Tradable Credit Scheme for Control of Evolutionary Traffic Flows to System Optimum: Model and its Convergence

We propose a dynamic tradable credit scheme for control of the day-to-day evolution process of traffic flows towards the system optimum (SO) state in a traffic network with elastic demand. In the scheme, the distribution and charge of travel credits are adjusted from period to period. The interacting dynamics among day-to-day traffic flows, period-to-period credit adjustment, and day-to-day credit price is formulated as an evolutionary game model. We mathematically prove two properties of the model, i.e., the consistence of the stationary state with the SO state and the convergence of the evolutionary trajectory. Finally, numerical results on a middle-size network are presented to validate the dynamic tradable credit scheme and to demonstrate the properties and application of the model.

     

基于电子路票征收及补偿的新型离散路网设计与算法

在城市交通网络设计中,可交易电子路票系统是一种新型且更加公平的拥挤收费方法。本文将具有征收与补偿机制的0分配可自由交易电子路票和路网离散改造设计结合起来共同研究城市道路交通网络设计与管理问题。建立的新型路网设计与管理模型同时考虑了0分配电子路票系统与离散路网设计。采用Logit随机用户均衡原理模拟出行者的路线选择行为,并设计具有路段容量和电子路票可行约束的随机均衡问题的有效算法。由于该模型是一个具有不动点约束的数学规划问题,求解比较困难,因此采用常见的群体智能优化算法求解该模型,数值实验验证了模型和算法的有效性和可行性。     

The combination of continuous network design and route guidance

In this study, a traffic management measure is presented by combining the route guidance of Advanced Traveler Information System (ATIS) and the continuous network design (CNDP) to alleviate increasing traffic congestion. The route guidance recommends the travelers to choose the shortest path based on marginal travel cost and user constraints. The problem is formulated into a bi-level programming problem. The most distinct property of this problem formulation is that the feasible path set of its lower-level problem is determined by the decision variable of upper-level problem, while in conventional transportation network design problems the feasible path set for lower-level traffic assignment problem is fixed to be all the viable paths between each specific origin-destination pair. The simulated annealing algorithm is improved to solve this bi-level problem. A path-based traffic algorithm is developed to calculate the lower-level traffic assignment problem under the route guidance. Compared to the results of conventional CNDP, the measure presented in this study can better improve the transportation network performance.     

Optimal mixed charging schemes for traffic congestion management with subsidy to new energy vehicle users

Traffic congestion and emissions are two severe and urgent problems in the transportation field. To alleviate congestion and encourage green travel, this paper studies a mixed charging scheme for the multimodal transportation network including conventional vehicles (CV) and new energy vehicles (NEV). We exploit the optimal mixed charging schemes according to the mixed equilibria and system optimum model. The mixed equilibria characterized by the variational inequality system represents that the players follow either the user equilibrium or Cournot–Nash equilibrium principle in routing choice. The proposed mixed scheme comprises a regular tolling plan and a tradable credit plan for the CV and NEV users, respectively, where the tradable credits can be seen as a subsidy to the NEV users since they are issued free by the government. Moreover, the existence of the optimal mixed charging scheme is proved for the multimodal transportation network and demonstrated numerically by a concrete example.     

Study on continuous network design problem using simulated annealing and genetic algorithm

In general, a continuous network design problem (CNDP) is formulated as a bi-level program. The objective function at the upper level is defined as the total travel time on the network, plus total investment costs of link capacity expansions. The lower level problem is formulated as a certain traffic assignment model. It is well known that such bi-level program is non-convex and non-differentiable and algorithms for finding global optimal solutions are preferable to be used in solving it. Simulated annealing (SA) and genetic algorithm (GA) are two global methods and can then be used to determine the optimal solution of CNDP. Since application of SA and GA on continuous network design on real transportation network requires solving traffic assignment model many times at each iteration of the algorithm, computation time needed is tremendous. It is important to compare the efficacy of the two methods and choose the more efficient one as reference method in practice. In this paper, the continuous network design problem has been studied using SA and GA on a simulated network. The lower level program is formulated as user equilibrium traffic assignment model and Frank–Wolf method is used to solve it. It is found that when demand is large, SA is more efficient than GA in solving CNDP, and much more computational effort is needed for GA to achieve the same optimal solution as SA. However, when demand is light, GA can reach a more optimal solution at the expense of more computation time. It is also found that increasing the iteration number at each temperature in SA does not necessarily improve solution. The finding in this example is different from [Karoonsoontawong, A., & Waller, S. T. (2006). Dynamic continuous network design problem – Linear bilevel programming and metaheuristic approaches. Network Modeling 2006 Transportation Research Record (1964) (pp. 104–117)]. The reason might be the bi-level model in this example is nonlinear while the bi-level model in their study is linear.     

