Behaviours in a dynamical model of traffic assignment with elastic demand

This paper investigates the dynamical behaviour of network traffic flow. Assume that trip rates may be influenced by the level of service on the network and travellers are willing to take a faster route. A discrete dynamical model for the day-to-day adjustment process of route choice is presented. The model is then applied to a simple network for analysing the day-to-day behaviours of network flow. It finds that equilibrium is arrived if network flow consists of travellers not very sensitive to the differences of travel cost. Oscillations and chaos of network traffic flow are also found when travellers are sensitive to the travel cost and travel demand in a simple network.

Research on traffic congestion mechanism and countermeasures based on dynamic traffic assignment

Traffic congestion is widely distributed around a network. Generally, to analyze traffic congestion, static traffic capacity is adopted. But dynamic characteristics must be studied because congestion is a dynamic process. A Dynamic Traffic Assignment modeling fundamental combined with an urban congestion analysis method is studied in this paper. Three methods are based on congestion analysis, and the stochastic user optimal DTA models are especially considered. Correspondingly, a dynamic system optimal model is suggested for responding congestion countermeasures and an ideal user optimal model for predicted congestion countermeasure respectively.

A DYNAMIC MODEL OF MULTIPLE PATH TRAFFIC ASSIGNMENT

A “Random Shortest Path”traffic assignment model and its algorithm arepresented by simulating the trip-makers’route-choice characters,and the dynamic meth-od is introduced in the assignment model.It is a ideal multiple path assignment modelwhich can be carried out by the dynamic method and static method,can better reflect boththe shortest path factor and the random factor in the route-choice,and is of reasonableassignment volumes.Besides,both dynamic and static softwares particularly suited to thetraffic assignment of large and medium-sized transportation networks arc developed.

Development of a Methodology to Evaluate Projects Using Dynamic Traffic Assignment Models

The identification and selection of performance measures play an important role in any decision making process. Additionally, millions of dollars are spent on appropriate planning and identification of prospective projects for improvements. As a result, current practitioners spend a lot of time and money in prioritizing their limited resources. This research proposes two tasks: 1) estimation of performance measures using a simulation based on dynamic traffic assignment model, and 2) development of a methodology to evaluate multiple projects based on benefit-cost analysis. The model, DynusT, is used for the Las Vegas roadway network during the morning peak time period. A comparative analysis of the results from proposed methodology with existing California Benefit-Cost (Cal-B/C) models is presented. The results indicate that the new methodology provides an accurate benefit-cost ratio of the projects. In addition, it signifies that the existing Cal-B/C models underestimate the benefits associated with the prospective project improvements. The major contribution of this research is the simultaneous estimation of the performance measures and development of a methodology to evaluate multiple projects. This is helpful to decision makers to rank and prioritize future projects in a cost-effective manner. Planning and operational policies for the transportation systems can be developed based on the gained insights from this study.

A Temporal Domain Decomposition Algorithmic Scheme for Large-Scale Dynamic Traffic Assignment

With emergent interest of Simulation-Based Dynamic Traffic Assignment(SBDTA)in the field of transportation network modeling,deployment of SBDTA models for traffic operations and transportation planning have increased significantly in recent years.In parallel,research and development of innovative approaches of the SBDTA model have enhanced the quality of both the assignment component,i.e,improvement of convergence quality of the Dynamic User Equilibrium(DUE)problem,and the traffic simulation element.However,computational requirement remains to be one of the great challenges for DTA implementations on large-scale networks with a long analysis period.This paper presents a temporal decomposition scheme for large spatial-and temporal-scale dynamic traffic assignment,in which the entire analysis period is divided into Epochs.Vehicle assignment is performed sequentially in each Epoch,thus improving the model scalability and confining the peak run-time memory requirement regardless of the total analysis period.A proposed self-turning scheme adaptively searches for the run-time-optimal Epoch setting during iterations regardless of the characteristics of the modeled network.Extensive numerical experiments confirm the promising performance of the proposed algorithmic schemes.

Bus frequency optimization in a large-scale multi-modal transportation system:integrating 3D-MFD and dynamic traffic assignment

A properly designed public transport system is expected to improve traffic efficiency.A high-frequency bus service would decrease the waiting time for passengers,but the interaction between buses and cars might result in more serious congestion.On the other hand,a low-frequency bus service would increase the waiting time for passengers and would not reduce the use of private cars.It is important to strike a balance between high and low frequencies in order to minimize the total delays for all road users.It is critical to formulate the impacts of bus frequency on congestion dynamics and mode choices.However,as far as the authors know,most proposed bus frequency optimization formulations are based on static demand and the Bureau of Public Roads function,and do not properly consider the congestion dynamics and their impacts on mode choices.To fill this gap,this paper proposes a bi-level optimization model.A three-dimensional Macroscopic Fundamental Diagram based modeling approach is developed to capture the bi-modal congestion dynamics.A variational inequality model for the user equilibrium in mode choices is presented and solved using a double projection algorithm.A surrogate model-based algorithm is used to solve the bi-level programming problem.

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.

