With the development of the economy and the acceleration of urbanization, the number of vehicles in cities is increasing rapidly, which greatly increases the pressure on urban traffic. Solving traffic accidents and pr...With the development of the economy and the acceleration of urbanization, the number of vehicles in cities is increasing rapidly, which greatly increases the pressure on urban traffic. Solving traffic accidents and problems to keep smooth travel and safe travel has become a top priority in road construction. In this paper, how to optimize the traffic at the intersection of the urban road was discussed with the aim of reducing traffic accidents and problems to keep peoples’ smooth travel and safe travel.展开更多
交通事故导致车辆被迫采取换道、减速等行为,严重影响路段的通行能力。为缓解交叉口处因事故导致的交通阻塞,有必要基于事故车道位置、与交叉口距离及事故规模等信息,分析事故前后交通流变化规律,从而进行交通影响研究,并制定满足交通...交通事故导致车辆被迫采取换道、减速等行为,严重影响路段的通行能力。为缓解交叉口处因事故导致的交通阻塞,有必要基于事故车道位置、与交叉口距离及事故规模等信息,分析事故前后交通流变化规律,从而进行交通影响研究,并制定满足交通需求的临时性信号配时方案。该研究借助实地调研事故数据,构建了宏观交通流仿真模型,以选定交叉口的交通量、平均车速、最大排队车辆数和平均延误作为优化目标,并通过SUMO(Simulation of Urban Mobility)仿真试验验证4种信号配时方案的有效性。最后,建立基于熵权-TOPSIS法的综合评估模型,对4种信号配时方案的效果进行综合评估。结果表明:事故发生位置、事故规模对交通运行的影响呈非线性变化;在4种信号配时方案中,方案2的整体优化效果最佳,事故方向交通量提升了11.76%,平均车速提升了16.37%,事故方向平均延误降低了12.59%,最大排队车辆数降低22.41%。展开更多
With the rapid development of urban, the scale of the city is expanding day by day. The road environment is becoming more and more complicated. The vehicle ego-localization in complex road environment puts forward imp...With the rapid development of urban, the scale of the city is expanding day by day. The road environment is becoming more and more complicated. The vehicle ego-localization in complex road environment puts forward imperative requirements for intelligent driving technology. The reliable vehicle ego-localization, including the lane recognition and the vehicle position and attitude estimation, at the complex traffic intersection is significant for the intelligent driving of the vehicle. In this article, we focus on the complex road environment of the city, and propose a pose and position estimation method based on the road sign using only a monocular camera and a common GPS （global positioning system）. Associated with the multi-sensor cascade system, this method can be a stable and reliable alternative when the precision of multi-sensor cascade system decreases. The experimental results show that, within 100 meters distance to the road signs, the pose error is less than 2 degrees, and the position error is less than one meter, which can reach the lane-level positioning accuracy. Through the comparison with the Beidou high-precision positioning system L202, our method is more accurate for detecting which lane the vehicle is driving on.展开更多
文摘With the development of the economy and the acceleration of urbanization, the number of vehicles in cities is increasing rapidly, which greatly increases the pressure on urban traffic. Solving traffic accidents and problems to keep smooth travel and safe travel has become a top priority in road construction. In this paper, how to optimize the traffic at the intersection of the urban road was discussed with the aim of reducing traffic accidents and problems to keep peoples’ smooth travel and safe travel.
文摘交通事故导致车辆被迫采取换道、减速等行为,严重影响路段的通行能力。为缓解交叉口处因事故导致的交通阻塞,有必要基于事故车道位置、与交叉口距离及事故规模等信息,分析事故前后交通流变化规律,从而进行交通影响研究,并制定满足交通需求的临时性信号配时方案。该研究借助实地调研事故数据,构建了宏观交通流仿真模型,以选定交叉口的交通量、平均车速、最大排队车辆数和平均延误作为优化目标,并通过SUMO(Simulation of Urban Mobility)仿真试验验证4种信号配时方案的有效性。最后,建立基于熵权-TOPSIS法的综合评估模型,对4种信号配时方案的效果进行综合评估。结果表明:事故发生位置、事故规模对交通运行的影响呈非线性变化;在4种信号配时方案中,方案2的整体优化效果最佳,事故方向交通量提升了11.76%,平均车速提升了16.37%,事故方向平均延误降低了12.59%,最大排队车辆数降低22.41%。
基金This work was supported by the Key Project of National Natural Science Foundation of China under Grant No. 61332015 and the Natural Science Foundation of Shandong Province of China under Grant Nos. ZR2013FM302 and ZR2017MF057.
文摘With the rapid development of urban, the scale of the city is expanding day by day. The road environment is becoming more and more complicated. The vehicle ego-localization in complex road environment puts forward imperative requirements for intelligent driving technology. The reliable vehicle ego-localization, including the lane recognition and the vehicle position and attitude estimation, at the complex traffic intersection is significant for the intelligent driving of the vehicle. In this article, we focus on the complex road environment of the city, and propose a pose and position estimation method based on the road sign using only a monocular camera and a common GPS （global positioning system）. Associated with the multi-sensor cascade system, this method can be a stable and reliable alternative when the precision of multi-sensor cascade system decreases. The experimental results show that, within 100 meters distance to the road signs, the pose error is less than 2 degrees, and the position error is less than one meter, which can reach the lane-level positioning accuracy. Through the comparison with the Beidou high-precision positioning system L202, our method is more accurate for detecting which lane the vehicle is driving on.
