Crossing Behavior Of Nonmotorized Vehiclesat Urban Intersections Based On Survival Analysis Method

As a developing country, China has its own traffic characteristics. A mix of non-motorized and motorized vehicles is an important traffic type in China. It is also one of the most important reasons which cause urban traffic congestion and frequent accidents. Because of its high flexibility and punctuality, a non-motor vehicle is the ideal mode of transportation for short/middle distance travel and transfer to public transport. Non-motorized vehicle is irreplaceable at this stage because it is suitable to our national conditions and has widely basis of the masses. In the past, Non-motorized vehicle only refers to human bicycle. In recent years, with the development of technology, electric bike is widely used. Compared with human bicycle, riding an electric bike can reach faster and farther, but more unsafely. It is a new problem of urban traffic in China. In addition, riding a non-motorized vehicle is healthy, non-polluting, and energy-efficient. Development of urban non-motorized traffic can prevent and mitigate traffic congestion, reduce air pollution and energy consumption. It is very beneficial to people's living and urban sustainable development.However, non-motor vehicle is a relatively weak group in urban traffic. There are always a high proportion of traffic accidents involving non-motor vehicles. One typical type of rule violation behavior is red-light running. Because of the poor law enforcement and peoples'low safety awareness, red-light running is rather prevalent in China. The literature review suggests that very little has been done on the red-light running of non-motor vehicles, much less on this study based on survival analysis method. Survival analysis has the advantage that can consider censored data, and can combine to study the result of the event and the time that the result experienced. This method is very suitable for studying red-light crossing behavior at signalized intersections. Therefore, according to our national traffic characteristic, based on survival analysis methods, this dissertation focused on the study of crossing behavior and waiting endurance times of non-motorized vehicles and their influence factors. Then, a special management measure was evaluated. Specifically, the contents of this dissertation are as follows:(1) Crossing behavior of non-motorized vehicles was analyzed by the empirical research. First, typical intersections on main urban roads were chosen, basic data about crossing behavior was collected by field observation. Then, several variables which described crossing behavior at intersection were coded and were used to reveal microscopic behavior differences among cyclists, electric bike riders and pedestrians. These variables included waiting time, waiting position, moving trajectory, travel speed and safety margin, etc. The results show that red-light crossing rates of electric bike riders are significantly higher than those of cyclists and pedestrians. The riders coming from both sides are more likely to run against a red light than the ones of straight arrival. Pedestrians are less likely to not wait to cross the red light than bicycles and electric vehicles. Generally, they are likely to wait longer times than cyclists and electric bike riders. Among three modes of transportation, an electric bike has the fastest speed and the smallest safety margin.(2) Waiting endurance time distributions of non-motor vehicles were explored. Based on the empirical research, a duration model of waiting times for non-motor vehicles crossing an intersection was proposed. Their waiting times were estimated. The red-light crossing rates of non-motor vehicles were explored. The results show that the red-light crossing rates increase with the increasing waiting time. About18.2%of riders are at high risk of violation and low waiting time to cross the intersections. About20.6%of all the riders are generally non-risk takers who can obey the traffic rules after waiting for120seconds. In addition, it is noted that the improper handle of censored data would overestimate the red-light crossing rates of non-motor vehicles.(3) Cox hazard-based models and the influence factors of red-light running behavior were investigated. Cox hazard-based models of riders' waiting endurance times were proposed. Based on the surveyed data, the model parameters were estimated. The effects of various potential factors on riders' violation risk and waiting time were analyzed systematically. The results show that traffic mode, waiting position, rush hour, conformity behavior and motorized vehicle volume have significantly impact on red-light running behavior. Electric bike riders have higher risks and shorter waiting times than cyclists. Nearer to main roads waiting position is, more likely to run against a red light riders are. Riders in off-peak hours are more likely to run against a red light than those in peak hours. Riders have higher risk and shorter waiting times with less volume of motorized vehicles, as well as the bigger number of other riders that are crossing against the red light when arrives. The Cox hazard model formulated in this chapter can be applied to forecast temporal shifts in waiting duration times of non-motorized vehciles due to changes in traffic operation, management and control.(4) Safety crossing reliability of commuter riders was modeled and analyzed. Crossing reliability of commuter riders at intersections was proposed by using reliability theory and accelerated hazard models method. Based on the empirical data, the optimal mathematical model of safety crossing reliability was chosen. The key factors affecting the crossing reliability were investigated. Furthermore, commuter riders are divided into two categories:wait and don't wait. Their safety crossing reliabilities were discussed respectively. The results show that some potential variables including waiting position, moving direction and conformity behavior have significantly impacts on safety crossing reliability of commuter riders. Gompertz distribution model is very appropriate for fitting of safety crossing reliability for waiting commuter riders.(5) Control effects of traffic wardens on red-light running behavior were evaluated. By using Logistic model, analysis of variance, covariance analysis and survival analysis methods, riders'behavior characteristics, red-light running rates and waiting endurance times were analyzed with and without traffic wardens. According to the comparison, the control effect of traffic wardens on red-light crossing behavior was evaluated. The results show that traffic wardens have a significantly impact on red-light crossing behavior. Red-light crossing rates of riders and pedestrians with traffic wardens are lower than those without traffic wardens. Waiting endurance times of riders and pedestrians with traffic wardens are longer than those without traffic wardens. Traffic wardens have good control effects on the straight-arrival groups, but no significant effects on groups coming from the left and right sides.