考虑乘客信任程度的营运车辆合乘线路规划

车辆合乘是解决交通拥堵的有效方法,然而乘客对车辆合乘行为缺乏信任是影响合乘发展的难题。针对这一问题,通过引入信任度权重和用户偏好来衡量合乘的信任水平,以车辆的总行驶距离最短以及总信任度值最高为目标函数,同时考虑了车辆搭载容量约束、车辆行驶距离约束、乘客需求响应约束以及车辆站点服务约束,构建了考虑乘客信任程度的合乘模型,然后针对该模型采用改进的遗传算法进行求解。最后采用北京市新发地周边地区的营运车辆数据进行算法验证。结果表明,该模型能够在有效减少车辆总行驶距离的同时保障较高的乘客合乘信任水平,相较于仅考虑距离优化的模型,距离成本增加了14.8%,信任水平提升了3.3倍。通过对优化结果的对比分析,验证了模型和算法的有效性。     

Agent-based approach to analyzing the effects of dynamic ridesharing in a multimodal network

An agent-based modeling for dynamic ridesharing in a multimodal network is proposed in this paper. The study aims to evaluate the performance of dynamic ridesharing system within a multimodal network and explore the competing mechanism between dynamic ridesharing and public transit, with the presence of managed lane facility. The modeling process simulates the interaction between travelers and the network, and applies a heuristic algorithm to model travelers' decision making process under uncertainty. The model is applicable to networks with varying demographics. Multiple scenarios based on the classic Sioux Falls network have been examined. The modeling results demonstrate that the effects of dynamic ridesharing on a network differ with traffic demand and market penetrations of various travel modes. In networks with high travel demand and low market penetration of public transit, the benefits of dynamic ridesharing system on reducing congestion and providing reliable travel time are quite limited. To enhance the effectiveness of dynamic ridesharing, traffic operators may consider project investments on managed lane facilities. In networks with high market penetration of public transit, dynamic ridesharing may attract large amounts of short distance trips and aggravate congestion, especially at the initial launching phase. Policy makers would want to ensure that the existing infrastructure is sufficient to accommodate the extra traffic induced by ridesharing. Ridesharing service providers might also consider proper strategies to avoid “abuse” of the system by short trips and accelerate the market penetration.     

A dynamic vehicle routing problem with time-dependent travel times

In this paper we present a formulation for the dynamic vehicle routing problem with time-dependent travel times. We also present a genetic algorithm to solve the problem. The problem is a pick-up or delivery vehicle routing problem with soft time windows in which we consider multiple vehicles with different capacities, real-time service requests, and real-time variations in travel times between demand nodes.The performance of the genetic algorithm is evaluated by comparing its results with exact solutions and lower bounds for randomly generated test problems. For small size problems with up to 10 demands, the genetic algorithm provides almost the same results as the exact solutions, while its computation time is less than 10% of the time required to produce the exact solutions. For the problems with 30 demand nodes, the genetic algorithm results have less than 8% gap with lower bounds.This research also shows that as the uncertainty in the travel time information increases, a dynamic routing strategy that takes the real-time traffic information into account becomes increasingly superior to a static one. This is clear when we compare the static and dynamic routing strategies in problem scenarios that have different levels of uncertainty in travel time information. In additional tests on a simulated network, the proposed algorithm works well in dealing with situations in which accidents cause significant congestion in some part of the transportation network.     

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.     

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.     

信号控制下的动态路段行程时间模型

通过对车辆在路段上所处的状态不同,将路段行程时间划分成多个组成部分,并分别研究各部分的计算模型,提出一种新的动态路段行程时间模型。这种新的模型计算简单,能够适用于实际交通网络中对动态路段行程时间进行预测计算。通过算例的分析表明,在信号灯的控制下,车辆的动态行程时间是一个间断函数,其不仅仅与路段流量有关,还与该车辆进入路段的时刻以及控制信号设置有很大关系。      

