Traffic intersections are incredibly dangerous for drivers and pedestrians. Statistics from both Canada and the U.S. show a high number of fatalities and serious injuries related to crashes at intersections. In Canada...Traffic intersections are incredibly dangerous for drivers and pedestrians. Statistics from both Canada and the U.S. show a high number of fatalities and serious injuries related to crashes at intersections. In Canada, during 2019, the National Collision Database shows that 28% of traffic fatalities and 42% of serious injuries occurred at intersections. Likewise, the U.S. National Highway Traffic Administration (NHTSA) found that about 40% of the estimated 5,811,000 accidents in the U.S. during the year studied were intersection-related crashes. In fact, a major survey by the car insurance industry found that nearly 85% of drivers could not identify the correct action to take when approaching a yellow traffic light at an intersection. One major reason for these accidents is the “yellow light dilemma,” the ambiguous situation where a driver should stop or proceed forward when unexpectedly faced with a yellow light. This situation is even further exacerbated by the tendency of aggressive drivers to inappropriately speed up on the yellow just to get through the traffic light. A survey of Canadian drivers conducted by the Traffic Injury Research Foundation found that 9% of drivers admitted to speeding up to get through a traffic light. Another reason for these accidents is the increased danger of making a left-hand turn on yellow. According to the National Highway Traffic Safety Association (NHTSA), left turns occur in approximately 22.2% of collisions—as opposed to just 1.2% for right turns. Moreover, a study by CNN found left turns are three times as likely to kill pedestrians than right turns. The reason left turns are so much more likely to cause an accident is because they take a driver against traffic and in the path of oncoming cars. Additionally, most of these left turns occur at the driver’s discretion—as opposed to the distressingly brief left-hand arrow at busy intersections. Drive Safe Now proposes a workable solution for reducing the number of accidents occurring during a yellow light at intersections. We believe this fairly simple solution will save lives, prevent injuries, reduce damage to public and private property, and decrease insurance costs.展开更多
This paper focuses on the simulation analysis of stripe formation and dynamic features of intersecting pedestrian flows.The intersecting flows consist of two streams of pedestrians and each pedestrian stream has a des...This paper focuses on the simulation analysis of stripe formation and dynamic features of intersecting pedestrian flows.The intersecting flows consist of two streams of pedestrians and each pedestrian stream has a desired walking direction.The model adopted in the simulations is the social force model, which can reproduce the self-organization phenomena successfully. Three scenarios of different cross angles are established. The simulations confirm the empirical observations that there is a stripe formation when two streams of pedestrians intersect and the direction of the stripes is perpendicular to the sum of the directional vectors of the two streams. It can be concluded from the numerical simulation results that smaller cross angle results in higher mean speed and lower level of speed fluctuation. Moreover, the detailed pictures of pedestrians＇ moving behavior at intersections are given as well.展开更多
Connected Automated Vehicles(CAVs)have drawn much attention in recent years.High reliable automatic technologies can help CAVs to follow given trajectories well.However,safety and efficiency are hard to be ensured sin...Connected Automated Vehicles(CAVs)have drawn much attention in recent years.High reliable automatic technologies can help CAVs to follow given trajectories well.However,safety and efficiency are hard to be ensured since the interactions between CAVs and pedestrians are complex problems.Thus,this study focuses on cooperative intersection management for CAVs and pedestrians.To avoid the effects of uncertainty about pedestrian behaviors,an indirect way is to use pedestrians’signal lights to guide the movements of pedestrians,and such lights with communication devices can share information with CAVs to make decisions together.In time domains,a general conflict-free rule is established depending on the positions of CAVs and crosswalks.Geometric analysis with coordinate calculation is used to accurately determine the feasible vehicle trajectories and the reasonable periods for signal lights turning green.Four control strategies for the same conditions are compared in simulation experiments,and their performances are analyzed.We demonstrate that the proposed cooperative strategy not only balances the benefits of vehicles and pedestrians but also improves the traffic efficiency at the intersection.展开更多