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。     

A Path-Based Solution Algorithm for Dynamic Traffic Assignment

The approximation of the traversal cost is a critical component of dynamic traffic assignment model. In link based traffic assignment, it assumes that the constraints sets are linear or convex and it is not realistic in general traffic networks. Comparing with the link based model, the path cost in the objective function of the path based traffic assignment model is implicitly nonlinear or non-convex, which is difficult to solve. In this paper, a path based traffic assignment model combining the generalized expansion method in M/G/c/c model with the point queue model is proposed to extend the link traversal cost to the travel cost along the path. Comparing with the link based model without considering intersection effects, this proposed path based model can take into account queuing delays between intersections and it is easy to implement. In order to validate the proposed path based model, a comparative experiment is implemented by comparing with the traditional travel cost models in Sydney traffic networks. Taking into account travel flow changes and blocking time, the proposed path based model is more effective for travellers from the uncongested traffic to congested traffic networks. In addition, the results from traffic assignment model show that the proposed model can achieve feasible results.     

考虑交通拥堵成本的零售商供货量决策模型

引入交通拥堵成本因子,考虑从零售商配送中心到市场间的路段存在交通拥堵情况下,分别建立了供应链系统中供应商定价模型和零售商供货量模型;运用动态博弈分析方法,得出了零售商和供应商满足利润最大化条件下解的均衡方程;最后,研究了一个简化的供应链网络,对上述模型进行了验证与分析。结果表明,随着交通拥堵成本因子的不断增大,零售商会逐渐减少对市场的商品供货量,通过提高市场价格和减少运输费用来弥补拥堵成本的增加。     

Urban traffic congestion propagation and bottleneck identification

Bottlenecks in urban traffic network are sticking points in restricting network collectivity traffic efficiency. To identify network bottlenecks effectively is a foundational work for improving network traffic condition and preventing traffic congestion. In this paper, a congestion propagation model of urban network traffic is proposed based on the cell transmission model （CTM）. The proposed model includes a link model, which describes flow propagation on links, and a node model, which represents link-to-link flow propagation. A new method of estimating average journey velocity （AJV） of both link and network is developed to identify network congestion bottlenecks. A numerical example is studied in Sioux Falls urban traffic network. The proposed model is employed in simulating network traffic propagation and congestion bottleneck identification under different traffic demands. The simulation results show that continual increase of traffic demand is an immediate factor in network congestion bottleneck emergence and increase as well as reducing network collectivity capability. Whether a particular link will become a bottleneck is mainly determined by its position in network, its traffic flow （attributed to different OD pairs） component, and network traffic demand.     

Adaptive Park-and-ride Choice on Time-dependent Stochastic Multimodal Transportation Network

In transportation networks with stochastic and dynamic travel times, park-and-ride decisions are often made adaptively considering the realized state of traffic. That is, users continue driving towards their destination if the congestion level is low, but may consider taking transit when the congestion level is high. This adaptive behavior determines whether and where people park-and-ride. We propose to use a Markov decision process to model the problem of commuters’ adaptive park-and-ride choice behavior in a transportation network with time-dependent and stochastic link travel times. The model evaluates a routing policy by minimizing the expected cost of travel that leverages the online information about the travel time on outgoing links in making park-and-ride decisions. We provide a case study of park-and-ride facilities located on freeway I-394 in Twin Cities, Minnesota. The results show a significant improvement in the travel time by the use of park-and-ride during congested conditions. It also reveals the time of departure, the state of the traffic, and the location from where park-and-ride becomes an attractive option to the commuters. Finally, we show the benefit of using online routing in comparison to an offline routing algorithm.

     

Capacitated Refueling Station Location Problem with Traffic Deviations Over Multiple Time Periods

In this paper, we consider the problem of locating refueling stations in a transportation network via mathematical programming. The proposed model is applicable for several alternative fuel types and is particularly suitable for hydrogen fuel. We assume that a central planner, a hydrogen manufacturer or a government agency, determines the locations of the refueling stations for a given intra-city transportation network while accounting for multi-period travel demand, nonlinear refueling station operational cost, and the deviations of travelers from their shortest routes to refuel. Incorporating demand patterns over multiple periods allows us to account for both short- and long-term variation in hydrogen refueling demand (the former due to time of day, and the latter future hydrogen fuel cell vehicle growth). It also helps us model the changes in user preferences (station and route choices) and traffic conditions over different periods. To account for refueling station operational cost in making investment decisions, we introduce a staircase marginal cost function. In addition, the model explicitly considers station and route choices of travelers as they may deviate from their original paths to refuel, incurring additional costs and affecting the number and locations of refueling stations. We formulate this problem as a multi-period, mixed-integer model with constant link travel time and staircase operational cost at refueling stations. We applied two well-known solution algorithms, branch-and-bound and Lagrangian relaxation, to solve the problem. Our analysis shows that although we are able to solve the refueling station location problem to optimality with branch-and-bound, the Lagrangian relaxation approach provides very good results with less computational time. Additionally, our numerical example of Mashhad, Iran demonstrates that locating refueling stations with considering multi-period traffic patterns (as opposed to single-period) results in minimum network-wide traffic congestion and lower user and agency costs over a planning horizon.     

