A new weighted probabilistic model for analyzing the injury rate in public transport road accidents

Zhang, Han; Yao, Xinpeng; Seong, Jin-Taek; Alshanbari, Huda M.; Albalawi, Olayan

doi:10.1016/j.aej.2024.05.040

Alexandria Engineering Journal

2024

DOI: 10.1016/j.aej.2024.05.040

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A new weighted probabilistic model for analyzing the injury rate in public transport road accidents

Han Zhang,

Xinpeng Yao,

Jin-Taek Seong

et al.

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Analyzing Rear-End Crash Counts on Ohio Interstate Freeways Using Advanced Multilevel Modeling

Almutairi

2024

Systems

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This study presents a new modeling approach for rear-end crash counts on Ohio’s interstate freeways based on a dataset for 2021 that contains 2745 rear-end crashes. The analysis encompasses 20 interstate freeways, comprising 1833 homogeneous segments and extending over approximately 1313 miles. These interstate freeways exhibit varying safety performances, indicating a significant degree of heterogeneity. A unique rear-end crash risk rate was devised for each interstate, capturing diverse risk profiles. Three distinct models were developed: a standard negative binomial model, an uncorrelated two-level negative binomial model, and a correlated two-level negative binomial model. The correlated two-level negative binomial model demonstrated superior fit, as evidenced by the likelihood ratio test, Akaike information criterion, and Bayesian information criterion. The correlated two-level negative binomial model exhibited enhanced forecasting precision, as measured by the Root Mean Square Error. A significant finding is that the rear-end crash risk rate significantly improves the fit of the models. The study also reveals that rear-end crashes are expected to occur more frequently in urban segments of interstate freeways with high rear-end risk rates. However, rural segments experience no such significant variations in the rear-end crash risk rate. However, an increase in the inner shoulder width is associated with a decrease in expected rear-end crashes. This research offers a valuable methodology for modeling rear-end crashes on interstate freeways, providing insights into the contributing variables that could inform targeted safety improvements.

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Analyzing Rear-End Crash Counts on Ohio Interstate Freeways Using Advanced Multilevel Modeling

Almutairi

2024

Systems

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A new weighted probabilistic model for analyzing the injury rate in public transport road accidents

Cited by 1 publication

References 24 publications

Analyzing Rear-End Crash Counts on Ohio Interstate Freeways Using Advanced Multilevel Modeling

Analyzing Rear-End Crash Counts on Ohio Interstate Freeways Using Advanced Multilevel Modeling

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