To explore the relationship between rear-end crash risk and its influencing factors, on-road experiments were conducted for measuring the individual vehicle trajectory data associated with novice and experienced drive...To explore the relationship between rear-end crash risk and its influencing factors, on-road experiments were conducted for measuring the individual vehicle trajectory data associated with novice and experienced drivers. The rear-end crash potential probability based on the time to collision was proposed to represent the interpretation of rear-end crash risk.One-way analysis of variance was applied to compare the rearend crash risks for novice and experienced drivers. The rearend crash risk models for novice and experienced drivers were respectively developed to identify the effects of contributing factors on the driver rear-end crash risk. Also, the cumulative residual method was used to examine the goodness-of-fit of models. The results show that there is a significant difference in rear-end risk between the novice and experienced drivers.For the novice drivers, three risk factors including the traffic volume, the number of lanes and gender are found to significantly impact on the rear-end crash risk, while significant impact factors for experienced drivers are the vehicle speed and traffic volume. The rear-end crash risk models perform well based on the existing limited data samples.展开更多
This study develops new real-time freeway rear-end crash potential predictors using support vector machine(SVM) technique. The relationship between rear-end crash occurrences and traffic conditions were explored using...This study develops new real-time freeway rear-end crash potential predictors using support vector machine(SVM) technique. The relationship between rear-end crash occurrences and traffic conditions were explored using historical loop detector data from Interstate-894 in Milwaukee, Wisconsin, USA. The extracted loop detection data were aggregated over different stations and time intervals to produce explanatory features. A feature selection process, which addresses the interaction between SVM classifiers and explanatory features, was adopted to identify the features that significantly influence rear-end crashes. Afterwards, the identified significant explanatory features over three separate time levels were used to train three SVM models. In the end, the multi-layer perceptron(MLP) artificial neural network models were used as benchmarks to evaluate the performance of SVM models. The results show that the proposed feature selection procedure greatly enhances the accuracy and generalization capability of SVM models. Moreover, the optimal SVM classifier achieves 81.1% overall prediction precision rate. In comparison with MLP artificial neural networks, SVM models provide better results in terms of crash prediction accuracy and false positive rate, which confirms the superior performance of SVM technique in rear-end crash potential prediction analysis.展开更多
To improve the passive safety of high-speed trains,it is very important to understand the mechanism of head injury in high-speed train collisions.In this study,the head injury mechanisms of occupants in high-speed tra...To improve the passive safety of high-speed trains,it is very important to understand the mechanism of head injury in high-speed train collisions.In this study,the head injury mechanisms of occupants in high-speed train rear-end collisions were investigated based on the occupant-seat coupling model,which included a dummy representing the Chinese 50th percentile adult male.The typical injury responses in terms of skull fractures,brain contusions,and diffuse axonal injury(DAI)were analyzed.Meanwhile,the influences of collision speed and seat parameters on head injury response were examined.The simulation results indicate that the skull fractures primarily occur at the skull base region due to excessive neck extension,while the brain contusions and DAI result from the relative displacement of different brain regions.The increase in collision speed will promote the probability of skull fracture,brain contusion,and DAI.Seat design modifications,such as reduced seat spacing,increased seat backrest angles,and selecting the appropriate cushion angle(76°)and friction coefficient(0.15),can effectively mitigate probably occupant's head injury.展开更多
Rear-end crashes are among the most common crash types at signalized intersections. To examine the risk factors for the occurrence of this crash type, this study involved the analysis of nine years of intersection cra...Rear-end crashes are among the most common crash types at signalized intersections. To examine the risk factors for the occurrence of this crash type, this study involved the analysis of nine years of intersection crash records in the state of Wyoming. With that, the contributing factors related to crash, driver, environmental, and roadway characteristics, including pavement surface friction, were investigated. A binomial logistic regression modeling approach was applied to achieve the study’s objective. The results showed that three factors related to crash and driver’s attributes (commercial vehicle involvement, speeding, and driver’s age) and four factors related to environmental and roadway characteristics (lighting, weather conditions, area type, whether urban or rural and pavement friction) are associated with the risk of rear-end crash occurrence at signalized intersections. This study provides insights into the mitigation measures to implement concerning rear-end crashes at signalized intersections.展开更多
