Unobserved heterogeneity of crashes remains a significant issue for freeways that influence crash prediction, and therefore deserves much attention. Using a fusion data set of crash data, driving behavior data, and tr...
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Unobserved heterogeneity of crashes remains a significant issue for freeways that influence crash prediction, and therefore deserves much attention. Using a fusion data set of crash data, driving behavior data, and traffic flow data, this study explores the spatiotemporal heterogeneity of crash determinants for different freeway segments (e.g. Yixing section and Liyang section of Ning-Hang freeway of China) and then predict the crash probability. A random effect negative binomial regression model is built to investigate the contributing factors of the crashes. Remarkable differences are observed in the crash determinants for Yixing section (include average vehicle speed, hourly average traffic volume, average free speed, road segment length, and number of left lane-merging) and Liyang section (include average intensity of aggressive driving behavior, average kilometer traffic volume). The results found the traffic flow has a more significant impact on crashes than the driving behaviors. It is found that the crash probability is a monotone decreasing function when the predicted number of crash is 0. With the increase of the number of predicted crash, the crash probability gradually converges from a large value to 0. Then the probability of other predicted number of crashes (e.g. crash = 1, crash = 2, crash = 3) presents a quadratic parabola trends. The model comparison demonstrates that the proposed model outperforms conventional model, and the prediction performance for Liyang section is better than that of Yixing section. The research findings are interesting and important for preventing crashes.
To study riding safety at intersection entrance,video recognition technology is used to build vehicle-bicycle conflict models based on the Bayesian *** is analyzed the relationship among the width of nonmotorized lane...
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To study riding safety at intersection entrance,video recognition technology is used to build vehicle-bicycle conflict models based on the Bayesian *** is analyzed the relationship among the width of nonmotorized lanes at the entrance lane of the intersection,the vehicle-bicycle soft isolation form of the entrance lane of intersection,the traffic volume of right-turning motor vehicles and straight-going non-motor vehicles,the speed of right-turning motor vehicles,and straight-going non-motor vehicles,and the conflict between right-turning motor vehicles and straight-going nonmotor *** to the traditional statistical methods,to overcome the discreteness of vehicle-bicycle conflict data and the differences of influencing factors,the Bayesian randomeffect Poisson-log-normal model and random effect negative binomial regression model are *** results show that the randomeffect Poisson-log-normal model is better than the negativebinomial distribution of randomeffects;The width of non-motorized lanes,the form of vehicle-bicycle soft isolation,the traffic volume of right-turning motor vehicles,and the coefficients of straight traffic volume obey a normal *** them,the type of vehicle-bicycle soft isolation facilities and the vehicle-bicycle traffic volumes are significantly positively correlated with the number of vehicle-bicycle *** width of non-motorized lanes is significantly negatively correlated with the number of vehicle-bicycle *** periods and flat periods,the average speed of right-turning motor vehicles,and the average speed of straight-going non-motor vehicles have no significant influence on the number of vehicle-bicycle conflicts.
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