Evolution of Road Traffic Congestion Control:A Survey from Perspective of Sensing,Communication,and Computation

Road traffic congestion can inevitably de-grade road infrastructure and decrease travel efficiency in urban traffic networks,which can be relieved by employing appropriate congestion control.Accord-ing to different developmental driving forces,in this paper,the evolution of road traffic congestion control is divided into two stages.The ever-growing num-ber of advanced sensing techniques can be seen as the key driving force of the first stage,called the sens-ing stage,in which congestion control strategies ex-perienced rapid growth owing to the accessibility of traffic data.At the second stage,i.e.,the communica-tion stage,communication and computation capabil-ity can be regarded as the identifying symbols for this stage,where the ability of collecting finer-grained in-sight into transportation and mobility reality improves dramatically with advances in vehicular networks,Big Data,and artificial intelligence.Specifically,as the pre-requisite for congestion control,in this paper,ex-isting congestion detection techniques are first elab-orated and classified.Then,a comprehensive survey of the recent advances for current congestion control strategies with a focus on traffic signal control,vehi-cle route guidance,and their combined techniques is provided.In this regard,the evolution of these strate-gies with continuous development of sensing,com-munication,and computation capability are also intro-duced.Finally,the paper concludes with several re-search challenges and trends to fully promote the in-tegration of advanced techniques for traffic congestion mitigation in transportation systems.

交叉口网联车速度与信号协同优化控制

网联环境下,为了提升信号交叉口绿灯利用效率,根据网联车与交通设施之间交互的车辆速度、位置及信号状态信息,提出一种单点信号交叉口网联车速度与信号配时协同优化控制方法。该方法基于车队离散理论,预测交叉口车辆到达率并根据预测结果优化交叉口信号配时。在此基础上,以周期内总排队车辆数最小为优化目标,对每辆网联车通过交叉口速度进行判断,按照车辆编号次序求解网联车的最佳行驶速度,实现速度引导与交叉口信号控制有效协调。采用MATLAB软件对模型进行仿真分析。研究结果表明,经信号优化与速度引导协同优化后,提出的速度引导与信号协同控制方法能够在相同时间内增加通过交叉口的车辆数,减少车辆延误,并有效提升交叉口交通效率。

基于模型偏差学习的交通信号自适应优化方法

城市交通信号配时优化是保证交通系统整体运行效率的前提条件。传统基于模型的信号控制方法往往基于历史统计数据,并且底层交通流模型不可避免地存在模型偏差,影响控制方案的科学性及其实际应用效果。考虑模型与数据融合驱动,提出一种基于模型偏差学习的交通信号控制自适应优化方法,以最小化路网行程时间为目标,建立信号控制最优化模型。首先,针对交通流预测模型的模型偏差,引入偏差函数表示预测模型与实际交通流状态间的误差;其次,建立基于径向基函数(RBF)神经网络的偏差函数,结合实际交通流数据对模型偏差进行学习,提高偏差函数的拟合效果;在此基础上,提出考虑模型偏差信息的自适应优化方法,以提高信号控制方法的控制性能。以小型测试路网与实际路网为例进行算例分析,基于SUMO仿真对本文提出的模型偏差学习方法进行验证,考虑不同交通流量条件,与固定配时、模型预测控制进行比较,分析其控制性能指标。结果表明,模型与数据融合驱动的信号控制自适应优化方法能有效提高预测模型的准确性,降低路网行程时间,与基于模型的控制和模型预测控制方法相比路网累积行程时间平均减少了38.3%和25.6%,提升了路网的实际运行效果。最后在宣城实际路网的仿真验证了方法的有效性。

面向高速公路连续瓶颈的协同可变限速控制

高速公路瓶颈处极易因通行能力受限而诱发交通拥堵,进而导致通勤时间延长、燃料消耗增加、驾驶舒适性降低,尤其是在连续多瓶颈处,更容易对道路网络造成连锁反应而大范围增加事故风险。既有拥堵控制策略聚焦于孤立瓶颈处拥堵消除,通过对多个瓶颈路段实施多个孤立瓶颈控制策略,由于控制策略间交通状态与控制参数缺乏协同,致使控制效果受限,且极易因策略协同失衡导致更为复杂的交通拥堵。本文提出一种基于模型预测控制方法的连续多瓶颈拥堵消除的协同可变限速控制策略:首先,针对连续多瓶颈主动管控场景改进元胞传输模型,以模拟高速公路多瓶颈生成、通行能力下降、随机交通流波动和可变限速控制状态与效果;其次,基于拉格朗日坐标系改进LWR模型,以精准预测交通拥堵场景下的交通流随机状态;再次,构建以多瓶颈路段总体交通运行状态作为输入的可变限速控制策略,并采用反馈式控制框架协同多瓶颈可变限速控制策略;最后,通过构建仿真实验测试了所提出控制策略消除连续瓶颈处拥堵的控制效率,并与其他控制策略进行了效果对比。实验结果表明,本文所提出的协同可变限速策略可有效消除连续瓶颈的多处拥堵,且控制效果显著优于其他控制策略,车辆总行程时间和总延误时间分别降低21.3%和70.6%。