Dynamic Traffic Assignment with More Flexible Modelling within Links

Traffic network models tend to become very large even for medium-size static assignment problems. Adding a time dimension, together with time-varying flows and travel times within links and queues, greatly increases the scale and complexity of the problem. In view of this, to retain tractability in dynamic traffic assignment (DTA) formulations, especially in mathematical programming formulations, additional assumptions are normally introduced. In particular, the time varying flows and travel times within links are formulated as so-called whole-link models. We consider the most commonly used of these whole-link models and some of their limitations.In current whole-link travel-time models, a vehicle's travel time on a link is treated as a function only of the number of vehicles on the link at the moment the vehicle enters. We first relax this by letting a vehicle's travel time depend on the inflow rate when it enters and the outflow rate when it exits. We further relax the dynamic assignment formulation by stating it as a bi-level program, consisting of a network model and a set of link travel time sub-models, one for each link. The former (the network model) takes the link travel times as bounded and assigns flows to links and routes. The latter (the set of link models) does the reverse, that is, takes link inflows as given and finds bounds on link travel times. We solve this combined model by iterating between the network model and link sub-models until a consistent solution is found. This decomposition allows a much wider range of link flow or travel time models to be used. In particular, the link travel time models need not be whole-link models and can be detailed models of flow, speed and density varying along the link. In our numerical examples, algorithms designed to solve this bi-level program converged quickly, but much remains to be done in exploring this approach further. The algorithms for solving the bi-level formulation may be interpreted as traveller learning behaviour, hence as a day-to-day traffic dynamics. Thus, even though in our experiments the algorithms always converged, their behaviour is still of interest even if they cycled rather than converged. Directions for further research are noted. The bi-level model can be extended to handle issues and features similar to those addressed by other DTA models.     

A-RESCUE: An Agent based Regional Evacuation Simulator Coupled with User Enriched Behavior

Household behavior and dynamic traffic flows are the two most important aspects of hurricane evacuations. However, current evacuation models largely overlook the complexity of household behavior leading to oversimplified traffic assignments and, as a result, inaccurate evacuation clearance times in the network. In this paper, we present a high fidelity multi-agent simulation model called A-RESCUE (Agent-based Regional Evacuation Simulator Coupled with User Enriched behavior) that integrates the rich activity behavior of the evacuating households with the network level assignment to predict and evaluate evacuation clearance times. The simulator can generate evacuation demand on the fly, truly capturing the dynamic nature of a hurricane evacuation. The simulator consists of two major components: household decision-making module and traffic flow module. In the simulation, each household is an agent making various evacuation related decisions based on advanced behavioral models. From household decisions, a number of vehicles are generated and entered in the evacuation transportation network at different time intervals. An adaptive routing strategy that can achieve efficient network-wide traffic measurements is proposed. Computational results are presented based on simulations over the Miami-Dade network with detailed representation of the road network geometry. The simulation results demonstrate the evolution of traffic congestion as a function of the household decision-making, the variance of the congestion across different areas relative to the storm path and the most congested O-D pairs in the network. The simulation tool can be used as a planning tool to make decisions related to how traffic information should be communicated and in the design of traffic management policies such as contra-flow strategies during evacuations.     

Dynamic User Equilibrium Model for Combined Activity-Travel Choices Using Activity-Travel Supernetwork Representation

Integrated urban transportation models have several benefits over sequential models including consistent solutions, quicker convergence, and more realistic representation of behavior. Static models have been integrated using the concept of Supernetworks. However integrated dynamic transport models are less common. In this paper, activity location, time of participation, duration, and route choice decisions are jointly modeled in a single unified dynamic framework referred to as Activity-Travel Networks (ATNs). ATNs is a type of Supernetwork where virtual links representing activity choices are added to augment the travel network to represent additional choice dimensions. Each route in the augmented network represents a set of travel and activity arcs. Therefore, choosing a route is analogous to choosing an activity location, duration, time of participation, and travel route. A cell-based transmission model (CTM) is embedded to capture the traffic flow dynamics. The dynamic user equilibrium (DUE) behavior requires that all used routes (activity-travel sequences) provide equal and greater utility compared to unused routes. An equivalent variational inequality problem is obtained. A solution method based on route-swapping algorithm is tested on a hypothetical network under different demand levels and parameter assumptions.     

多个MFD子区边界协调控制方法

为了改善交通网络运行状况,根据车流密度的差异对宏观路网进行子区划分,提出了面向多个宏观基本图（Macroscopic fundamental diagram,MFD）子区的边界协调控制方法.根据划分的多个子区间邻接关系和流入流出交通流率,建立了路网车流平衡方程.通过与最佳累积车辆数进行比较,确定了拥挤度高的子区边界交叉口最佳流入与流出的交通流量;进而建立了以整个路网旅行完成流率最大、平均行程时间和平均延误最小的多目标边界协调优化模型,并通过自适应遗传算法对多目标函数进行求解.以存在4个MFD子区的实际路网为分析对象,对比仿真结果表明所提方法可有效提高路网运行效率、缓解拥堵状况.     

