Providing personalized system optimum traveler information in a congested traffic network with mixed users

The advancement of information and communication technology allows the use of more sophisticated information provision strategies for real-time traffic management in a congested network. This article proposes a personalized system optimum traveler information (PSOI) system under ubiquitous communication, which allows traffic system operators to fully optimize and coordinate individuals' trip plans according to the personal attributes, such as real-time location, value of time, allowable budgets for congestion tolling, and willingness to take detours. We also developed an efficient queue-based evaluation and solution heuristic algorithm using mesoscopic simulation models to solve for near-optimal PSOI strategies—route suggestions for each individual traveler. The simulation optimization algorithm can account for different information users and provide predictive information that robustly accounts for potential decisions of other travelers in real time. Case studies were carried out on a test network and a real-world network, and the proposed heuristic algorithm is proven effective. Also, sensitivity analyses show that PSOI not only is an effective traffic management method in reducing average system travel time, but also potentially provides travelers with reasonable or even shorter travel times compared with other information users. Further, simulation results showed that even in mixed traffic, PSOI is able to shorten travel times for both users without information and users of other information types. Thus, PSOI is recommended by this article as an advantageous way for next-generation advanced information systems and dynamic traffic management.     

Robust traffic assignment model: Formulation,solution algorithms and empirical application

ABSTRACT

The deterministic traffic assignment problem based on Wardrop's first criterion of traffic network utilization has been widely studied in the literature. However, the assumption of deterministic travel times in these models is restrictive, given the large degree of uncertainty prevalent in urban transportation networks. In this context, this paper proposes a robust traffic assignment model that generalizes Wardrop's principle of traffic network equilibrium to networks with stochastic and correlated link travel times and incorporates the aversion of commuters to unreliable routes.

The user response to travel time uncertainty is modeled using the robust cost (RC) measure (defined as a weighted combination of the mean and standard deviation of path travel time) and the corresponding robust user equilibrium (UE) conditions are defined. The robust traffic assignment problem (RTAP) is subsequently formulated as a Variational Inequality problem. To solve the RTAP, a Gradient Projection algorithm is proposed, which involves solving a series of minimum RC path sub-problems that are theoretically and practically harder than deterministic shortest path problems. In addition, an origin-based heuristic is proposed to enhance computational performance on large networks. Numerical experiments examine the computational performance and convergence characteristics of the exact algorithm and establish the accuracy and efficiency of the origin-based heuristic on various real-world networks. Finally, the proposed RTA model is applied to the Chennai road network using empirical data, and its benefits as a normative benchmark are quantified through comparisons against the standard UE and System Optimum (SO) models.     

观测路径出行时间下随机网络交通需求估计

道路网络起讫点（OD）需求是城市决策长期交通规划和短期交通管理中的基础参数,准确的交通需求更是实施交通拥堵控制、限行限速、路径诱导等措施的先决条件。综合运用观测的轨迹已知和未知路径出行时间,建立随机网络交通需求估计双层规划模型。上层广义最小二乘模型最小化历史交通需求与待估交通需求、观测路径出行时间与待估路径出行时间之间的偏差,约束为交通需求、路段流量、路段出行时间与路径出行时间之间的传播关系,通过高斯混合模型（GMM）对其中轨迹未知的观测出行时间依概率聚类。下层为随机网络交通出行均衡模型,分别运用出行时间预算和随机用户均衡处理路网不确定性和出行者感知误差。上、下层之间通过交通需求和OD-路段关联比例进行信息传递。设计迭代算法框架求解双层规划模型,迭代算法包含求解上层模型的最速下降法、求解下层模型的相继平均算法和求解GMM模型的最大期望（EM）算法。通过算例表明轨迹未知的路径出行信息的加入在提升需求估计精度的同时也增大了估计值的方差;设计的迭代算法能够稳定收敛到10^-5的精度;GMM软聚类方法估计的交通需求显著优于硬聚类方法估计的需求值;交通需求值对观测路径出行时间的扰动更加敏感。研究考虑出行者风险态度,通过轨迹信息的重新构建揭示城市交通需求演化规律。     

出行者有限理性条件下混合策略网络均衡模型

将道路交通网络出行时间连续随机分布条件下的出行用户交通出行作为研究对象,结合我国交通信息系统逐步普及的背景,按其所采用出行策略的不同划分出行用户,将出行者有限理性的特点引入到出行者路径效用函数当中,以累积前景理论为基础,建立出行者的路径感知效用函数.结合Wardrop原理和变分不等式思想建立多类型用户的网络均衡模型,通...     

