城市路网多路口路径动态实时选择方法

为了缓解城市交通拥堵问题,如何充分利用现有的道路资源进行有效的路线导航,一直是学者们关心的热点问题.现有的研究方法包括：优化交通灯信号周期以增大交通流量;对个别车辆的行驶路线进行优化;利用历史交通数据或者通过路网中心和车辆之间的主从式博弈进行路径导航等.然而,这些研究并没有考虑到微观行驶车辆的个性化交通需求以及多车辆彼此之间的路线选择冲突,对于城市路网中交通状况的动态不确定性也没有充分考虑.基于以上问题,提出了城市交通路网动态实时多路口路径选择模型DR²SM（dynamic and real-time route selection model in urban traffic networks）,结合车辆对前方可选路线的偏好以及可选路线的实时交通状况,并利用自适应学习算法SALA（self-adaptive learning algorithm）进行博弈,以使得各行驶车辆的动态路线选择策略达到Nash均衡.     

基于道路风险评估的城市路网实时路径选择

为了缓解城市交通拥堵、避免交通事故的发生,城市路网的路径选择一直以来是一个热门的研究课题.随着边缘计算和车辆智能终端技术的发展,城市路网中的行驶车辆从自组织网络朝着车联网(Internet of vehicles,IoV)范式过渡,这使得车辆路径选择问题从基于静态历史交通数据的计算向实时交通信息计算转变.在城市路网路径选择问题上,众多学者的研究主要聚焦如何提高出行效率,减少出行时间等.然而这些研究并没有考虑所选路径是否存在风险等问题.基于以上问题,首次构造了一个基于边缘计算技术的道路风险实时评估模型(real-time road risk assessment model based on edge computing, R³A-EC),并提出基于该模型的城市路网实时路径选择方法(real-time route selection method based on risk assessment, R²S-RA). R3A-EC模型利用边缘计算技术的低延迟,高可靠性等特点对城市道路进行实时风险评估,并利用最小风险贝叶斯决策验证道路是否存在风险问...     

Optimal Information Location for Adaptive Routing

One strategy for addressing uncertain roadway conditions and travel times is to provide real-time travel information to drivers through variable message signs, highway advisory radio, or other means. However, providing such information is often costly, and decisions must be made about the most useful places to inform drivers about local conditions. This paper addresses this question, building on adaptive routing algorithms describing optimal traveler behavior in stochastic networks with en route information. Three specific problem contexts are formulated: routing of a single vehicle, assignment of multiple vehicles in an uncongested network, and adaptive equilibrium with congestion. A network contraction procedure is described which makes an enumerative algorithm computationally feasible for small-to-medium sized roadway networks, along with heuristics which can be applied for large-scale networks. These algorithms are demonstrated on three networks of varying size.     

A Dynamic Road Incident Information Delivery Strategy to Reduce Urban Traffic Congestion

Advanced information and communication technologies can be used to facilitate traffic incident management. If an incident is detected and blocks a road link, in order to reduce the incident-induced traffic congestion, a dynamic strategy to deliver incident information to selected drivers and help them make detours in urban areas is proposed by this work. Time-dependent shortest path algorithms are used to generate a subnetwork where vehicles should receive such information. A simulation approach based on an extended cell transmission model is used to describe traffic flow in urban networks where path information and traffic flow at downstream road links are well modeled. Simulation results reveal the influences of some major parameters of an incident-induced congestion dissipation process such as the ratio of route-changing vehicles to the total vehicles, operation time interval of the proposed strategy, traffic density in the traffic network, and the scope of the area where traffic incident information is delivered. The results can be used to improve the state of the art in preventing urban road traffic congestion caused by incidents.      

RFID，GPS和GIS技术集成在交通智能监管系统中的应用研究*

为实现在城市复杂路网情况下对交通车辆的实时监控,并且能通过一定数量的车辆运行状态来判断道路交通的拥挤状况,采用射频识别技术(RFID)对道路上运行的车辆进行动态识别和数据信息交换;依靠全球定位系统(GPS)技术实时获得目标车辆的位置信息,并通过地理信息系统(GIS)将车辆的运行状况以及路网的交通状况以电子地图形式实时地展现给用户。将GPS、GIS与RFID技术综合应用于城市道路交通管理系统中,在此基础上设计出道路交通车辆的全程监控模型和系统框架。对交通监管的信息化建设具有一定的借鉴意义。     