The aim of this study is to identify factors that affect injury severity levels of work zone rear-end crashes with high collision speeds(P35 miles per hour(mph,1 mph equals about 1.609344 km/h)).Using statewide crash ...The aim of this study is to identify factors that affect injury severity levels of work zone rear-end crashes with high collision speeds(P35 miles per hour(mph,1 mph equals about 1.609344 km/h)).Using statewide crash data provided by the South Carolina Department of Transportation from 2014 to 2020,a mixed binary logit model with heterogeneity in mean and variance is estimated.The model’s outcome variable is injury or non-injury(i.e.,property damage only),and the explanatory variables include information related to vehicle,collision,time,occupant,roadway,and environmental characteristics.The estima-tion results show that the interstate variable is best modeled as a random parameter at a 90%confidence level.Late-night and dawn/dusk conditions influence the mean effect,while driving under the influence affects the variance of the random parameter.Factors positively influencing injury severity include multi-vehicle involvement,airbag deploy-ment,dark conditions,and truck-involved crashes.Conversely,advanced warning area,activity area,lane shift/crossover,young and middle-aged drivers,and dawn/dusk condi-tions have negative effects on injury severity.展开更多
Vehicles involved in traffic accidents generally experience divergent vehicle motion,which causes severe damage.This paper presents a self-learning drift-control method for the purpose of stabilizing a vehicle’s yaw ...Vehicles involved in traffic accidents generally experience divergent vehicle motion,which causes severe damage.This paper presents a self-learning drift-control method for the purpose of stabilizing a vehicle’s yaw motions after a high-speed rear-end collision.The struck vehicle generally experiences substantial drifting and/or spinning after the collision,which is beyond the handling limit and difficult to control.Drift control of the struck vehicle along the original lane was investigated.The rear-end collision was treated as a set of impact forces,and the three-dimensional non-linear dynamic responses of the vehicle were considered in the drift control.A multi-layer perception neural network was trained as a deterministic control policy using the actor-critic reinforcement learning framework.The control policy was iteratively updated,initiating from a random parameterized policy.The results show that the self-learning controller gained the ability to eliminate unstable vehicle motion after data-driven training of about 60,000 iterations.The controlled struck vehicle was also able to drift back to its original lane in a variety of rear-end collision scenarios,which could significantly reduce the risk of a second collision in traffic.展开更多
Purpose： Rear-end crashes attribute to a large portion of total crashes in China, which lead to many casualties and property damage, especially when involving commercial vehicles. This paper aims to investigate the c...Purpose： Rear-end crashes attribute to a large portion of total crashes in China, which lead to many casualties and property damage, especially when involving commercial vehicles. This paper aims to investigate the critical factors for occupant injury severity in the specific rear-end crash type involving trucks as the front vehicle （W）. Methods： This paper investigated crashes occurred from 2011 to 2013 in Beijing area, China and selected 100 qualified cases i.e., rear-end crashes involving trucks as the FV. The crash data were supplemented with interviews from police officers and vehicle inspection. A binary logistic regression model was used to build the relationship between occupant injury severity and corresponding affecting factors. More- over, a multinomial logistic model was used to predict the likelihood of fatal or severe injury or no injury in a rear-end crash. Results： The results provided insights on the characteristics of driver, vehicle and environment, and the corresponding influences on the likelihood of a rear-end crash. The binary logistic model showed that drivers＇ age, weight difference between vehicles, visibility condition and lane number of road signifi- cantly increased the likelihood for severe injury of rear-end crash. The multinomial logistic model and the average direct pseudo-elasticity of variables showed that night time, weekdays, drivers from other provinces and passenger vehicles as rear vehicles significantly increased the likelihood of rear drivers being fatal. Conclusion： All the abovementioned significant factors should be improved, such as the conditions of lighting and the layout of lanes on roads. Two of the most common driver factors are drivers＇ age and drivers＇ original residence. Young drivers and outsiders have a higher injury severity. Therefore it is imperative to enhance the safety education and management on the young drivers who steer heavy duty truck from other cities to Beiiing on weekdays.展开更多