基于多智能体博弈的路径选择策略仿真研究

系统最优与用户最优代表了交通分配中路网管理者与出行者两种不同的利益出发点。在综合考虑两者在路径选择过程中动态交互特点的基础上,引入博弈论的思想协调两者的利益冲突,建立了路网系统管理者与出行者之间的路径选择博弈模型。为验证模型的有效性,结合多智能体技术进行了相应的仿真实验,并利用多智能体仿真软件Starlogo进行模拟。通过对无信息无博弈（随机）出行、用户最优出行、有信息有博弈出行以及系统最优出行等4种不同仿真方案的比较分析,验证了系统与出行个体之间协调的博弈模型性能满足了驾驶员出行需求,提高了路网整体效率,为建立实用的诱导策略提供了参考。     

流量和信号控制对运行时间预测的影响

通过对实际交通状况的分析，说明路线运行时间不仅是路线交通流量的函数，而且是由于交叉口信号控制引起延误的函数。     

信息对出行时间的影响研究

在一个不确定性交通网络上,考虑不同风险厌恶程度出行者的出行时间受交通信息的影响,采用出行时间及对出行时间不确定性的偏爱程度建立期望效用函数,研究信息环境下与非信息环境下的期望出行时间,得到如下结论:(1)信息的提供将减少所有出行者的期望出行时间;(2)在信息环境下,风险厌恶程度高的出行者期望出行时间的减少比风险厌恶程度低的出行者期望出行时间减少更显著。并用算例对模型结论加以验证。     

基于计划延误成本的出发时间选择模型

出行时间不确定性对出行者造成诸多负面影响,其中之一就是计划延误成本增加.向出行者提供交通信息可减少出行时间不确定性的负面影响,减少计划延误成本.为了估计其负面效应和出行时间信息的价值,研究了一个出行时间不确定性下的出发时间选择模型.假定在不同的出发时间下,出行时间分布的形式不同.模型中出行者可得到交通信息服务系统(A T IS)提供的基于平均出行状况的交通信息或基于当日出行状况的交通信息,同时他对得到的信息有自己的感知值,基于此感知预测出行时间并选择出发时间.用算例反映了出行时间不确定性、不同信息状况和信息预测的质量对出行者广义出行阻抗的影响.结果表明交通信息的收益不应该只表现为传统的出行时间节省,它还能减少约30% ~40% 的计划延误成本.      

Bi-objective routing problem with asymmetrical travel time distributions

Recent studies have confirmed that travelers consider travel time reliability in addition to average travel time when making route choice decisions. In this study, we develop a bi-objective routing model that seeks to simultaneously optimize the average travel time and travel time reliability. The semi-standard deviation (SSD) is chosen as the reliability measure because it reflects travelers' concerns over longer travel time better than the commonly used standard deviation. The Pareto-optimal solutions to the bi-objective model are found by using an improved strength Pareto evolutionary algorithm. Tests on a real-world urban network with field measured travel time data have demonstrated good performance of the algorithm in the aspects, such as computational efficiency, quick convergence, and closeness to the global Pareto-optimal. Overall, the bi-objective routing model generates reasonable path recommendations. The SSD-based model is sensitive to the asymmetry of travel time distribution and tends to avoid paths with excessively long delays. This would be particularly helpful to those users placing high values on travel time reliability.     

