Investigating the spatial heterogeneity of factors influencing speeding-related crash severities using correlated random parameter order models with heterogeneity-in-means

Yuan, Renteng; Ding, Shengxuan; Fang, Zhiheng; Gu, Xin; Xiang, Qiaojun

doi:10.1080/19427867.2023.2262201

Cited by 6 publications

(1 citation statement)

References 82 publications

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“…We examined three different distribution types for these variables: normal, lognormal, and uniform. Our ndings showed that the normal distribution achieved the optimal statistical t. This outcome aligns with the ndings of prior research [34][35][36] , reinforcing the choice to model random coe cients following a normal distribution across individuals. To estimate the coe cients for the random parameters, we…”

Section: Modelsupporting

confidence: 86%

Analyzing Injury Severity of School Bus Crashes considering Temporal and Spatial Heterogeneity Using Random Parameters Ordered Probit Model

Rao,

Yuan,

Ding

et al. 2024

Preprint

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Ensuring the safety of students is a multifaceted task, extending beyond the mere design of vehicles. It involves analyzing crash data related to school buses, which plays a critical role in preventing injuries and fatalities of children both in and around these vehicles. The National Highway Traffic Safety Administration 1 gathers comprehensive data on a wide range of school bus-related crashes, from minor incidents to serious and fatal crashes. The school bus crash data not only educates parents and caregivers but also supports various prevention campaigns. The objective of this study is to investigate the different risk factors that influence the severity of injuries in school bus crashes, considering variations in time and location. To achieve this, a Random thresholds random parameters ordered probit (RPOP) model is used, which is particularly effective in identifying and accounting for unobserved variables that might affect the outcomes. Moreover, the model results suggest that springtime and highway locations significantly impact crash outcomes, considering crash details, vehicle information, and driver-passenger characteristics. These findings enhance our comprehension of the diverse factors that contribute to school bus crashes. This improved understanding aids in the development of effective strategies to reduce the severity of injuries resulting from such crashes.

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Section: Modelsupporting

confidence: 86%

Analyzing Injury Severity of School Bus Crashes considering Temporal and Spatial Heterogeneity Using Random Parameters Ordered Probit Model

Rao,

Yuan,

Ding

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

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A study on diversion behavior in weaving segments: Individualized traffic conflict prediction and causal mechanism analysis

Yuan,

Abdel-Aty,

Xiang

2024

Accident Analysis & Prevention

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A matched case-control analysis of autonomous vs human-driven vehicle accidents

Abdel-Aty,

Ding

2024

Nat Commun

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Despite the recent advancements that Autonomous Vehicles have shown in their potential to improve safety and operation, considering differences between Autonomous Vehicles and Human-Driven Vehicles in accidents remain unidentified due to the scarcity of real-world Autonomous Vehicles accident data. We investigated the difference in accident occurrence between Autonomous Vehicles’ levels and Human-Driven Vehicles by utilizing 2100 Advanced Driving Systems and Advanced Driver Assistance Systems and 35,113 Human-Driven Vehicles accident data. A matched case-control design was conducted to investigate the differential characteristics involving Autonomous’ versus Human-Driven Vehicles’ accidents. The analysis suggests that accidents of vehicles equipped with Advanced Driving Systems generally have a lower chance of occurring than Human-Driven Vehicles in most of the similar accident scenarios. However, accidents involving Advanced Driving Systems occur more frequently than Human-Driven Vehicle accidents under dawn/dusk or turning conditions, which is 5.25 and 1.98 times higher, respectively. Our research reveals the accident risk disparities between Autonomous Vehicles and Human-Driven Vehicles, informing future development in Autonomous technology and safety enhancements.

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Investigating the spatial heterogeneity of factors influencing speeding-related crash severities using correlated random parameter order models with heterogeneity-in-means

Cited by 6 publications

References 82 publications

Analyzing Injury Severity of School Bus Crashes considering Temporal and Spatial Heterogeneity Using Random Parameters Ordered Probit Model

Analyzing Injury Severity of School Bus Crashes considering Temporal and Spatial Heterogeneity Using Random Parameters Ordered Probit Model

A study on diversion behavior in weaving segments: Individualized traffic conflict prediction and causal mechanism analysis

A matched case-control analysis of autonomous vs human-driven vehicle accidents

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