高速公路实时交通流模糊逻辑优化控制系统设计

针对高速公路交通流特征复杂多变,并受到天气、道路状况和车辆类型等多种因素影响的问题,设计了一种新的高速公路实时交通流模糊逻辑优化控制系统,包括硬件和软件两部分;在硬件方面,设计了车流控制模块、无线传输车载终端、实时交通流逻辑调度模块和模糊控制器模块,并确定了它们之间的实时连接关系;在软件方面,求解了交通流逻辑控制条件,联合了高速公路实时通行相位条件,调节了模糊控制器,并根据高速公路交通路网模型推导了交通流优化控制条件的求解表达式;实验结果表明,该系统可以解决逻辑协调错误的问题,并有效控制高速公路车流量。

基于车路协同的下穿地道积水监测与交通预警管控系统

为解决城市道路下穿地道积水引发的交通拥堵与安全事故问题,该文基于车路协同理论,对下穿地道积水监测、风险预警、交通诱导和车辆管控开展研究。以四分位数法分析车辆安全涉水高度数据,合理划分下穿地道积水深度等级。基于积水划分等级,结合现有道路交通设施,研究下穿地道积水安全预警防护系统。实现下穿地道积水条件下的交通自动预警与管控,可以有效预防高风险等级下车辆误入下穿地道造成安全事故,提升恶劣环境下的交通安全管理水平。

基于宏观基本图的交通控制有效性与路网稳定性研究

区域城市路网在不同控制方法下呈现的网络交通流输出水平与维持能力不尽相同,要评价控制效果的优劣,需要分析区域控制方式下路网交通流与路网耦合时所体现出来的特征值。作为一种路网的基本属性,选择宏观基本图作为研究区域交通控制有效性与路网稳定性的评价载体,解析了相对于路段、交叉口等单点评价具有的优势。基于MFD的区域交通控制评价不受路网交通需求变化的影响。研究建立了路网最大加权流量、最佳累计车辆数、路网负载能力、交通流分布均衡度以及时序均衡度5评价指标,以对区域交通信号控制方法的有效性与路网运行的稳定性进行评价,解读路网高峰时段的交通流维持能力。以成都青羊区太升南路附近37个信号交叉口为路网,利用VISSIM二次开发技术,获得了该路网在定时控制、感应控制、自适应控制以及自适应协调控制下的路网宏观基本图形态。通过对比各控制方法的宏观基本图形态特征与评价指标,从多个维度比较了各控制方法的特征与适用性,分析了相应的控制有效性,解读了其从平峰至过饱和各阶段的交通流维持能力,进而可以了解特定控制方法下路网的运行效率。同时,根据相应控制方法的稳定性表现,指出了各自的局限性,有助于在实际应用中根据区域路网面临的环境,分析应采取的交通控制策略。

市政道路平面交叉口优化设计与评价方法研究

城市平交口作为市政道路的重要组成部分,具备沿线行人、机动车等交通转向、分合流等功能。但是,目前城市基础设施建设水平无法匹配迅猛增长的道路交通需求,再加上交通管理措施不完善,平交口交通事故发生率显著增加,给社会造成了损失。为了提高市政道路的通行效率且保证道路的安全性,科学设计平交口至关重要。文章首先分析了平面交叉口存在的问题,进而研究了平面交叉口优化设计要点,最后根据工程实例对平交口的优化设计和评价方法进行了具体分析。

城市道路交通工程施工过程中的质量控制措施

城市道路交通工程施工质量的提高涉及多方面因素,包括材料选择、施工工艺、质量监管和管理水平等。在实际工程实施过程中,常常面临材料质量不达标、施工工艺不规范、监管不到位及缺乏专业管理人员等问题,这些问题直接影响到道路工程的安全性、耐久性和经济性。因此,通过科学合理的质量控制措施和现代化管理技术的应用,是确保城市道路交通工程质量和安全的关键措施。