A Hybrid Route Choice Model for Dynamic Traffic Assignment

Network user equilibrium or user optimum is an ideal state that can hardly be achieved in real traffic. More often than not, every day traffic tends to be in disequilibrium rather than equilibrium, thanks to uncertainties in demand and supply of the network. In this paper we propose a hybrid route choice model for studying non-equilibrium traffic. It combines pre-trip route choice and en-route route choice to solve dynamic traffic assignment (DTA) in large-scale networks. Travelers are divided into two groups, habitual travelers and adaptive travelers. Habitual travelers strictly follow their pre-trip routes which can be generated in the way that major links, such as freeways or major arterial streets, are favored over minor links, while taking into account historical traffic information. Adaptive travelers are responsive to real-time information and willing to explore new routes from time to time. We apply the hybrid route choice model in a synthetic medium-scale network and a large-scale real network to assess its effect on the flow patterns and network performances, and compare them with those obtained from Predictive User Equilibrium (PUE) DTA. The results show that PUE-DTA usually produces considerably less congestion and less frequent queue spillback than the hybrid route choice model. The ratio between habitual and adaptive travelers is crucial in determining realistic flow and queuing patterns. Consistent with previous studies, we found that, in non-PUE DTA, supplying a medium sized group (usually less than 50%) of travelers real-time information is more beneficial to network performance than supplying the majority of travelers with real-time information. Finally, some suggestions are given on how to calibrate the hybrid route choice model in practice to produce realistic results.     

交通信号的实时公平调度及其仿真

交通信号的实时调度是改善交通拥堵的重要途径之一,其公平性研究同样至关重要。针对通信网络和交通网络的共同特点,借鉴其最大最小公平和比例公平的思想,分别提出最小最大公平、比例公平交通信号实时调度算法;并与优化队列长度的实时调度、固定周期调度算法进行仿真对比。实验结果表明,优化队列长度的实时调度和固定周期调度会使得部分车辆等待时间过长而表现出不公平;最小最大公平调度表现出最好的公平性,但在网络高密度下平均时延表现较差;比例公平调度则在各种交通密度下同时表现出较低的平均时延和较好的公平性。研究结果为实时交通信号的公平调度提供了解决方案,具有较好的应用价值。     

城市快速路入口匝道与交叉口协调控制策略

为提高城市快速路的运行效率,避免快速路入口匝道处发生拥堵并影响地面交通,结合快速路交通状态的实时判别、匝道调节率及交叉口信号配时方案,提出了针对快速路典型交通状态下的精准协同控制模型。协调控制模型的四个子模型为,流量精准推送模型、交叉口关键相位迟闭、早断配时优化模型、匝道排队调节控制模型。案例仿真表明：协调控制信号迟闭策略下匝道通过流量、车均延误,交叉口平均排队长度、车均延误分别优化了2.3%、40.4%、21.8%、22.4%;协调控制信号早断策略下匝道车均延误、平均排队长度,交叉口平均排队长度、车均延误分别优化了22.6%、31.7%、9.7%、4.5%;协调控制较大程度上提升了研究区域运行效率。     

基于网络演算的智能交通流量分配研究

精确的交通流量分配计算模型,能为实际的交通工程应用提供具体的流量出入速率或者信号灯控制时间方案,具有重大价值。首次将动态交通流量分配拟化为网络负载均衡问题,使用漏桶理论和网络演算方法,将交通的流量分配与路径时延转换为一系列极值运算,结合贪婪算法,以均衡网络延时为优化目标,得到交通配流。仿真结果表明,本模型在化解拥堵的同时,使分流后的道路平均延时普遍降低,能提升整体路网的通行能力。     

Vehicle routing under time-dependent travel times: The impact of congestion avoidance

Daily traffic congestion forms a major problem for businesses such as logistic service providers and distribution firms. It causes late arrivals at customers and additional costs for hiring the truck drivers. Such costs caused by traffic congestion can be reduced by taking into account and avoiding predictable traffic congestion within vehicle route plans. In the literature, various strategies are proposed to avoid traffic congestion, such as selecting alternative routes, changing the customer visit sequences, and changing the vehicle-customer assignments. We investigate the impact of these and other strategies in off-line vehicle routing on the performance of vehicle route plans in reality. For this purpose, we develop a set of vehicle routing problem instances on real road networks, and a speed model that reflects the key elements of peak hour traffic congestion. The instances are solved for different levels of congestion avoidance using a modified Dijkstra algorithm and a restricted dynamic programming heuristic. Computational experiments show that 99% of late arrivals at customers can be eliminated if traffic congestion is accounted for off-line. On top of that, about 87% of the extra duty time caused by traffic congestion can be eliminated by clever congestion avoidance strategies.     