考虑区域协调性的城际列车开行方案优化

针对城际列车开行方案没有有效匹配城市轨道交通运能的问题，提出一种考虑区域协调性的城际列车开行方案优化方法。首先，以旅客出行费用最小和铁路运输效益最大为优化目标，考虑城际列车载客能力、出发地目的地（Original Destination，OD）客流需求和通过能力等约束；然后，在此基础上增加运能匹配度的限制，构建了考虑区域协调性的城际列车开行方案多目标非线性规划模型，并设计改进的模拟退火算法求解模型；最后，以广深城际铁路为例并进行两组对比分析。实验结果表明:考虑区域协调性的列车开行方案可以使旅客出行总广义费用降低约4.06%，铁路部门的效益提高约9.58%，旅客和铁路的系统总成本降低约23.27%；与遗传算法相比，改进的模拟退火算法在求解质量与收敛速度上均有较大提高。所提模型和算法可充分兼顾旅客和铁路双方利益，能够为城际列车开行方案优化问题提供有效解决方法。     

A robust green traffic-based routing problem for perishable products distribution

Nowadays, transportation and logistics are considered as the drivers of economic development in the countries due to their impacts on the main variables of the country's economy such as production, employment, price, and the cost of living. Statistics indicate that fuel consumption constructs a major part of transportation costs, where its optimization leads to the creation of an energy-efficient and sustainable transportation system. On the other hand, vehicles' traffic is also one of the main criteria affecting the travel time of vehicles between demand nodes in a supply chain, increasing fuel consumption, and, consequently, damaging effects of greenhouse gasses. In this paper, a novel robust mixed-integer linear programming model is developed for a green vehicle routing problem with intermediate depots considering different urban traffic conditions, fuel consumption, time windows of services, and uncertain demand for perishable products. To validate and solve the suggested model, CPLEX solver of GAMS software is employed as an exact method. Finally, a case study problem is investigated to evaluate the applicability of the proposed model and determine the optimal managerial insights and policies in the real-world conditions using sensitivity analyses. Moreover, a novel robustness threshold comparison is conducted to find the optimal level of budget assignment.     

排放量成本度量的合乘交通系统优化研究

为缓解交通拥堵,构建绿色交通系统,研究了以排放量成本度量的交通系统优化问题,建立了针对包括合乘的交通系统的分阶段模型。对出行者交通选择模式进行了方式划分,建立了以排放量衡量的用户均衡和系统最优交通流分配模型,设计了相应的求解算法,并通过一个算例说明了模型和算法的可行性和有效性。通过对比,说明了按时间成本度量的系统最优分配和按排放量成本度量的系统最优分配所能达到的效果;根据不同的排放相对系数说明了考虑合乘的交通系统中交通量、系统总时间和排放量的变化趋势。     

Multi-criteria evolutionary optimization of a traffic light using genetics algorithm and teaching-learning based optimization

Today, the development of urbanization and increasing the number of vehicles has resulted in displeased consequences like traffic congestion and vehicle queuing. The vast majority of countries in the world encounter the challenge of the explosive rise in traffic demand. In this regard, it is necessary to meet traffic demand in transport networks, especially in metropolitans. In traffic management and shortening the trip duration, traffic lights on the signalized intersections play an essential role in urban pathways. This work provides a multi-criteria decision-making method for optimum traffic light control in an isolated corner. The main idea involves establishing a set of sub-optimal solutions for traffic light timing and selecting the best one among the diverse solutions. We have mathematically modelled the problem as an optimization problem to achieve an optimal solution with less waiting time for vehicles in intersections and the lowest cost. Genetic algorithm (GA) and Teaching-Learning-based Optimization (TLBO) are utilized for each phase to create a set of suitable timing scenarios. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is used to identify the best scenario, considering both waiting vehicles and traffic capacity as decision criteria. Its efficiency has been demonstrated over three different traffic volumes. Also, in a real-world implementation, its practical capability has been approved at a crossroads in Mashhad, Iran. The simulations indicate the improvement in the number of vehicles waiting behind the crossroad and the traffic capacity by 10% and 6.76% compared to the existing signal timing of the studied intersection, respectively.     

