Characterizing the differences of injury severity between single-vehicle and multi-vehicle crashes in China

Ma, Jingfeng; Ren, Gang; Li, Haojie; Wang, Shunchao; Yu, Jingcai

doi:10.1080/19439962.2022.2056931

Cited by 8 publications

(4 citation statements)

References 57 publications

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“…The mechanism of crashes varies based on the number of vehicles involved [5], including single-vehicle (SV) and multi-vehicle (MV) crashes, which are usually investigated separately due to their different mechanisms and suitable countermeasures [6][7][8][9]. PU crashes, also referred to as chain-reaction crashes, occur when three or more vehicles are involved in a series of successive collisions with each other in a short period of time, making them a distinct type of multi-vehicle (MV) crash.…”

Section: Definition and Background Of Pu Crashmentioning

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

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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%

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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“…Wu et al (2014) [2], Rezapour et al (2018) [24], Ma et al (2023) [25] Road barrier [26], Russo & Savolainen (2018) [27], Molan et al (2020) [28] The following six factors have been identified as potential influential factors [29].…”

Section: Comparisonmentioning

confidence: 99%

“…Several studies have compared single and multi-vehicle crashes, such as those by Wu et al (2014) and Rezapour et al (2018) [2,24]. Ma et al (2023) found that time, road, speed, lighting, and weather correlate positively with single-vehicle crash injury severity but negatively with multi-vehicle crash injury severity [25]. Factors such as area, location, and angle are significant only for single-vehicle crashes, whereas the day, interference, and wind are significant only for multi-vehicle crashes.…”

Section: Comparisonmentioning

confidence: 99%

Impact Analysis of Road Infrastructure and Traffic Control on Injury Severity of Single- and Multi-vehicle Crashes

Tanishita¹,

Sekiguchi²

2023

Preprint

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Single and multi-vehicle crashes are a significant issue that causes economic and social costs and has therefore gained attention. This study explored the factors associated with injury severity for both single- and multi-vehicle crashes using over 550,000 crash data points in Japan between 2019 and 2021. We identified the determinants of road infrastructure and traffic control while controlling for driver, vehicle, environmental, and accident characteristics by applying ordered logit and bias-reduced binomial regression models. Our findings are as follows. 1) Traffic control variables did not affect single-vehicle crashes. 2) Guardrails had a higher severity in both single-vehicle crashes and multi-vehicle crashes at intersections. 3) The impact of the centerline differed between intersections and non-intersections for multi-vehicle crashes. These results of this study provide transportation agencies with important guidance as to the road infrastructure and transport control.

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“…Several studies have compared single and multi-vehicle crashes, such as those by Wu et al (2014) and Rezapour et al (2018) [3,25]. Ma et al (2023) found that time, road, speed, lighting, and weather correlate positively with single-vehicle crash injury severity but negatively with multi-vehicle crash injury severity [26]. Factors such as area, location, and angle are significant only for single-vehicle crashes, whereas the day, interference, and wind are significant only for multi-vehicle crashes.…”

Section: Conditionsmentioning

confidence: 99%

Impact Analysis of Road Infrastructure and Traffic Control on Injury Severity of Single- and Multi-Vehicle Crashes

Tanishita,

Sekiguchi

2023

Sustainability

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Single- and multi-vehicle crashes are a significant issue that has economic and social costs and has therefore gained attention. This study explored the factors associated with injury severity for both single- and multi-vehicle crashes using over 550,000 crash data in Japan from 2019 to 2021. The determinants of road infrastructure and traffic control were identified while considering driver, vehicle, environmental, and accident characteristics, using ordered logit and bias-reduced binomial regression models. Our findings are as follows. Traffic control variables had no significant effect on the injury of single-vehicle crashes. Guardrails were associated with higher severity in both single-vehicle and multi-vehicle crashes at intersections. The impact of the centerline varied between intersections and non-intersections for multi-vehicle crashes. The results of this study provide transportation agencies with important guidance for road infrastructure and transport control.

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Characterizing the differences of injury severity between single-vehicle and multi-vehicle crashes in China

Cited by 8 publications

References 57 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

Impact Analysis of Road Infrastructure and Traffic Control on Injury Severity of Single- and Multi-vehicle Crashes

Impact Analysis of Road Infrastructure and Traffic Control on Injury Severity of Single- and Multi-Vehicle Crashes

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