基于激光传感数据的拥堵路口交通信号灯控制

针对城市车流高峰时段的道路拥堵问题,提出基于激光传感数据的交通信号灯智能控制方法研究。在道路两侧均匀布置激光传感器节点,采集实时的激光传感数据和车流量信息,并构建一种两层级的交通信号灯控制模型,以提取的交通路口实时传感数据作为输入项进行模糊推理,并求解出交通信号控制模糊子集,最后推导出当前车流长度、车辆在路口的平均滞留时长及车辆的延误时长等变量,达到缓解交通拥堵,提高通行效率的目的。仿真实验数据表明,提出的拥堵交通信号灯智能控制方法具有良好的控制效果,可以明显减少车辆延误时长,提高道路通行的效率和安全性。     

公交移动协助城市车载网络分层路由协议

城市车载网络作为未来智能交通的重要组成部分,为解决城市交通问题提供了有效的解决方案.现有大部分路由方案未能很好地解决车辆高速移动、复杂城市交通环境和不同车流密度三大问题对路由转发的影响,导致路由协议的性能在高效和可靠性方面不足.根据城市交通信号传播环境、红灯等停以及公交运行轨迹固定、周期循环等特性,提出公交移动协助的城市车载网络路由协议,将车车之间的复杂、不确定路由转换为普通车辆-簇头-公交三层节点之间的确定、协作方式,发挥公交周期移动优势,并用簇头策略优化普通车辆与公交之间的通信.设计根据城市信号传播衰减与车辆移动相关的单跳链路筛选与多跳延迟相关的概率转发机制,保证公交转发的高效、可靠,并设计能自适应车流密度的簇头触发策略,仿真实验结果表明,整体方案转发跳数达到一定数量时,能取得较好的数据传输成功率和较低的网络延迟.     

城市交通网络正系统模型与稳态信号控制

城市路网内车流分布不均衡为交通拥堵原因之一,且交通控制系统为典型正系统．为此,本文采用Compartment正系统对网络交通流演化建模,以网络均衡为控制目标,提出稳态信号控制方法．首先,建立网络交通流Compartment正系统模型,可描述具有任意控制结构网络的过饱和特征;进一步,给定网络车流输入,由非负矩阵理论可得网络存在唯一稳定平衡点,给出平衡点解析计算公式．由此,提出网络稳态信号控制律,为路段状态反馈控制律．最后,以北京市奥林匹克公园区域拓扑路网为例,在VISSIM软件中建立仿真环境,比较稳态信号控制方法与定时控制方法,仿真结果表明在高需求网络条件下,稳态信号控制方法可改善网络整体性能和缓解局部拥堵．     

A general framework for modeling shared autonomous vehicles with dynamic network-loading and dynamic ride-sharing application

Shared autonomous vehicles (SAVs) could provide low-cost service to travelers and possibly replace the need for personal vehicles. Previous studies found that each SAV could service multiple travelers, but many used unrealistic congestion models, networks, and/or travel demands. The purpose of this paper is to provide a method for future research to use realistic flow models to obtain more accurate predictions about SAV benefits. This paper presents an event-based framework for implementing SAV behavior in existing traffic simulation models. We demonstrate this framework in a cell transmission model-based dynamic network loading simulator. We also study a heuristic approach for dynamic ride-sharing. We compared personal vehicles and SAV scenarios on the downtown Austin city network. Without dynamic ride-sharing, the additional empty repositioning trips made by SAVs increased congestion and travel times. However, dynamic ride-sharing resulted in travel times comparable to those of personal vehicles because ride-sharing reduced vehicular demand. Overall, the results show that using realistic traffic flow models greatly affects the predictions of how SAVs will affect traffic congestion and travel patterns. Future work should use a framework such as the one in this paper to integrate SAVs with established traffic flow simulators.     

Detailed traffic animation for urban road networks

We present a new agent-based system for detailed traffic animation on urban arterial networks with diverse junctions like signalized crossing, merging and weaving areas. To control the motion of traffic for visualization and animation purposes, we utilize the popular follow-the-leader method to simulate various vehicle types and intelligent driving styles. We also introduce a continuous lane-changing model to imitate the vehicle’s decision-making process and dynamic interactions with neighboring vehicles. By applying our approach in several typical urban traffic scenarios, we demonstrate that our system can well visualize vehicles’ behaviors in a realistic manner on complex road networks and generate immersive traffic flow animations with smooth accelerating strategies and flexible lane changes.     