考虑驾乘人员公平性的网约出租车合乘路径优化模型

为了充分发挥合乘出租车承载率高、运营效益高及交通资源省等优点, 缓解城市打车难问题, 对网约出租 车合乘路径优化问题开展研究. 首先针对路网中网约出租车的供需情况, 以系统路径最短为优化目标建立目标函 数, 其次考虑网约出租车额定载客量、路径合理性、乘客利益及驾驶员收益与时间窗因素, 构建优化模型的约束条 件, 并结合绕行距离与乘客公平性原则进行费用约束, 使得绕行距离长的合乘子路径获得更多的费用补偿, 然后基 于遗传算法思想, 针对合乘路径中乘客需求起终点的次序问题, 设计了改进的交叉与变异算子. 最后依据大连市区 局部路网高峰时段内的出租车供需数据, 利用合乘路径优化模型及算法进行求解. 研究表明, 优化模型及算法可以 短时间寻求到系统近似最优解, 所得合乘方案较非合乘出行模式有效减少了出租车空驶率与乘客的出行成本, 提高 了驾驶员的平均收益.     

网约拼车出行的乘客车辆匹配及路径优化

城市道路拥堵严重及共享理念的盛行带来了拼车出行的兴起。出行线路相似的乘客共乘一辆车,可提高座位利用率、节省费用、缓解交通压力。以带时间窗约束的无换乘多车辆静态拼车问题为研究背景,从车辆使用费、途中走行成本及到达时间窗惩罚成本3个方面建立乘客车辆匹配及路径优化的目标函数,以车辆容量、乘客出发及到达时间窗、路径无迂回、乘客车辆匹配无重叠等限制构建模型约束条件,采用演化策略算法求解问题,根据模型特征设计编码解码规则,解码结果可同时获得车辆乘客匹配关系和走行路径,采用交叉变异操作更新迭代个体种群,进而求得最优解。运用MATLAB求解算例验证了模型可行性及算法有效性,结果表明算法能快速响应静态拼车问题,在较短时间即可给出乘客车辆的先后匹配关系及车辆走行路径,拼车方案相比独自出行能节省更多成本。     

考虑网络流量的最优路径求解模型和算法

本文旨在解决交通网络中群体车辆的路径选择问题．即为每个车辆寻求最优行驶路径．使之在起迄点间的旅行时间最短．考虑到网络流量对路段旅行时间的影响，先进行流量分配，再同时为各个车辆寻求最短路径．为此，首先给出了考虑流量影响的网络模型，然后建立了基于路段的用于流量分配的变分不等式模型．该模型的解给出了车辆按照最优路径行驶时分配到各路段上的车辆数目．由于该模型是完全基于路段的，从而克服了基于路径方法必须进行路径穷举的缺陷．最后给出了最优路径选择算法，并证明了算法的正确性．本文给出的模型和算法适用于交通畅通、交通拥挤等各种情况．实验结果表明本文提出的模型和算法是非常有效的．     

固定费率下城市出租车拥挤收费模型与算法

针对出租车自身的特殊性, 研究了实施拥挤收费政策过程中出租车是否也应进行拥挤收费。在收费费率已知条件下, 采用组合网络均衡模型描述了出租车拥挤收费前后对出行产生、模式划分、流量分配以及出行分布的影响, 构建了与之等价的最优化模型, 设计了求解模型的启发式算法, 比较了对出租车征收拥挤费前后的社会福利变化。算例表明, 收费前的社会福利总是明显大于收费后的, 并且二者之间的差距随着出行者时间价值(VOT)的增加而减小, 随着弹性灵敏参数的增加而增大。这一研究有助于为拥挤收费政策制定者提供科学的决策依据。     

雨雾天气下公路可变限速控制策略研究

针对雨雾天气下公路交通事故上升与通行能力下降的问题,综合考虑交通安全和通行效率两个因素,提出一种雨雾天气下可变限速控制策略;首先,考虑雨雾天气下道路线形对车辆速度和道路通行能力的影响,建立雨雾天气下动态交通流模型;然后,根据驾驶员可视距离、路面附着系数和道路曲线等因素确定雨雾天气下车辆最大安全速度;最后,采用自适应遗传算法对包含总行程时间和总行驶里程的目标函数进行求解,得到雨雾天气下各路段的限速值;仿真结果表明,所提出的可变限速控制策略有效降低了雨雾天气下公路的行车风险,并缓解了交通拥堵。