Older drivers and younger drivers are affected differently both in summer and winter. Different factors affect each level of severity differently;some factors </span><span><span>affect a particular l...Older drivers and younger drivers are affected differently both in summer and winter. Different factors affect each level of severity differently;some factors </span><span><span>affect a particular level of injury severity differently from when the same factor is analyzed for another injury severity. The goal of this study is to identify the </span><span>factors that contribute to injury severity among older drivers (65+) and young </span><span>drivers (16</span></span><span> </span><span>-</span><span> </span><span><span>25) considering two seasons namely, summer and winter at intersections. Binary ordered probit models were used to develop four models to identify the contributing factors, two models for each season, namely winter and summer. A statistical t-test has been done to identify the statistically </span><span>significant variables @ 90% confidence interval. Based on the developed models, </span><span>in summer, three contributing factors, driving too fast condition, rear-end crashes, and followed too close are associated with younger drivers injury severity, while two contributing factors, rear-end crashes and followed too close are associated with older drivers injury severity. In winter, five factors</span></span><span>,</span><span><span> made an improper turn, E Failed to Yield Right-of-Way from Traffic Signal, rear </span><span>end (front to rear), gender like male and lighting condition like dark and dusk</span><span> light condition</span></span><span>,</span><span> are associated with younger drivers injury severity, while three factors such as made improper turn, rear-end crashes, and followed too close are associated with older drivers injury severity. Contributing factors in summer are the same for both younger and older drivers, but different in winter for both younger and older drivers. This indicates that older drivers and younger drivers are affected differently both in summer and winter.展开更多
Many studies suggest that more crashes occur due to mixed traffic flow at unsignalized intersections. However, very little is known about the injury severity of these crashes. The objective of this study is therefore ...Many studies suggest that more crashes occur due to mixed traffic flow at unsignalized intersections. However, very little is known about the injury severity of these crashes. The objective of this study is therefore to investigate how contributory factors affect crash injury severity at unsignalized intersections. The dataset used for this analysis derived from police crash reports from Dec. 2006 to Apr. 2009 in Heilongjiang Province, China. An ordered probit model was developed to predict the probability that the injury severity of a crash will be one of four levels ： no injury, slight injury, severe injury, and fatal injury. The injury severity of a crash was evaluated in terms of the most severe injury sustained by any person involved in the crash. Results from the present study showed that different factors had varying effects on crash injury severity. Factors found to result in the increased probability of serious injuries include adverse weather, sideswiping with pedestrians on poor surface, the interaction of rear-ends and the third-class highway, winter night without illumination, and the interaction between traffic signs or markings and the third-class highway. Although there are some limitations in the current study, this study provides more insights into crash injury severity at unsignalized intersections.展开更多
Despite the importance of heavy vehicles in Australia’s transportation system,little is known on the factors influencing injury severity from accidents involving a single heavy vehicle.Heavy vehicular crashes have be...Despite the importance of heavy vehicles in Australia’s transportation system,little is known on the factors influencing injury severity from accidents involving a single heavy vehicle.Heavy vehicular crashes have been one of the main causes of fatal injuries in Australia,and this raises safety concerns for transport authorities,insurance companies,and emergency services.Although there have been several potential attempts to identify the factors contributing to heavy vehicle crashes and injury severity,it is still necessary to reduce the number of traffic crashes and lower the fatality rate involving heavy vehicles.The aims of this study were investigating the effects of heavy trucks’presence in accidents on the injury severity level sustained by the vehicle driver and detecting the contributing factors that lead to specific injury severity levels.Fixed-and random-parameter ordered probit and logit models were applied for predicting the likelihood of three injury severity categories severe,moderate,and no injury based on data from crashes caused by heavy trucks in Victoria,Australia in 2012-2017.The results showed that the random-parameter ordered probit model performed better than the other models did.Twenty variables(i.e.,factors)were found to be significant,and 12 of them were found to have random parameters that were normally distributed.Since some of the investigated factors had different effects on the type of injury severity in Australia,this paper does not recommend generalizing the findings from other case studies.Based on the findings,Victoria state authorities can have insight and enhanced understanding of the specific factors that lead to various types of injury severity involving heavy trucks.Consequently,the safety of all road users,including heavy vehicle drivers,can be enhanced.展开更多