Edge-centric trust management in vehicular networks

A major objective of vehicular networking is to improve road safety and reduce traffic congestion. The experience of individual vehicles on traffic conditions and travel situations can be shared with other vehicles for improving their route planning and driving decisions. Nevertheless, the frequent occurrence of adversary vehicles in the network may affect the overall network performance and safety. These vehicles may behave intelligently to avoid detection. To effectively control and monitor such security threats, an efficient Trust Management system should be employed to identify the trustworthiness of individual vehicles and detect malicious drivers which is the major focus of this work. We propose a hybrid solution, which integrates Edge Computing and Multi-agent modeling in a Trust Management system for vehicular networks. The proposed solution also aims to overcome the limitations of the two commonly utilized approaches in this context: cloud computing and Peer-to-Peer (P2P) networking. Our framework has a set of features that make it an efficient platform to address the major security challenges in vehicular networks including latency, scalability, uncertainty, data accessibility, and malicious behavior detection. Performance of the approach is evaluated by simulating a realistic environment. Experimental results show that the proposed approach outperforms similar approaches from literature for various performance indicators.     

基于路段元胞传输模型的动态用户最优配流问题

利用基于路段的元胞传输模型进行模拟, 给出了一种计算实际路段出行阻抗的方法, 并在此基础上构造了基于路段变量的动态用户最优变分不等式模型. 模型采用针对迄节点的路段变量, 在每一个小时段都能给出路段流入率、流出率、路段流量和实际路段阻抗, 为用户提供较为全面的诱导信息打下了较好的理论基础. 采用了修正投影算法来进行求解. 数值算例表明模型具有的实用性和优越性, 使道路交通流宏观模型与动态网络交通配流问题得到较好的结合.     

基于协调博弈的交通拥塞传播临界值研究

为深入分析交通网络的拥塞扩散过程,得出交通拥塞传播的临界值,提出了交通拥塞传播的协调博弈模型。通过网络个体之间的协调博弈,从出行者面对拥塞的决策行为出发,描述了交通网络的拥塞扩散过程。通过网络邻居之间的行为传递,形成了交通网络的拥塞扩散模型,并利用概率母函数方法推导了交通拥塞扩散的临界条件。最后构建了交通通塞的仿真系统,并通过路网结构、节点度分布等参数对交通拥塞进行了仿真分析。仿真实验结果与解析分析结果一致,并能反映交通拥塞动态过程信息,结果表明交通拥塞扩散的临界条件关键在于拥塞节点对周边正常节点的影响力,当局部交通拥塞对周边节点的影响力达到一定程度时,可能导致大规模交通拥塞的出现。     

A modeling framework for urban traffic systems microscopic simulation

In this paper a modeling framework for urban traffic systems (UTS) is presented. The model, used for agent based micro-simulation, describes both the traffic network and dynamic entities, namely vehicles, traffic lights, and pedestrians. The framework allows defining systematically the necessary components and their behavior of a model oriented to event driven simulation, which can be executed in a distributed way. In the model, the vehicles are conceived as mobile agents with decision making capabilities that interact with the environment and other entities within the traffic network, performing diverse activities according to numerous situations arisen during the simulation. A multi-level Petri net based formalism, named n-LNS is used for describing the structure of the UTS and the components behavior. The first level describes the traffic network; the second level models the behavior of diverse road network users considered as agents, and the third level specifies detailed procedures performed by the agents, namely travel plans, tasks, etc.     

Design of Regulatory Traffic Light Control Systems with Synchronized Timed Petri Nets

Fixing the phases is one of the common methods to control an urban traffic network. Once a road is filled with a high traffic flow approaching its capacity, the conventional traffic light controller is not able to handle this traffic congestion phenomenon well. In this paper, we propose a novel regulatory traffic light control system to handle such traffic congestion by using synchronized timed Petri nets (STPNs). Three kinds of intersections in an urban traffic network are defined and employed to demonstrate our new regulatory traffic light control system models. Finally, the liveness and reversibility of the proposed STPN models are proven through the reachability graph analysis method. To our knowledge, this is the first work that solves a traffic congestion problem with a regulatory traffic light control technique that is effective in preventing vehicles from entering traffic congestion zones.     