城市道路交通工程施工管理过程中的质量控制措施

城市道路交通工程是现代城市建设的重要组成部分,其施工质量直接关系到城市交通的顺畅和市民的出行安全。文中从施工管理的角度,探讨在城市道路交通工程施工过程中,如何实施有效的质量控制措施,以确保工程质量达到预期目标。文中将分析当前城市道路交通工程施工质量管理中存在的问题,并提出针对性的质量控制策略。

城市道路交通工程全过程质量控制

随着城市人口和车辆数量的快速增长,城市道路交通网络面临着日益复杂的管理和运营挑战。传统的道路建设和管理模式已经难以满足现代城市化进程中对交通流畅和安全的高要求。基于此,全过程质量控制作为一种系统化管理方法,不仅关注工程建设过程中的每个环节质量,还强调在工程运营阶段持续保障道路设施的安全性和功能性。通过引入现代化技术手段和加强人员培训与管理,可以有效提升工程质量和管理效率,推动城市道路交通工程朝着更加安全、高效和可持续的方向发展。

针对大规模复杂车流的PG强化交通灯控制算法

优化城市道路中的交通信号灯控制是低成本地提升城市交通路网性能的方法之一。该研究提出了一种利用策略梯度(Policy Gradient, PG)强化调优的交通灯控制算法。该算法引入了道路压力项、旅程时间项和黑名单机制项,利用统计方式预测汽车行程轨迹,并采用策略梯度估计的优化算法调整算法中的参数。在数据挖掘国际会议Knowledge Discovery and Data Mining (KDD)组织的算法竞赛KDD Cup 2021城市大脑挑战赛中,获得了冠军的成绩。在该挑战赛提供的城市路网规模复杂车流仿真平台上的实验结果表明,算法具有应用于实际场景的价值。

交通噪声的污染防治技术分析

近年来,随着我国经济的快速增长,城市化进程加速,居民的出行需求显著增加。这一变化带来了交通噪声污染问题,对居民的日常生活和学习环境造成了不小的影响。为了促进经济与社会的和谐发展,必须采取有效的污染防治措施,以改善和提升人们的生活质量。分析交通噪声污染的防治技术,探讨如何从多角度出发,采取综合措施,以期达到优化生活环境、推动社会可持续发展的目标。

城市道路两侧住宅声环境研究

城市道路交通噪声对道路两侧住宅的影响日益严重,临街交通噪声污染已成为市区环境中的热点问题。通过对南京市某快速路交通噪声及其两侧住宅小区声环境进行监测,分析道路噪声、小区声环境和住宅室内声环境现状。结果表明:道路噪声受交通量干扰严重,且存在严重的噪声超标情况;临街小区室外声环境状况恶劣,距小区围挡60 m范围内声环境严重超标;临街侧面向道路开设洞口的房间噪声污染最为严重。最后探讨降低和防治噪声的有效措施,为临街住宅噪声污染防治提供参考。

基于边缘计算的交叉口信号自动控制

交叉口信号控制需及时响应交通流量的变化,特别是在高峰时段,需要快速、准确地进行调整,以确保交通顺畅.为此,提出基于边缘计算的交叉口信号自动控制方法.该方法利用边缘计算预测交通交叉路口的车流量.并以最小化车辆延误时间及强化通行能力为目标,引入二流理论描述研究区域路网的服务水平和拥堵状态,建立交通路段的流量-密度模型,进而完成信号灯自动控制.实验结果表明:提出的方法能够准确预测交叉路口的车流量,并有效缩短车辆的排队长度,能够对交叉路口的信号灯实现有效控制.

A Model with Traffic Routers, Dynamically Managing Signal Phases to Address Traffic Congestion in Real Time