基于可持续发展的交通管理模式研究

对欧美等国采取的交通管理理论及方法进行了摘编,阐述了当前如何应用交通需求管理（Travel Demand Management,TDM）合理引导需求,以及从长远角度考虑如何整合土地利用与交通规划（Integrating land use and transport）,从而实现交通管理的可持续发展,为相关工作提供参考。     

供需不确定条件下的预算-超额用户平衡模型

为更加全面、准确反映随机路网中出行者规避风险的择路行为,以预算-超额行程时间作为出行者选择路径的依据,提出了一种供应及需求不确定条件下同时考虑可靠性和不可靠性的交通分配模型——预算-超额用户平衡模型,推导了需求服从Gamma分布、路段通行能力服从均匀分布条件下预算-超额行程时间的解析表达式,并以此为基础建立起用等价变分不等式表示的平衡模型。利用一个小型测试网络比较了用户平衡模型、基于可靠性的用户平衡模型以及预算-超额用户平衡模型的性能。研究结果表明：提出的模型是有效、可行的;其平衡流量模式不同于用户平衡模型和基于可靠性的用户平衡模型;随着需求水平、可靠度以及路段通行能力退化程度的增加,预算-超额行程时间随之增加。     

运行时间可靠度在单向交通组织中的应用

运用网络可靠性计算的串并联理论,提出了道路网络中节点OD（Origin-Destination）对之间的路径运行时间可靠度计算方法,建立了基于节点OD需求的路网运行时间可靠度计算模型。在节点OD需求已知的情况下,根据交通组织状况,采用动态交通分配和交通仿真方法获得模型参数,计算节点OD对之间分别在双向和单向交通组织条件下的运行时间可靠度,并建立交通组织方案的临界判别条件,作为单向交通组织方案评价和决策的量化指标。     

深港西部通道过境交通量预测的比较研究

过境交通量预测是深港西部通道可行性研究阶段的重要工作，也是确定口岸设计交通量的基础。过境交通量预测的影响因素比较复杂，深港双方为此开展了独立的交通调查，研发了各自的交通需求模型。双方的预测结果存在一定差异，深港双方专家组协商确定了最终的预测交通量。对于未来跨界通道的设计具有一定的启发意义。     

拥挤道路收费与停车收费组合优化模型研究

道路拥挤收费和停车收费是交通需求管理中的两大有效手段,合理地实施收费可以达到缓解交通拥挤的目的.文中针对交通网络中私家车用户可以在任意公交站点处发生换乘这一广泛情形,讨论了弹性需求下的路段拥挤收费和停车收费组合优化策略,并给出基于混沌优化方法的启发式算法求解所建立的双层规划模型.结果表明,所建模型有助于个体出行方式和出行路径的决策,且组合收费在使得公众易于接受实施拥挤收费政策的同时,也可以吸引更多出行者选择换乘公共交通方式,这将有利于减轻交通拥挤程度和环境压力.     

一种新的城市快速路行程时间预测技术

根据城市快速路交通诱导和监控系统的实际需要,提出了基于宏观动态流体力学模型的行程时间预测技术,可以动态预测稳定流和非稳定流状况下城市快速路网上任意两点间行程时间.     

深入挖掘路网时空资源 科学保障道路安全畅通

以南京市道路交通为研究对象,从交通供需矛盾入手分析城市交通现状,对交通拥堵形成的根源进行剖析。通过对交通管理理论与安全保畅经验进行总结,以建设"十二五"、服务青奥会的整体战略规划为出发点,从城市发展模式、交通系统管理、交通需求管理、交通秩序管理等方面入手,合理配置路网时空资源以科学保障道路安全畅通,提出适合南京城市交通发展的对策建议。     

