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Journal Article

Citation

Xie M, Cheng W, Gill GS, Zhou J, Jia X, Choi S. Traffic Injury Prev. 2018; 19(2): 207-213.

Affiliation

SOUTHERN CALIFORNIA ASSOCIATION OF GOVERNMENTS , 818 West 7th Street, 12th Floor, Los Angeles , CA 90017.

Copyright

(Copyright © 2018, Informa - Taylor and Francis Group)

DOI

10.1080/15389588.2017.1371302

PMID

28837362

Abstract

OBJECTIVE: Most of the extensive research dedicated to identifying the influential factors of hit-and-run (HR) crashes has utilized the typical Maximum Likelihood Estimation Binary Logit models, and none of them have employed the real-time traffic data. To fill this gap, this study focused on investigating contributing factors of HR crashes, as well as the severity levels of HR.

METHODS: This study analyzed four-year crash and real time loop detector data by employing the hierarchical Bayesian models with random effects within a sequential Logit structure. Along with the evaluation of impact of random effects on model fitness and complexity, the prediction capability of the models was also examined. Stepwise incremental sensitivity and specificity were calculated and ROC (Receiver Operating Characteristic) curve was utilized to graphically illustrate the predictive performance of the model.

RESULTS: Among the real-time flow variables, the average occupancy and speed from upstream detector was observed to be positively correlated with HR crash possibility. The average upstream speed and speed difference of upstream and downstream speed were correlated with the occurrence of severe HR crashes. Apart from real-time factors, the other variables found influential for HR and severe HR crashes were length of segment, adverse weather conditions, dark lighting conditions with malfunctioning street light, driving under influence of alcohol, width of inner shoulder, and night time.

CONCLUSIONS: This study suggests the potential traffic conditions of HR and severe HR occurrence, which refer to relatively congested upstream traffic conditions with high upstream speed and significant speed deviations on long segments. The above findings suggest that traffic enforcement should be directed towards mitigating the risky driving under the aforementioned traffic conditions. Moreover, the enforcement agencies may employ alcohol checkpoints to counter DUI during the night time. As per the engineering improvements, wider inner shoulders may be constructed to potentially reduce HR cases and the street lights should be installed and maintained in working conditions to make the roads less prone to such crashes.


Language: en

Keywords

Bayesian Logit Model with Random Effects; Hit-and-run Crashes; Real-Time Loop Detector Data; Receiver Operating Characteristic; Sequential Logit Structure

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