基于LSTM-BP组合模型的短时交通流预测

为减轻日益严重的交通拥堵问题,实现智能交通管控,给交通流诱导和交通出行提供准确实时的交通流预测数据,设计了基于长短时记忆神经网络（LSTM）和BP神经网络结合的LSTM-BP组合模型算法.挖掘已知交通流数据的特征因子,建立时间序列预测模型框架,借助Matlab完成从数据的处理到模型的仿真,实现基于LSTM-BP的短时交通流精确预测.通过与LSTM\BP\WNN三种预测网络模型的对比,结果表明LSTM-BP预测的时间序列具有较高的精度和稳定性.该模型的搭建,可对交通分布的预测、交通方式的划分、实时交通流的分配提供依据和参考.     

基于层次颜色Petri网的交通紧急调度算法与建模

针对城市交通网络中紧急车辆在行驶区段中如何较快地到达终点的问题,提出了一种基于Petri网的交通紧急控制策略模型。利用Davidson函数中行驶时间与交通流之间的对应关系,得出紧急车辆在道路上的最短行驶时间,并将其作为权重,运用Dijkstrsa算法进行最短路径寻优;采用紧急信号灯控制策略对最短路径上的交叉口信号灯进行了调整,减少紧急车辆在交叉口的延滞时间,并运用Petri网理论,建立紧急车辆在交叉口的紧急信号灯控制模型。为了描述紧急信号灯控制策略的动态行为特性,将其各部分关键要素分别设计为相应的Petri网子模型。通过模型的一个仿真实例,进行了紧急控制策略与普通策略的实验对比,实验结果表明前者可以对紧急车辆的到达时间进行优化。     

基于DCGRU-RF模型的路网短时交通流预测

随着城市化进程的加快,我国城市机动车数量快速增加,使得现有路网容量难以满足交通运输需求,交通拥堵、环境污染、交通事故等问题与日俱增。准确高效的交通流预测作为智能交通系统的核心,能够有效解决交通出行和管理方面的问题。现有的短时交通流预测研究往往基于浅层的模型方法,不能充分反映交通流特性。文中针对复杂的交通网络结构,提出了一种基于DCGRU-RF(Diffusion Convolutional Gated Recurrent Unit-Random Forest)模型的短时交通流预测方法。首先,使用DCGRU(Diffusion Convolutional Gated Recurrent Unit)网络刻画交通流时间序列数据中的时空相关性特征;在获取数据中的依赖关系和潜在特征后,选择RF(Random Forest)模型作为预测器,以抽取的特征为基础构建非线性预测模型,得出最终的预测结果。实验以两条城市道路中的38个检测器为实验对象,选取了5周工作日的交通流数据,并将所提方法与其他常见交通流量预测模型进行比较。结果表明,DCGRU-RF模型能够进一步提高预测精度,准确度可达95%。     

交通诱导预测控制仿真研究

将动态交通分配实施过程纳入预测控制框架下以满足实时交通诱导的目的,提出一种交通诱导预测控制算法．该算法是在滚动时域基础上进行的,包括实时交通分配、交通流模拟运行及评价以及进化最佳路径３ 个重要环节．仿真结果表明,交通诱导预测控制是一种良好的计算机控制方法学,其优化过程预先考虑了目前交通分配对未来路网的影响,因而可有效地防范交通拥堵,实现考虑反馈的路网交通流实时分配优化,同时为出行者提供最佳路径．     

Comparison of Methods for Path Flow Reassignment for Dynamic User Equilibrium

Models to describe or predict of time-varying traffic flows and travel times on road networks are usually referred to as dynamic traffic assignment (DTA) models or dynamic user equilibrium (DUE) models. The most common form of algorithms for DUE consists of iterating between two components namely dynamic network loading (DNL) and path inflow reassignment or route choice. The DNL components in these algorithms have been investigated in many papers but in comparison the path inflow reassignment component has been relatively neglected. In view of that, we investigate various methods for path inflow reassignment that have been used in the literature. We compare them numerically by embedding them in a DUE algorithm and applying the algorithm to solve DUE problems for various simple network scenarios. We find that the choice of inflow reassignment method makes a huge difference to the speed of convergence of the algorithms and, in particular, find that ??travel time responsive?? reassignment methods converge much faster than the other methods. We also investigate how speed of convergence is affected by the extent of congestion on the network, by higher demand or lower capacity. There appears to be much scope for further improving path inflow reassignment methods.