Improving the capacity of intersections is the key to enhancing road traffic systems.Benefiting from the application of Connected Automated Vehicles(CAVs)in the foreseeing future,it is promising to fully utilize spati...Improving the capacity of intersections is the key to enhancing road traffic systems.Benefiting from the application of Connected Automated Vehicles(CAVs)in the foreseeing future,it is promising to fully utilize spatiotemporal resources at intersections through cooperative and intelligent trajectory planning for CAVs.Lane-free traffic is currently a highly anticipated solution that can achieve more flexible trajectories without being limited by lane boundaries.However,it is challenging to apply efficient lane-free traffic to be compatible with the traditional intersection control mode for mixed flow composed of CAVs and Human-driving Vehicles(HVs).To address the research gap,this paper proposes a spatiotemporal-restricted A∗algorithm to obtain efficient and flexible lane-free trajectories for CAVs.First,we restrict the feasible area of the heuristic search algorithm by considering the feasible area and orientation of vehicles to maintain the trajectory directionality of different turning behaviors.Second,we propose a spatiotemporal sparse sampling method by defining the four-dimensional spatiotemporal grid to accelerate the execution of the heuristic search algorithm.Third,we consider the motions of HVs as dynamic obstacles with rational trajectory fluctuation during the process of trajectory planning for CAVs.The proposed method can retain the advantage of efficiently exploring feasible trajectories through the hybrid A*algorithm,while also utilizing multiple spatiotemporal constraints to accelerate solution efficiency.The experimental results of the simulated and real scenarios with mixed flows show that the proposed model can continuously enhance traffic efficiency and fuel economy as the penetration of CAVs gradually increases.展开更多
文摘Traffic intersections are incredibly dangerous for drivers and pedestrians. Statistics from both Canada and the U.S. show a high number of fatalities and serious injuries related to crashes at intersections. In Canada, during 2019, the National Collision Database shows that 28% of traffic fatalities and 42% of serious injuries occurred at intersections. Likewise, the U.S. National Highway Traffic Administration (NHTSA) found that about 40% of the estimated 5,811,000 accidents in the U.S. during the year studied were intersection-related crashes. In fact, a major survey by the car insurance industry found that nearly 85% of drivers could not identify the correct action to take when approaching a yellow traffic light at an intersection. One major reason for these accidents is the “yellow light dilemma,” the ambiguous situation where a driver should stop or proceed forward when unexpectedly faced with a yellow light. This situation is even further exacerbated by the tendency of aggressive drivers to inappropriately speed up on the yellow just to get through the traffic light. A survey of Canadian drivers conducted by the Traffic Injury Research Foundation found that 9% of drivers admitted to speeding up to get through a traffic light. Another reason for these accidents is the increased danger of making a left-hand turn on yellow. According to the National Highway Traffic Safety Association (NHTSA), left turns occur in approximately 22.2% of collisions—as opposed to just 1.2% for right turns. Moreover, a study by CNN found left turns are three times as likely to kill pedestrians than right turns. The reason left turns are so much more likely to cause an accident is because they take a driver against traffic and in the path of oncoming cars. Additionally, most of these left turns occur at the driver’s discretion—as opposed to the distressingly brief left-hand arrow at busy intersections. Drive Safe Now proposes a workable solution for reducing the number of accidents occurring during a yellow light at intersections. We believe this fairly simple solution will save lives, prevent injuries, reduce damage to public and private property, and decrease insurance costs.
基金Project supported by the National Natural Science Foundation of China(Grant No.61233001)the Fundamental Research Funds for the Central Universities,China(Grant No.2017JBM014)
文摘This paper focuses on the simulation analysis of stripe formation and dynamic features of intersecting pedestrian flows.The intersecting flows consist of two streams of pedestrians and each pedestrian stream has a desired walking direction.The model adopted in the simulations is the social force model, which can reproduce the self-organization phenomena successfully. Three scenarios of different cross angles are established. The simulations confirm the empirical observations that there is a stripe formation when two streams of pedestrians intersect and the direction of the stripes is perpendicular to the sum of the directional vectors of the two streams. It can be concluded from the numerical simulation results that smaller cross angle results in higher mean speed and lower level of speed fluctuation. Moreover, the detailed pictures of pedestrians＇ moving behavior at intersections are given as well.