基于出行时间预算的多用户交通分配模型及算法

通过分析在满足一定可靠性要求条件下出行者追求总预期出行时间最小的择路行为,定义交通网络的惯常均衡态。以不同的出行者会增加相异的出行时间预算为假设前提,建立相应的多用户变分不等式交通分配模型。这一模型不仅具有不依赖具体路阻函数形式的一般化建模特征,而且避免了现有文献中必须处理非凸约束集的问题,因此可以对模型的解进行定性分析。针对该模型,提出相应的求解算法。由于求解算法仅需对现有的网络交通分配算法加以局部修改即可实现,因此可被应用于实际规模的网络分析。通过算例结果的比较分析说明新模型及算法不仅有效,而且更符合现实。     

Reliability-based network flow estimation with day-to-day variation: A model validation on real large-scale urban networks

Day-to-day variation in the travel times of congested urban transportation networks is a frustrating phenomenon to the users of these networks. These users look pessimistically at the path travel times, and learn to spend additional time to safeguard against serious penalties that await late arrivals at the destinations. These additional expenses are charges similar to the tolls in system equilibrium flow problem, but may not be collected. With this conjecture, the user equilibrium (UE) formulation of congested network flow problem would lack some necessary factors in addressing appropriate path choices. This study, following a previous work proposing pessimistic UE (PUE) flow, aims to show how to measure this additional travel cost for a link, and investigates how different is PUE from UE, and when such differences are pronounced. Data are collected from the peak-hour travel times for the links of paths in the city of Tehran, to estimate the variance of travel times for typical links. Deterministic functions are obtained by calibrating the standard deviation of the daily variations of link travel times, and probabilistic functions by the technique of copula. UE and PUE traffic assignment models are built and applied to three large cities of Mashhad, Shiraz, and Tehran in Iran. The results show that the estimated flows by PUE model replicate the observed flows in screen lines much better than the UE model, particularly for longer trips. Since PUE is computationally equivalent to UE, this improvement is attained virtually at no cost.     

共享无人驾驶交通系统调度技术综述

众多研究证明共享无人驾驶车辆（SAVs）交通系统，能够实现车辆完全可控并为乘客提供个性化服务，即克服导致共享单车大量倒闭的运维和调配难题，也可消除制约公交竞争力的准时性、舒适性、覆盖性差的不足，有望成为未来城市交通的主体出行模式之一，并在很大程度上解决交通拥堵、环境污染、能源紧缺等城市病。基于此，总结SAVs交通系统相对传统共享车辆系统在不同运行模式方面的优点及不足，综述SAVs交通系统的调度技术。从SAVs交通系统运行过程出发，总结并分析SAVs交通系统不同运行方式，包括SAVs交通系统出行需求产生方式、车辆与需求匹配方式、车辆路径规划与车辆再平衡方式，以及运行方式与系统参数发生变化时对车辆行驶里程、车辆行驶时间、车队规模、乘客等待时间和服务水平等系统性能的积极与消极影响。对现有成果的梳理发现对于SAVs交通系统运行与性能的研究虽然取得了很大进展，但对于选择SAVs的出行需求计算多未考虑需求转移的影响，能够有效在大型路网中使用的系统模型与算法依然缺乏，且对于SAVs交通系统的调度研究考虑的形式可以更加多样，使之更符合现实情况。最后，基于提高SAVs系统性能对未来研究方向进行了展望，提出一些可能的研究课题。研究成果可为SAVs交通系统的研究提供参考。     

Optimizing multi-agent based urban traffic signal control system

Agent-based approach is a popular tool for modelling and developing large-scale distributed systems such as urban traffic control system with dynamic traffic flows. This study proposes a multi-agent-based approach to optimize urban traffic network signal control, which utilizes a mathematical programming method to optimize the signal timing plans at intersections. To improve the overall network efficiency, we develop an online agent-based signal coordination scheme, underpinned by the communication among different intersection control agents. In addition, the initial coordination scheme that pre-adjusts the offsets between the intersections is developed based on the historical demand information. Comparison and sensitivity analysis are conducted to evaluate the performance of the proposed method on a customized traffic simulation platform using MATLAB and VISSIM. Simulation results indicate that the proposed method can effectively avoid network oversaturation and thus reduces average travel delay and improves average vehicle speed, as compared to rule-based multi-agent signal control methods.