城市交通网络中救援车辆路径优化研究

道路中断、可靠性差等路径选择问题从根本上影响了救援工作的效率,针对这一现状,设计了基于MATLAB的应急救援车辆最优路径模型.依托城市交通路网的数据,通过层次分析法确定影响应急救援的因素,利用MATLAB蚁群算法结合ArcGIS平台构建城市路网要素,定位应急设施及求解应急救援车辆路径优化结果.通过实例分析改变要素信息时...     

混合Petri网交叉口信号灯建模的模糊优化

城市车流量急剧增加和道路通行能力之间的矛盾日益激化,交通拥堵已成为亟待解决的社会问题。道路交叉口信号灯优化控制是解决该问题的有效方法。基于城市道路交通系统随机性强、离散性连续性混杂、难以用数学模型精确建模等特点,提出了一种用连续Petri网建立道路交通流模型,用离散Petri网对道路交叉口信号灯进行控制的方案。根据车流量的动态变化,采用模糊控制对交叉口绿灯时间进行自适应优化。仿真结果表明,该方案能提高交叉口通行能力,减少车辆延误,优于传统的控制方法。     

基于云网格集成调度的防拥堵车辆路径规划算法

在道路交通路网中,车辆拥堵问题是流量与路网结构之间相互作用的一个复杂动态过程,通过车辆路径规划,实现对路网网格集成调度,从而提高路网通行吞吐量。传统方法采用并行微观交通动态负载平衡预测算法实现车辆拥堵调度和车辆路径规划,不能准确判断路面上的车辆密度,路径规划效益不好。提出一种基于云网格集成调度的防拥堵车辆路径规划算法,即构建基于Small-World模型的云网格路网模型,采用RFID标签信息进行路况信息采集,实现交通网络拥堵评估信息特征的提取,采用固有模态函数加权平均求得各车道的车辆拥塞状态函数,对所有车道内车辆密度取统计平均可获得簇内的车辆密度。设计交通路网拥堵检测算法来对当前个体道路信息进行一维邻域搜索,从而实现车辆路径规划控制目标函数最佳寻优。通过动态博弈的方式求得车辆防拥堵路径的近似最优轨迹,实现路径规划算法的改进。仿真结果表明,该算法能准确规划车辆路径,实现最优路径控制,从而提高严重拥堵路段的车流速度和路网吞吐性能,性能优越。     

车联网环境下基于模糊逻辑的交通拥堵检测方法

针对现有的道路交通拥堵检测方法的不足,提出了一种基于V2V的道路交通拥堵检测方法。首先基于V2V的方式实时获取邻居车辆状态信息,建立车辆邻居表;其次依据车辆行驶速度、车流密度、交通拥堵评级体系构建模糊控制器,完成本地交通拥堵水平的估计;然后通过车车通信进行邻居车辆交通拥堵状况的查询,并根据大子样假设检验验证本地交通拥堵水平值,完成所在区域交通拥堵水平的检测;最后基于Veins平台搭建仿真测试场景,仿真对比了拥堵检测结果的准确率,同时测试车辆节点的退避时槽数量和接收广播数据包的数量。实验结果表明, 提出的道路交通拥堵检测方法实现的拥堵检测准确率分别比线圈法和CoTEC法提高了5.5%和7.5%;提出的道路交通拥堵检测方法实现的车车通信网络拥塞比CoTEC法降低了90.8%,并且在未发生交通拥堵时通信节点的通信负载显著降低。     

Dynamic routing under recurrent and non-recurrent congestion using real-time ITS information

In just-in-time (JIT) manufacturing environments, on-time delivery is a key performance measure for dispatching and routing of freight vehicles. Growing travel time delays and variability, attributable to increasing congestion in transportation networks, are greatly impacting the efficiency of JIT logistics operations. Recurrent and non-recurrent congestion are the two primary reasons for delivery delay and variability. Over 50% of all travel time delays are attributable to non-recurrent congestion sources such as incidents. Despite its importance, state-of-the-art dynamic routing algorithms assume away the effect of these incidents on travel time. In this study, we propose a stochastic dynamic programming formulation for dynamic routing of vehicles in non-stationary stochastic networks subject to both recurrent and non-recurrent congestion. We also propose alternative models to estimate incident induced delays that can be integrated with dynamic routing algorithms. Proposed dynamic routing models exploit real-time traffic information regarding speeds and incidents from Intelligent Transportation System (ITS) sources to improve delivery performance. Results are very promising when the algorithms are tested in a simulated network of South-East Michigan freeways using historical data from the MITS Center and Traffic.com.