基金supported by the Science and Technology Commission of Shanghai Municipality(Nos.22YF1461400 and 22DZ1100102)the National Natural Science Foundation of China(No.72001007).
文摘Connected Automated Vehicles(CAVs)have drawn much attention in recent years.High reliable automatic technologies can help CAVs to follow given trajectories well.However,safety and efficiency are hard to be ensured since the interactions between CAVs and pedestrians are complex problems.Thus,this study focuses on cooperative intersection management for CAVs and pedestrians.To avoid the effects of uncertainty about pedestrian behaviors,an indirect way is to use pedestrians’signal lights to guide the movements of pedestrians,and such lights with communication devices can share information with CAVs to make decisions together.In time domains,a general conflict-free rule is established depending on the positions of CAVs and crosswalks.Geometric analysis with coordinate calculation is used to accurately determine the feasible vehicle trajectories and the reasonable periods for signal lights turning green.Four control strategies for the same conditions are compared in simulation experiments,and their performances are analyzed.We demonstrate that the proposed cooperative strategy not only balances the benefits of vehicles and pedestrians but also improves the traffic efficiency at the intersection.
文摘为了降低共用车道中公共汽车和社会车辆行驶时的相互干扰,本文选取进口道公交停靠站的位置设置为研究对象进行优化.以厦门市3个沿进口道设置的单泊位公交停靠站为例,基于实地数据采集建立了进口道对应的车道宽度、机动车流量、公交车辆与社会车辆速度差异、距离交叉口位置等参数与干扰程度的相关模型,利用该模型预测公交车辆和社会车辆之间的交汇次数误差均低于20%.进而以厦门市的公交停靠站点为实例进行了干扰状态预测,以评价相应进口道运行延误情况,并以干扰最低值作为约束条件,对进口道处设置的公交停靠站提出相应的优化方法.研究验证表明,当机动车道设置宽度在3.5~3.75 m、公交停靠站距离交叉口长度在80~110 m时,干扰现象最不显著.两类车的交通流总量降至500 pcu/h时,车流干扰趋于缓和,且车辆速度差异差小于3 km/h.
基金This work was supported by the Science and Technology Commission of Shanghai Municipality(Nos.22YF1461400 and 22DZ1100102)the National Key R&D Program of China(No.2022ZD0160104).
文摘Improving the capacity of intersections is the key to enhancing road traffic systems.Benefiting from the application of Connected Automated Vehicles(CAVs)in the foreseeing future,it is promising to fully utilize spatiotemporal resources at intersections through cooperative and intelligent trajectory planning for CAVs.Lane-free traffic is currently a highly anticipated solution that can achieve more flexible trajectories without being limited by lane boundaries.However,it is challenging to apply efficient lane-free traffic to be compatible with the traditional intersection control mode for mixed flow composed of CAVs and Human-driving Vehicles(HVs).To address the research gap,this paper proposes a spatiotemporal-restricted A∗algorithm to obtain efficient and flexible lane-free trajectories for CAVs.First,we restrict the feasible area of the heuristic search algorithm by considering the feasible area and orientation of vehicles to maintain the trajectory directionality of different turning behaviors.Second,we propose a spatiotemporal sparse sampling method by defining the four-dimensional spatiotemporal grid to accelerate the execution of the heuristic search algorithm.Third,we consider the motions of HVs as dynamic obstacles with rational trajectory fluctuation during the process of trajectory planning for CAVs.The proposed method can retain the advantage of efficiently exploring feasible trajectories through the hybrid A*algorithm,while also utilizing multiple spatiotemporal constraints to accelerate solution efficiency.The experimental results of the simulated and real scenarios with mixed flows show that the proposed model can continuously enhance traffic efficiency and fuel economy as the penetration of CAVs gradually increases.