On-road Vehicular traffic congestion has detrimental effect on three lifelines: Economy, Productivity and Pollution (EPP). With ever increasing population of vehicles on road, traffic congestion is a major challenge to the economy, productivity and pollution, notwithstanding continuous developments in alternative fuels, alternative sources of energy. The research develops accurate and precise model in real time which computes congestion detection, dynamic signaling algorithm to evenly distribute vehicle densities while ensuring avoidance of starvation and deadlock situation. The model incorporates road segment length and breadth, quality and achievable average speed to compute road capacity. Vehicles installed with GPS enabled devices provide their location, which enables computing road occupancy. Road occupancy is evaluated based on number of vehicles as well as area occupied by vehicles. Ratio of road occupancy and road capacity provides congestion index important to compute signal phases. The algorithm ensures every direction is serviced once during a signaling cycle ensuring no starvation. Secondly, the definition of minimum and maximum signal timings ensures against dead lock situation. A simulator is developed to validate the proposition and proves it can ease congestion by more than 50% which is better than any of the contemporary approaches offering 15% improvement. In case of higher congestion index, alternate routes are suggested based on evaluation of traffic density graphs for shortest route or knowledge database. The algorithm to compute shortest route is optimized drastically, reducing computation cost to 3*√2N vis-à-vis computation cost of N2 by classical algorithms. The proposal brings down the cost of implementation per traffic junction from USD 30,000 to USD 2000.

Leveraging reinforcement learning for dynamic traffic control:A survey and challenges for field implementation

In recent years,the advancement of artificial intelligence techniques has led to significant interest in reinforcement learning(RL)within the traffic and transportation community.Dynamic traffic control has emerged as a prominent application field for RL in traffic systems.This paper presents a comprehensive survey of RL studies in dynamic traffic control,addressing the challenges associated with implementing RL-based traffic control strategies in practice,and identifying promising directions for future research.The first part of this paper provides a comprehensive overview of existing studies on RL-based traffic control strategies,encompassing their model designs,training algorithms,and evaluation methods.It is found that only a few studies have isolated the training and testing environments while evaluating their RL controllers.Subsequently,we examine the challenges involved in implementing existing RL-based traffic control strategies.We investigate the learning costs associated with online RL methods and the transferability of offline RL methods through simulation experiments.The simulation results reveal that online training methods with random exploration suffer from high exploration and learning costs.Additionally,the performance of offline RL methods is highly reliant on the accuracy of the training simulator.These limitations hinder the practical implementation of existing RL-based traffic control strategies.The final part of this paper summarizes and discusses a few existing efforts which attempt to overcome these challenges.This review highlights a rising volume of studies dedicated to mitigating the limitations of RL strategies,with the specific aim of enhancing their practical implementation in recent years.

Study on Optimization of Earthquake Emergency Traffic Scheduling Based on The Lifting of Cyberspace

How to improve the rescue efficiency of the network after the earthquake is the key content of emergency management decision-making, improve the efficiency of emergency rescue, and reduce the impact of emergency rescue to the non victims. Using cyberspace of lifeline network traffic and emergency transportation problem, considering the network restoration problem of disaster area, earthquake emergency supplies distribution model is established. In the model, to consider the need to repair the damaged sections and the existing emergency rescue generated traffic volume of emergency rescue network effects. And design heuristic algorithm for solving the model. Finally the example shows that in emergency rescue, emergency rescue of critical damage repair the road and traffic control of the whole lifeline network rescue efficiency highest, with the average nuisance greatly reduce, the lifeline network connectivity reliability.

An Urban Traffic Monitoring System Based on the ArcGIS Engine

The growing number of vehicles makes traffic jams and accidents significant problems. Making people get to know the real-time road condition can mitigate the effect of congestions greatly, but this is not supported by traditional traffic assistant systems. The intelligent traffic system is born to settle these problems. By making full use of the ArcGIS （Arc Geographic Information System） Engine characteristics, this paper designs and imple- ments an urban traffic monitoring system. The main functions of the system include the real-time road condition information display, layer-control, supervisory control management and the basic operations of a map. With the data collected by monitors deployed in intersections, different road conditions are calculated and shown with dif- ferent colors on the map and users can choose suitable roads to get away from the traffic congestion; meanwhile it can offer a reference for a traffic management department to make decisions on traffic control. The system has been deployed and shows high practicability and reliability in practical use.

基于数字孪生的道路交叉口建模与应用

为有效调控路网交通状态,建立了一种道路交叉口交通数字孪生管理系统。从理论方面和模型的构建、组装、融合、管理、验证及校正等6个方面,阐述了该模型的核心要素及模型构建理论体系;从交通要素管理和运行过程控制2个方面,分析了模型的运行机制和人、车、路、环境的实时交互关键技术;基于机器视觉,实时识别交叉口的交通要素及运行状态;分析“以虚控实”管理系统的构建技术。研究表明:该方法能对道路交叉口的交通要素及运行状态实现精确识别与仿真,可望为相关部门高效调节路网交通状态创建数字化窗口及为自动驾驶提供数据支持。