Dynamical evolution processes of traffic flow and travel cost in urban transportation networks

Considering such a fact that travellers dynamically adjust their routes and the resultant link traffic flows in a network evolve over time, this paper proposes a dynamical evolutionary model of the traffic assignment problem with endogenous origin-destination （OD） demands. The model＇s stability is analysed and the resultant user equilibrium （UE） state is shown to be stable under certain conditions. Numerical results in a grid network indicate that the model can generate convergent flow patterns and finally terminates at the UE state. Impacts by the parameters associated with OD demand function and link cost function are also investigated.

基于DTA的OD估计方法的交通检测器优化布置模型研究

论文在探讨了动态交通分配和动态OD估计背景下的交通检测器优化问题的基础上,提出了基于DTA的动态OD估计方法的交通检测器布置原则;从预算,对路网中交通流量信息的覆盖程度,对关键路段的检测、对重复检测器的剔除等方面对路网交通检测方案进行约束,建立了交通检测器优化布置模型;最后将遗传算法用于交通检测器优化布置模型的求解,证明了基于DTA的动态OD估计方法的交通检测器优化布置模型的有效性。论文方法概念清楚、操作简单,是交通检测器优化布置的一种可行方法。

实时动态交通分配(DTA)模型评价

回顾了实时动态交通分配模型的相关文献 ,提出它们的公共结构并用来分析评价一些有代表性的模型。分析表明现有的模型存在一些局限性 ,一个新的或改进的DTA模型是需要的。

Strategic flight assignment approach based on multi-objective parallel evolution algorithm with dynamic migration interval

The continuous growth of air traffic has led to acute airspace congestion and severe delays, which threatens operation safety and cause enormous economic loss. Flight assignment is an economical and effective strategic plan to reduce the flight delay and airspace congestion by rea- sonably regulating the air traffic flow of China. However, it is a large-scale combinatorial optimiza- tion problem which is difficult to solve. In order to improve the quality of solutions, an effective multi-objective parallel evolution algorithm （MPEA） framework with dynamic migration interval strategy is presented in this work. Firstly, multiple evolution populations are constructed to solve the problem simultaneously to enhance the optimization capability. Then a new strategy is pro- posed to dynamically change the migration interval among different evolution populations to improve the efficiency of the cooperation of populations. Finally, the cooperative co-evolution （CC） algorithm combined with non-dominated sorting genetic algorithm II （NSGA-II） is intro- duced for each population. Empirical studies using the real air traffic data of the Chinese air route network and daily flight plans show that our method outperforms the existing approaches, multi- objective genetic algorithm （MOGA）, multi-objective evolutionary algorithm based on decom- position （MOEA/D）, CC-based multi-objective algorithm （CCMA） as well as other two MPEAs with different migration interval strategies.

Integration control for traffic corridors considering guidance information

The problem of designing integration traffic strategies for traffic corridors with the use of ramp metering, speed limit, and route guidance is considered in this paper. As an improvement to the previous work, the presented approach has the following five features： 1） modeling traffic flow to analyze traffic characteristics under the influence of variable speed limit, on-ramp metering and guidance information; 2） building a hierarchy model to realize the integration design of traffic control and route guidance in traffic corridors; 3） devising a multi-class analytical dynamic traffic assignment （DTA） model for traffic corridors, where not only the route choice process will be different for each user-class, but also the traffic flow operations are user-class specific because the travel time characteristic for each user-class is considered; 4） predicting route choice probabilities adaptively with real-time traffic conditions and route choice behaviors corresponding to variant users, rather than assuming as pre-determined; and 5） suggesting a numerical solution algorithm of the hierarchy model presented in this paper based on the modified algorithm of iterative optimization assignment （IOA）. Preliminary numerical test demonstrates the potential of the developed model and algorithm for integration corridor control.

基于Dynameq软件的中观动态交通仿真实现方法

宏观交通模型主要用于研究大范围城市与交通规划和建设引起的中长期交通需求变化,而微观交通仿真适用于研究小范围高精度和基于个体的动态交通分析评估和可视化展示。随着基于多源大数据的大范围内动态交通分析技术逐步成熟,能较好的满足城市精准化量化分析预测的需要。对中观动态交通仿真技术涉及的关键数据和技术方法进行梳理,研究基于Dynameq软件进行中观动态交通仿真的流程和方法,为完善城市宏观—中观—微观一体化交通模型体系、提升城市交通量化分析精度和效率提供思路,并以示范区域为仿真对象进行验证。