文摘Older drivers and younger drivers are affected differently both in summer and winter. Different factors affect each level of severity differently;some factors </span><span><span>affect a particular level of injury severity differently from when the same factor is analyzed for another injury severity. The goal of this study is to identify the </span><span>factors that contribute to injury severity among older drivers (65+) and young </span><span>drivers (16</span></span><span> </span><span>-</span><span> </span><span><span>25) considering two seasons namely, summer and winter at intersections. Binary ordered probit models were used to develop four models to identify the contributing factors, two models for each season, namely winter and summer. A statistical t-test has been done to identify the statistically </span><span>significant variables @ 90% confidence interval. Based on the developed models, </span><span>in summer, three contributing factors, driving too fast condition, rear-end crashes, and followed too close are associated with younger drivers injury severity, while two contributing factors, rear-end crashes and followed too close are associated with older drivers injury severity. In winter, five factors</span></span><span>,</span><span><span> made an improper turn, E Failed to Yield Right-of-Way from Traffic Signal, rear </span><span>end (front to rear), gender like male and lighting condition like dark and dusk</span><span> light condition</span></span><span>,</span><span> are associated with younger drivers injury severity, while three factors such as made improper turn, rear-end crashes, and followed too close are associated with older drivers injury severity. Contributing factors in summer are the same for both younger and older drivers, but different in winter for both younger and older drivers. This indicates that older drivers and younger drivers are affected differently both in summer and winter.
基金supported by the National Natural Science Foundation of China(No.51178149)
文摘Many studies suggest that more crashes occur due to mixed traffic flow at unsignalized intersections. However, very little is known about the injury severity of these crashes. The objective of this study is therefore to investigate how contributory factors affect crash injury severity at unsignalized intersections. The dataset used for this analysis derived from police crash reports from Dec. 2006 to Apr. 2009 in Heilongjiang Province, China. An ordered probit model was developed to predict the probability that the injury severity of a crash will be one of four levels ： no injury, slight injury, severe injury, and fatal injury. The injury severity of a crash was evaluated in terms of the most severe injury sustained by any person involved in the crash. Results from the present study showed that different factors had varying effects on crash injury severity. Factors found to result in the increased probability of serious injuries include adverse weather, sideswiping with pedestrians on poor surface, the interaction of rear-ends and the third-class highway, winter night without illumination, and the interaction between traffic signs or markings and the third-class highway. Although there are some limitations in the current study, this study provides more insights into crash injury severity at unsignalized intersections.
文摘Despite the importance of heavy vehicles in Australia’s transportation system,little is known on the factors influencing injury severity from accidents involving a single heavy vehicle.Heavy vehicular crashes have been one of the main causes of fatal injuries in Australia,and this raises safety concerns for transport authorities,insurance companies,and emergency services.Although there have been several potential attempts to identify the factors contributing to heavy vehicle crashes and injury severity,it is still necessary to reduce the number of traffic crashes and lower the fatality rate involving heavy vehicles.The aims of this study were investigating the effects of heavy trucks’presence in accidents on the injury severity level sustained by the vehicle driver and detecting the contributing factors that lead to specific injury severity levels.Fixed-and random-parameter ordered probit and logit models were applied for predicting the likelihood of three injury severity categories severe,moderate,and no injury based on data from crashes caused by heavy trucks in Victoria,Australia in 2012-2017.The results showed that the random-parameter ordered probit model performed better than the other models did.Twenty variables(i.e.,factors)were found to be significant,and 12 of them were found to have random parameters that were normally distributed.Since some of the investigated factors had different effects on the type of injury severity in Australia,this paper does not recommend generalizing the findings from other case studies.Based on the findings,Victoria state authorities can have insight and enhanced understanding of the specific factors that lead to various types of injury severity involving heavy trucks.Consequently,the safety of all road users,including heavy vehicle drivers,can be enhanced.