基金The National Natural Science Foundation of China(No.51478110)
文摘To explore the relationship between rear-end crash risk and its influencing factors, on-road experiments were conducted for measuring the individual vehicle trajectory data associated with novice and experienced drivers. The rear-end crash potential probability based on the time to collision was proposed to represent the interpretation of rear-end crash risk.One-way analysis of variance was applied to compare the rearend crash risks for novice and experienced drivers. The rearend crash risk models for novice and experienced drivers were respectively developed to identify the effects of contributing factors on the driver rear-end crash risk. Also, the cumulative residual method was used to examine the goodness-of-fit of models. The results show that there is a significant difference in rear-end risk between the novice and experienced drivers.For the novice drivers, three risk factors including the traffic volume, the number of lanes and gender are found to significantly impact on the rear-end crash risk, while significant impact factors for experienced drivers are the vehicle speed and traffic volume. The rear-end crash risk models perform well based on the existing limited data samples.
基金Project(BK20160685)supported by the Science Foundation of Jiangsu Province,ChinaProject(61620106002)supported by the National Natural Science Foundation of China
文摘This study develops new real-time freeway rear-end crash potential predictors using support vector machine(SVM) technique. The relationship between rear-end crash occurrences and traffic conditions were explored using historical loop detector data from Interstate-894 in Milwaukee, Wisconsin, USA. The extracted loop detection data were aggregated over different stations and time intervals to produce explanatory features. A feature selection process, which addresses the interaction between SVM classifiers and explanatory features, was adopted to identify the features that significantly influence rear-end crashes. Afterwards, the identified significant explanatory features over three separate time levels were used to train three SVM models. In the end, the multi-layer perceptron(MLP) artificial neural network models were used as benchmarks to evaluate the performance of SVM models. The results show that the proposed feature selection procedure greatly enhances the accuracy and generalization capability of SVM models. Moreover, the optimal SVM classifier achieves 81.1% overall prediction precision rate. In comparison with MLP artificial neural networks, SVM models provide better results in terms of crash prediction accuracy and false positive rate, which confirms the superior performance of SVM technique in rear-end crash potential prediction analysis.
基金supported by the National Natural Science Foundation of China(Grant No.12122211)the Natural Science Foundation of Sichuan Province(Grant No.2022NSFSC0035)。
文摘To improve the passive safety of high-speed trains,it is very important to understand the mechanism of head injury in high-speed train collisions.In this study,the head injury mechanisms of occupants in high-speed train rear-end collisions were investigated based on the occupant-seat coupling model,which included a dummy representing the Chinese 50th percentile adult male.The typical injury responses in terms of skull fractures,brain contusions,and diffuse axonal injury(DAI)were analyzed.Meanwhile,the influences of collision speed and seat parameters on head injury response were examined.The simulation results indicate that the skull fractures primarily occur at the skull base region due to excessive neck extension,while the brain contusions and DAI result from the relative displacement of different brain regions.The increase in collision speed will promote the probability of skull fracture,brain contusion,and DAI.Seat design modifications,such as reduced seat spacing,increased seat backrest angles,and selecting the appropriate cushion angle(76°)and friction coefficient(0.15),can effectively mitigate probably occupant's head injury.
文摘Rear-end crashes are among the most common crash types at signalized intersections. To examine the risk factors for the occurrence of this crash type, this study involved the analysis of nine years of intersection crash records in the state of Wyoming. With that, the contributing factors related to crash, driver, environmental, and roadway characteristics, including pavement surface friction, were investigated. A binomial logistic regression modeling approach was applied to achieve the study’s objective. The results showed that three factors related to crash and driver’s attributes (commercial vehicle involvement, speeding, and driver’s age) and four factors related to environmental and roadway characteristics (lighting, weather conditions, area type, whether urban or rural and pavement friction) are associated with the risk of rear-end crash occurrence at signalized intersections. This study provides insights into the mitigation measures to implement concerning rear-end crashes at signalized intersections.