面向动态交通分配的交通需求深度学习预测方法

为满足动态交通分配对高精度、高时效性交通需求的要求,本文建立了一种交通需求深度学习预测方法。根据动态交通分配要求确定交通需求数据的时间间隔,构建对复杂交通需求预测性能较优的长短期记忆神经网络预测方法;针对动态交通分配中交通需求的周期性、随机性和非线性等特征,为减少数据噪声的干扰,引入局部加权回归周期趋势分解方法将交通需求数据分解,将其中的趋势分量和余项分量作为深度学习预测方法的输入量,周期分量采用周期估计进行预测;选用具有随机寻优能力强、寻优效率高等特点的布谷鸟寻优算法优化预测方法的隐藏层单元数量、学习速率和训练迭代次数等核心参数。应用西安市长安区的卡口车牌数据验证该方法。结果表明:本文模型的预测结果在高峰及平峰各连续4个时段内相比于自回归滑动平均模型、长短期记忆神经网络模型、支持向量回归模型,平均绝对误差降低了10.55%~19.80%,均方根误差降低了11.20%~17.99%,决定系数提升了8.62%~12.48%;相比遗传算法、粒子群算法优化的模型,平均绝对误差降低了7.36%~13.81%,均方根误差降低了4.23%~10.67%,决定系数提升了3.50%~7.01%,且本文模型运行时间最短。说明与对比模型相比,本文所建立的预测方法在面向动态交通分配的交通需求预测中具有更高的预测精度。

考虑电力-交通耦合网动态协调的EV集群灵活性挖掘与优化调度

电动汽车(EV)作为兼具交通与能量双重属性的跨域主体,发挥其时空灵活性将助力电力-交通耦合网的协调运行。为此,文中考虑电力-交通耦合网综合效益,提出了EV集群调控策略。首先,基于弧阻抗函数构建了动态交通网络加载模型。其次,考虑EV用户的各特征参数存在的差异性与耦合性,构建了单体EV灵活运行域模型,并采用基于zonotope线性近似的闵可夫斯基和算法对其聚合,得到EV集群的时变灵活运行域。在此基础上,提出了动态交通流最优分配下EV集群灵活性调控的双层模型,迭代求解得到上下两层耦合变量构成的瞬时单位流量出行成本,从而引导EV的出行与充放电行为。最后,通过与最短路径引导策略进行比较,验证了所提EV集群调控策略的有效性。

随机用户均衡准动态交通分配模型

动态交通分配问题是交通科学研究的热点和难点,将动态交通分配方法应用于大规模网络往往产生过高的计算成本。通过研究准动态交通分配问题,降低动态交通分配中时间的连续性和动态交通分配模型的复杂性。采用路段行程时间计算公式定义一种新的剩余交通需求计算方法;然后考虑剩余交通需求在时段之间的传播过程和出行者对路网的熟悉程度差异,建立基于Logit的随机用户均衡准动态交通分配模型,基于逐次平均法设计模型的求解算法。最后通过Braess网络和九节点网络说明模型的应用情况,并分析模型参数的敏感度,验证模型的合理性与算法的有效性。本研究丰富了交通科学基础理论,为交通政策的制定提供参考依据。

对动态交通分配的反思

交通分配是交通规划中的一个重要步骤。在交通规划方法相对成熟之后， 交通界的人士逐渐把注意力更多的转向交通控制与诱导，由此提出了动 态交通分配（ＤｙｎａｍｉｃＴｒａｆｆｉｃ Ａｓｓｉｇｎｍｅｎｔ）。本文首先总括了动态交通分 配的定义及其在交通控制与诱导中的位置和作用。然后列举了目前为止 常用的几种动态交通分配模型。通过对动态交通分配模型的分析，本文认 为对于解决交通控制与诱导问题，动态交通分配在其前提和结构上存在 不可克服的障碍。最后本文从智能的角度提出了解决交通控制与诱导问

动态交通分配理论的回顾与展望

由于静态交通分配模型无法描述城市交通网络的动态交通流以及智能交通系统（ＩＴＳ）研究的推进，使得动态交通分配理论日益成为国际上的研究热点，木文就此问题的发展过程进行综述性研究，力图阐明其中的关键问题，并探讨动态交通分配理论的发展方向。

对交通控制与诱导结合研究的评述

交通控制与诱导的集成和一体化代表了现代交通管理的发展方向,是智能交通系统中的重要研究内容。而动态交通分配与交通控制的结合是控制与诱导集成的理论基础。本文回顾交通控制与交通诱导以及动态交通分配结合研究的发展,对控制为主和以诱导为主的两类系统的设计和研究思想以及结合的主要模型和求解算法进行详细的论述、总结和分析,指出研究中存在的不足和局限性,提出建立控制与诱导真正协调、一体化的研究改进的方向和思路,最后给出一个完整的控制与诱导结合研究的框架结构。

动态混合行为交通网络平衡的遗传算法求解

采用遗传算法对动态交通网络平衡微分博弈模型进行求解,将动态混合行为交通网络平衡模型构造为一个开环信息结构下N个局中人非合作非零和博弈,并考虑了一个单OD对之间有两个平行弧的简单网络和两类局中人——用户平衡(UE)和古诺-纳升(C-N)——在拥挤现象中的相互作用,针对此简单网络阐明了遗传算法求解模型的具体步骤。遗传算法求解不必要求目标函数连续可微,大大提高了模型的适用性。通过算例对所设计的算法进行了验证,在算例中将Wie BW(1993)的研究中部分参数取值作了修改,使其更加合乎交通实际,并将计算结果与Wie B W(1993)采用最小值原理计算结果进行了对比分析,通过对比分析表明,其计算结果更符合交通实际。