A Random Parameters Ordered Probit Analysis of Injury Severity in Truck Involved Rear-End Collisions

Shao, Xiaojun; Ma, Xiaoxiang; Chen, Feng; Song, Mingtao; Pan, Xiaodong; You, Kesi

doi:10.3390/ijerph17020395

Cited by 43 publications

(23 citation statements)

References 42 publications

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“…The explanatory variables shown in Appendix A were classified into four categories: driver characteristics, road characteristics, crash characteristics, and occupant characteristics. Factors such as physical condition, helmet, and vehicle violation were found significant on injury-severity level according to some previous research [ 4 , 5 , 6 , 7 , 8 ]. Due to the high account of missing values, they were not considered in this study.…”

Section: Methodssupporting

confidence: 53%

“…The increase in probability for fatalities was linked with a wide range of variables (i.e., driver’s education, presence of curves on roadways, and high-speed limit). Shao et al [ 7 ] analyzed the influence of variables related to injury severity in truck-involved rear-end crashes. To analyze data between 2006 and 2015 from the United States for both SV and MV crashes, three random parameters probit models were developed.…”

Section: Introductionmentioning

confidence: 99%

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Determinants and Prediction of Injury Severities in Multi-Vehicle-Involved Crashes

Song

et al. 2021

IJERPH

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Multi-vehicle (MV) crashes, which can lead to great damages to society, have always been a serious issue for traffic safety. A further understanding of crash severity can help transportation engineers identify the critical reasons and find effective countermeasures to improve transportation safety. However, studies involving methods of machine learning to predict the possibility of injury-severity of MV crashes are rarely seen. Besides that, previous studies have rarely taken temporal stability into consideration in MV crashes. To bridge these knowledge gaps, two kinds of models: random parameters logit model (RPL), with heterogeneities in the means and variances, and Random Forest (RF) were employed in this research to identify the critical contributing factors and to predict the possibility of MV injury-severity. Three-year (2016–2018) MV data from Washington, United States, extracted from the Highway Safety Information System (HSIS), were applied for crash injury-severity analysis. In addition, a series of likelihood ratio tests were conducted for temporal stability between different years. Four indicators were employed to measure the prediction performance of the selected models, and four categories of crash-related characteristics were specifically investigated based on the RPL model. The results showed that the machine learning-based models performed better than the statistical models did when taking the overall accuracy as an evaluation indicator. However, the statistical models had a better prediction performance than the machine learning models had considering crash costs. Temporal instabilities were present between 2016 and 2017 MV data. The effect of significant factors was elaborated based on the RPL model with heterogeneities in the means and variances.

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Section: Methodssupporting

confidence: 53%

Section: Introductionmentioning

confidence: 99%

Determinants and Prediction of Injury Severities in Multi-Vehicle-Involved Crashes

Song

et al. 2021

IJERPH

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“…Some studies [ 31 , 32 ] revealed that among various types of traffic crashes, rear-end collision has become one of the leading causes of human injuries and fatalities together with massive property damage. Drivers’ injury severity, one of the main concerns of the rear-end crash, is influenced by many factors related to drivers’ age, gender, vehicle, crash position, airbag use, seat belt, time, two way traffic, light condition, and alcohol or drug use [ 32 ].…”

Section: Introductionmentioning

confidence: 99%

“…A similar study also investigated the differences of contributing factors to injury severity between car-strike-truck and truck-strike-car in rear-end collisions [ 31 ]. The results of their study reported that a significant difference between car-strike-truck and truck-strike-car crashes in terms of contributing factors (e.g., age group, trailing units, drinking driving, road surface and critical events) towards injury severity.…”

Section: Introductionmentioning

confidence: 99%

Mobile phone use intention while driving among public service vehicle drivers: Magnitude and its social and cognitive determinants

Eijigu

2021

PLoS ONE

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Despite its risks for accident and illegality, little is known about the magnitude and associated social and cognitive factors that motivate drivers to use mobile phone while driving. The present study, guided by theory of planned behavior, aimed at describing the magnitude of mobile use while driving and examining the role of attitudes, subjective norms, perceived behavioral control, and risk perceptions in predicting drivers’ intentions to use mobile phone while driving. A total of 155 public service vehicle drivers, who were selected from Debre Markos Town and its vehicle terminal took part in the study. To select study participants, systematic random sampling technique was employed. The instrument used to collect data was self-report questionnaire. The results indicated that more than two-third (69%) of the participants used their mobile phone while driving over the past week. Hierarchical regression analysis displayed that perceived behavioral control, risk perception, and attitude were found to be the most significant social and cognitive predictors of public service vehicle drivers’ intention to use mobile phone while driving, but not age and subjective norm variables. So as to reduce drivers’ intention to use mobile phone while driving, intervention strategies should focus on enhancing their confidence to avoid this behavior; alerting drivers to the traffic control regulation and the dangers of using mobile phones while driving.

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“…Jamal, et al [ 46 ] studied the vehicular crash severity in Saudi Arabia and identified that a sudden deviation from the lane of the at-fault vehicle caused a higher crash severity. Shao, et al [ 47 ] applied a RP ordered logit model to prove that a sudden stop of the front vehicle in the same lane significantly raised the injury severity of rear-end crashes with trucks involved. Hence, the severity of the primary crash in a CC series can be affected by the hazardous lane-related behaviors, such as a sudden lane change or a drastic change in speed, of the front vehicle(s), and a wider road with more numbers of lanes provides an wider space to avoid a fierce collision under such circumstances.…”

Section: Discussionmentioning

confidence: 99%

Influential Factors Associated with Consecutive Crash Severity: A Two-Level Logistic Modeling Approach

Meng

Song

et al. 2020

IJERPH

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A consecutive crash series is composed by a primary crash and one or more subsequent secondary crashes that occur immediately within a certain distance. The crash mechanism of a consecutive crash series is distinctive, as it is different from common primary and secondary crashes mainly caused by queuing effects and chain-reaction crashes that involve multiple collisions in one crash. It commonly affects a large area of road space and possibly causes congestions and significant delays in evacuation and clearance. This study identified the influential factors determining the severity of primary and secondary crashes in a consecutive crash series. Basic, random-effects, random-parameters, and two-level binary logistic regression models were established based on crash data collected on the freeway network of Guizhou Province, China in 2018, of which 349 were identified as consecutive crashes. According to the model performance metrics, the two-level logistic model outperformed the other three models. On the crash level, double-vehicle primary crash had a negative association with the severity of secondary consecutive crashes, and the involvement of trucks in the secondary consecutive crash had a positive contribution to its crash severity. On a road segment level, speed limit, traffic volume, tunnel, and extreme weather conditions such as rainy and cloudy days had positive effects on consecutive crash severity, while the number of lanes was negatively associated with consecutive crash severity. Policy suggestions are made to alleviate the severity of consecutive crashes by reminding the drivers with real-time potential hazards of severe consecutive crashes and providing educative programs to specific groups of drivers.

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A Random Parameters Ordered Probit Analysis of Injury Severity in Truck Involved Rear-End Collisions

Cited by 43 publications

References 42 publications

Determinants and Prediction of Injury Severities in Multi-Vehicle-Involved Crashes

Determinants and Prediction of Injury Severities in Multi-Vehicle-Involved Crashes

Mobile phone use intention while driving among public service vehicle drivers: Magnitude and its social and cognitive determinants

Influential Factors Associated with Consecutive Crash Severity: A Two-Level Logistic Modeling Approach

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