Jointly analyzing freeway primary and secondary crash severity using a copula-based approach

Huang, Helai; Ding, Xizhi; Chen, Yuan; Liu, Xinyuan; Tang, Jinjun

doi:10.1016/j.aap.2022.106911

Cited by 7 publications

(2 citation statements)

References 43 publications

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“…Also, they indicate that occupancy, time gap between two crashes, number of lane changes, and the number of lanes is associated with the severity of secondary crashes. Huang et al [15] investigated the crash severities by simultaneously modeling primary crashes and secondary crashes and examining how traffic changes resulting from primary crashes affect the severity of secondary crashes. According to their results, speed variations and traffic conditions resulting from a primary crash have an impact on the severity of secondary crashes.…”

Section: Definition and Background Of Pu Crashmentioning

confidence: 99%

“…However, these measures do not precisely reflect the traffic conditions immediately before the occurrence of crashes and may lead to biased results [18]. Recent studies have emphasized the investigation of the impact of traffic dynamics (traffic changes resulting from primary crashes) on the severity of secondary crashes and the probability of their occurrence [14,15,19,20]. Additionally, considering real-time traffic characteristics enables the identification of more accurate traffic situations leading to secondary crashes [13,21].…”

Section: Importance Of Real-time Traffic Characteristics In Safetymentioning

confidence: 99%

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Analyzing Pile-Up Crash Severity: Insights from Real-Time Traffic and Environmental Factors Using Ensemble Machine Learning and Shapley Additive Explanations Method

Samerei,

Aghabayk,

Montella

2024

Safety

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Pile-up (PU) crashes, which involve multiple collisions between more than two vehicles within a brief timeframe, carry substantial consequences, including fatalities and significant damages. This study aims to investigate the real-time traffic, environmental, and crash characteristics and their interactions in terms of their contributions to severe PU crashes, which have been understudied. This study investigates and interprets the effects of Total Volume/Capacity (TV/C), “Heavy Vehicles Volume/Total Volume” (HVV/TV), and average speed. For this purpose, the PU crash severity was modelled and interpreted using the crash and real-time traffic data of Iran’s freeways over a 5-year period. Among six machine learning methods, the CatBoost model demonstrated superior performance, interpreted via the SHAP method. The results indicate that avg.speed > 90 km/h, TV/C < 0.6, HVV/TV ≥ 0.1, horizontal curves, longitudinal grades, nighttime, and the involvement of heavy vehicles are associated with the risk of severe PU crashes. Additionally, several interactions are associated with severe PU crashes, including the co-occurrence of TV/C ≈ 0.1, HVV/TV ≥ 0.25, and nighttime; the interactions between TV/C ≈ 0.1 or 0.45, HVV/TV ≥ 0.25, and avg.speed > 90 km/h; horizontal curves and high average speeds; horizontal curves; and nighttime. Overall, this research provides essential insights into traffic and environmental factors driving severe PU crashes, supporting informed decision-making for policymakers.

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Section: Definition and Background Of Pu Crashmentioning

confidence: 99%

Section: Importance Of Real-time Traffic Characteristics In Safetymentioning

confidence: 99%

Analyzing Pile-Up Crash Severity: Insights from Real-Time Traffic and Environmental Factors Using Ensemble Machine Learning and Shapley Additive Explanations Method

Samerei,

Aghabayk,

Montella

2024

Safety

View full text Add to dashboard Cite

show abstract

Jointly modeling the dependence of injury severity and crash size involved in motorcycle crashes in Cambodia using a copula-based approach

Li,

Virakvichetra,

Zhang

et al. 2024

Front. Eng. Manag.

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From sky to road: Incorporating the satellite imagery into analysis of freight truck-related crash factors

Yu,

Hua,

Yang

et al. 2024

Accident Analysis & Prevention

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Jointly analyzing freeway primary and secondary crash severity using a copula-based approach

Cited by 7 publications

References 43 publications

Analyzing Pile-Up Crash Severity: Insights from Real-Time Traffic and Environmental Factors Using Ensemble Machine Learning and Shapley Additive Explanations Method

Analyzing Pile-Up Crash Severity: Insights from Real-Time Traffic and Environmental Factors Using Ensemble Machine Learning and Shapley Additive Explanations Method

Jointly modeling the dependence of injury severity and crash size involved in motorcycle crashes in Cambodia using a copula-based approach

From sky to road: Incorporating the satellite imagery into analysis of freight truck-related crash factors

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