Analysis of the impacts of risk factors on teenage and older driver injury severity using random-parameter ordered probit

Khales, Sina Darban; Kunt, Mehmet Metin; Dimitrijević, B

doi:10.1139/cjce-2019-0394

Cited by 13 publications

(3 citation statements)

References 51 publications

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“…In addition, research was conducted using ordered (probit) random parameters to consider the characteristics of each type of accident, such as vehicle type (truck, general vehicle), accident type (backward collision, single accident-left and right road departure, rollover accident, angle collision), road type (two-lane rural road), and age (elderly and youth) [19][20][21][22][23][24][25][26][27][28]. In existing studies, the random parameter model has been widely applied in analyzing traffic accidents.…”

Section: Literature Reviewmentioning

confidence: 99%

Severity Predictions for Intercity Bus Crashes on Highway Using a Random Parameter Ordered Probit Model

Kim,

Hong

2023

Sustainability

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As intercity buses are a mode that moves large-scale occupancy between regions, it accounts for the mode share-means for mid- to long-distance movement in South Korea. However, the study of intercity bus safety needs to be more extensive, and safety policies are carried out based on traditional probability models without considering the data characteristics of bus accidents. Therefore, in this study, the Random Parameter Ordered Logit model was applied to derive fixed parameter factors that have the same effect on the severity of intercity bus accidents and Random Parameters that consider the heterogeneity of unique attributes by accident. It also analyzed the marginal effect of intercity bus accident severity. As a result of this study, the influencing factors that reflect heterogeneity with random parameters were driver’s condition: drowsiness, vehicle size: medium, crash type: vehicle–pedestrian accident, road condition: wet pavement, and log form of AADT. The random parameter ordered logit model was traditionally found to be more suitable than the ordinal logit model, which only reflects fixed factors and more reliable predictions considering the heterogeneity of accident characteristics for each observation.

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Section: Literature Reviewmentioning

confidence: 99%

Severity Predictions for Intercity Bus Crashes on Highway Using a Random Parameter Ordered Probit Model

Kim,

Hong

2023

Sustainability

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“…Therefore, drivers no longer experience significant changes in driving skill levels as they age. Second, related studies comparing historical driving data for young drivers and experienced drivers also indicate that gender is no longer a significant factor in crash outcomes in experienced driver clusters [51,52].…”

Section: Demographic Characteristicsmentioning

confidence: 99%

Analysis of factors affecting crash under risk scenarios based on driver homogenous clustering

Zheng,

Li,

Ding

et al. 2023

PLoS ONE

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Research on road safety has focused on analyzing the factors that affect crashes. However, previous studies have often neglected differences in crash causation among heterogeneous clusters of drivers. In particular, the differences in the combined effect mechanisms of the factors in the risk scenarios have not been completely explained. Therefore, this study used the K-means algorithm to perform multidimensional feature homogeneous clustering for drivers involved in crashes and near-crashes. Structural equation modeling involving mediating effects was introduced to explore the direct and indirect effects of each influencing factor on vehicle crashes under risk scenarios and compare the differences in crash causation among different driver clusters. The results indicate that the drivers who experienced the risk scenarios can be classified into two homogeneous driver clusters. Significant differences exist in the demographic characteristics, intrinsic driving characteristics, and crash rates between them. In the risk scenario, traffic factors, distraction state, crash avoidance reaction, and maneuver judgment directly affect the crash outcomes of the two cluster drivers. Demographic characteristics and environmental factors have fewer direct influence on the crash outcomes of two-cluster drivers, but produce more complex mediating effects. Analysis of the differences in the influence of factors between clusters indicates that the fundamental cause of crashes for cluster 1 drivers includes poor driving skills. In contrast, cluster 2 drivers’ crashes were more influenced by traffic conditions and their safety awareness. The analysis method of this study can be used to develop more targeted road safety policies to reduce the occurrence of vehicle crashes.

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“…Moreover, the findings of the previous studies [1][2][3] suggest that the driver and vehicle characteristics represent significant factors of crash occurrence and severity of crashes. The data about the drivers and vehicles involved in a crash is collected after the crash occurrence as part of crash investigation and reporting, which is why this data is often referred to as reactive crash data.…”

Section: Introductionmentioning

confidence: 97%

Short-Term Segment-Level Crash Risk Prediction Using Advanced Data Modeling with Proactive and Reactive Crash Data

et al. 2022

Self Cite

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Highway crashes, along with the property damage, personal injuries, and fatalities that they cause, continue to present one of the most significant and critical transportation problems. At the same time, provision of safe travel is one of the main goals of any transportation system. For this reason, both in transportation research and practice much attention has been given to the analysis and modeling of traffic crashes, including the development of models that can be applied to predict crash occurrence and crash severity. In general, such models assess short-term crash risks at a given highway facility, thus providing intelligence that can be used to identify and implement traffic operations strategies for crash mitigation and prevention. This paper presents several crash risk and injury severity assessment models applied at a highway segment level, considering the input data that is typically collected or readily available to most transportation agencies in real-time and at a regional network scale, which would render them readily applicable in practice. The input data included roadway geometry characteristics, traffic flow characteristics, and weather condition data. The paper develops, tests, and compares the performance of models that employ Random effects Bayesian Logistics Regression, Gaussian Naïve Bayes, K-Nearest Neighbor, Random Forest, and Gradient Boosting Machine methods. The paper applies random oversampling examples (ROSE) method to deal with the problem of data imbalance associated with the injury severity analysis. The models were trained and tested using a dataset of 10,155 crashes that occurred on two interstate highways in New Jersey over a two-year period. The paper also analyzes the potential improvement in the prediction abilities of the tested models by adding reactive data to the analysis. To that end, traffic crashes were classified in multiple classes based on the driver age and the vehicle age to assess the impact of these attributes on driver injury severity outcomes. The results of this analysis are promising, showing that the simultaneous use of reactive and proactive data can improve the prediction performance of the presented models.

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Analysis of the impacts of risk factors on teenage and older driver injury severity using random-parameter ordered probit

Cited by 13 publications

References 51 publications

Severity Predictions for Intercity Bus Crashes on Highway Using a Random Parameter Ordered Probit Model

Severity Predictions for Intercity Bus Crashes on Highway Using a Random Parameter Ordered Probit Model

Analysis of factors affecting crash under risk scenarios based on driver homogenous clustering

Short-Term Segment-Level Crash Risk Prediction Using Advanced Data Modeling with Proactive and Reactive Crash Data

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