文摘The aim of this study is to identify factors that affect injury severity levels of work zone rear-end crashes with high collision speeds(P35 miles per hour(mph,1 mph equals about 1.609344 km/h)).Using statewide crash data provided by the South Carolina Department of Transportation from 2014 to 2020,a mixed binary logit model with heterogeneity in mean and variance is estimated.The model’s outcome variable is injury or non-injury(i.e.,property damage only),and the explanatory variables include information related to vehicle,collision,time,occupant,roadway,and environmental characteristics.The estima-tion results show that the interstate variable is best modeled as a random parameter at a 90%confidence level.Late-night and dawn/dusk conditions influence the mean effect,while driving under the influence affects the variance of the random parameter.Factors positively influencing injury severity include multi-vehicle involvement,airbag deploy-ment,dark conditions,and truck-involved crashes.Conversely,advanced warning area,activity area,lane shift/crossover,young and middle-aged drivers,and dawn/dusk condi-tions have negative effects on injury severity.
基金supported by International Science&Technology Cooperation Program of China(Grant No.2019YFE0100200)the National Natural Science Foundation of China(Grant No.51905483).This paper is also partially supported by Toyota.
文摘Vehicles involved in traffic accidents generally experience divergent vehicle motion,which causes severe damage.This paper presents a self-learning drift-control method for the purpose of stabilizing a vehicle’s yaw motions after a high-speed rear-end collision.The struck vehicle generally experiences substantial drifting and/or spinning after the collision,which is beyond the handling limit and difficult to control.Drift control of the struck vehicle along the original lane was investigated.The rear-end collision was treated as a set of impact forces,and the three-dimensional non-linear dynamic responses of the vehicle were considered in the drift control.A multi-layer perception neural network was trained as a deterministic control policy using the actor-critic reinforcement learning framework.The control policy was iteratively updated,initiating from a random parameterized policy.The results show that the self-learning controller gained the ability to eliminate unstable vehicle motion after data-driven training of about 60,000 iterations.The controlled struck vehicle was also able to drift back to its original lane in a variety of rear-end collision scenarios,which could significantly reduce the risk of a second collision in traffic.
文摘Purpose： Rear-end crashes attribute to a large portion of total crashes in China, which lead to many casualties and property damage, especially when involving commercial vehicles. This paper aims to investigate the critical factors for occupant injury severity in the specific rear-end crash type involving trucks as the front vehicle （W）. Methods： This paper investigated crashes occurred from 2011 to 2013 in Beijing area, China and selected 100 qualified cases i.e., rear-end crashes involving trucks as the FV. The crash data were supplemented with interviews from police officers and vehicle inspection. A binary logistic regression model was used to build the relationship between occupant injury severity and corresponding affecting factors. More- over, a multinomial logistic model was used to predict the likelihood of fatal or severe injury or no injury in a rear-end crash. Results： The results provided insights on the characteristics of driver, vehicle and environment, and the corresponding influences on the likelihood of a rear-end crash. The binary logistic model showed that drivers＇ age, weight difference between vehicles, visibility condition and lane number of road signifi- cantly increased the likelihood for severe injury of rear-end crash. The multinomial logistic model and the average direct pseudo-elasticity of variables showed that night time, weekdays, drivers from other provinces and passenger vehicles as rear vehicles significantly increased the likelihood of rear drivers being fatal. Conclusion： All the abovementioned significant factors should be improved, such as the conditions of lighting and the layout of lanes on roads. Two of the most common driver factors are drivers＇ age and drivers＇ original residence. Young drivers and outsiders have a higher injury severity. Therefore it is imperative to enhance the safety education and management on the young drivers who steer heavy duty truck from other cities to Beiiing on weekdays.
