, etc.) in nighttime conditions could have considerable safety benefits. Using the estimated random parameters models, extensive out-of-sample prediction simulations are used to provide estimates of the potential benefits of such nighttime mitigation policies and technologies, as well as how daytime/nighttime pedestrian injury severity probabilities have been changing over time.

Language: en

Keywords

Out-of-sample prediction; Pedestrian injury; Random parameters logit; Temporal instability