A link-node complementarity model and solution algorithm for dynamic user equilibria with exact flow propagations

In this paper, we propose a link-node complementarity model for the basic deterministic dynamic user equilibrium (DUE) problem with single-user-class and fixed demands. The model complements link-path formulations that have been widely studied for dynamic user equilibria. Under various dynamic network constraints, especially the exact flow propagation constraints, we show that the continuous-time dynamic user equilibrium problem can be formulated as an infinite dimensional mixed complementarity model. The continuous-time model can be further discretized as a finite dimensional non-linear complementarity problem (NCP). The proposed discrete-time model captures the exact flow propagation constraints that were usually approximated in previous studies. By associating link inflow at the beginning of a time interval to travel times at the end of the interval, the resulting discrete-time model is predictive rather than reactive. The solution existence and compactness condition for the proposed model is established under mild assumptions. The model is solved by an iterative algorithm with a relaxed NCP solved at each iteration. Numerical examples are provided to illustrate the proposed model and solution approach. We particularly show why predictive DUE is preferable to reactive DUE from an algorithmic perspective.     

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Frank-Wolfe(FW)算法是一类广泛应用于求解交通分配问题的算法。它具有容易编程实现,所需内存少的特点。但是该算法收敛速度较慢,不能得到路径信息。为了提高算法的效率,本文研究三种流量更新策略(all-at-once, one-origin-at-a-time, one-OD-at-a-time)以及不同的步长搜索策略下的FW算法,其中步长搜索策略包括精确线性搜索方法(包括二分法、黄金分割法、成功失败法)和不精确的线性搜索方法(包括基于Wolfe-Powell收敛准则的搜索方法和Gao等提出的非单调线性搜索方法)。最后,本文将上述策略应用于四种不同规模的交通网络中,并给出较适合求解的组合。     

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停机位的合理指派是机场地面作业中的一项核心任务,对减少枢纽机场航班中转时间起着重要作用,但是停机位指派问题是NP难问题,模型和算法都比较复杂。建立了以旅客满意度为优化目标的数学模型,并设计了一种贪婪模拟退火算法（GSAA）,以求解枢纽机场的停机位指派问题。该算法首先根据贪婪算法FCFS准则使“丢失”的航班数最少得到模拟退火算法的初始解,然后运用经典模拟退火算法求解出最优指派结果。数值仿真证明了模型和算法的有效性。     

新能源汽车购买行为及其环境影响效果研究

为了考量新能源汽车的推广使用对环境的具体影响,对新能源汽车的购买行为和普及率进 行了研究,同时分析新能源汽车在优惠政策条件下在实际路网中的环境影响。首先,对大连市居 民进行了意向问卷调查,建立描述家庭新能源汽车购买行为模型;其次,应用该模型对新能源汽 车在大连市的普及率进行分析;最后,在道路收费优惠政策条件下,考虑新能源汽车的普及率和 出行频率的变化,利用多用户交通平衡分配模型进行实例分析,探索新能源汽车在实际路网中的 排放影响。结果表明,在低普及率情况下,新能源汽车的减排效果并不明显,而在高普及率情况 下,减排效果比较显著。然而,对于交通领域巨大的碳排放体量而言,低普及率带来的碳排放量 下降的累积效应仍是非常可观的。     

Sensitivity analysis on stochastic equilibrium transportation networks using genetic algorithm

This study deals with the sensitivity analysis of an equilibrium transportation networks using genetic algorithm approach and uses the bi‐level iterative sensitivity algorithm. Therefore, integrated Genetic Algorithm‐TRANSYT and Path Flow Estimator (GATPFE) is developed for signalized road networks for various level of perceived travel time in order to test the sensitivity of perceived travel time error in an urban stochastic road networks. Level of information provided to drivers correspondingly affects the signal timing parameters and hence the Stochastic User Equilibrium (SUE) link flows. When the information on road system is increased, the road users try to avoid conflicting links. Therefore, the stochastic equilibrium assignment concept tends to be user equilibrium. The GATPFE is used to solve the bi‐level problem, where the Area Traffic Control (ATC) is the upper‐level and the SUE assignment is the lower‐level. The GATPFE is tested for six‐junction network taken from literature. The results show that the integrated GATPFE can be applied to carry out sensitivity analysis at the equilibrium network design problems for various level of information and it simultaneously optimize the signal timings (i.e. network common cycle time, signal stage and offsets between junctions).     

Cellpath: Fusion of cellular and traffic sensor data for route flow estimation via convex optimization

A new convex optimization framework is developed for the route flow estimation problem from the fusion of vehicle count and cellular network data. The issue of highly underdetermined link flow based methods in transportation networks is investigated, then solved using the proposed concept of cellpaths for cellular network data. With this data-driven approach, our proposed approach is versatile: it is compatible with other data sources, and it is model agnostic and thus compatible with user equilibrium, system-optimum, Stackelberg concepts, and other models. Using a dimensionality reduction scheme, we design a projected gradient algorithm suitable for the proposed route flow estimation problem. The algorithm solves a block isotonic regression problem in the projection step in linear time. The accuracy, computational efficiency, and versatility of the proposed approach are validated on the I-210 corridor near Los Angeles, where we achieve 90% route flow accuracy with 1033 traffic sensors and 1000 cellular towers covering a large network of highways and arterials with more than 20,000 links. In contrast to long-term land use planning applications, we demonstrate the first system to our knowledge that can produce route-level flow estimates suitable for short time horizon prediction and control applications in traffic management. Our system is open source and available for validation and extension.     

Planning for the unexpected: The value of reserve capacity for public transport network robustness

Public transport networks (PTN) are subject to recurring service disruptions. Most studies of the robustness of PTN have focused on network topology and considered vulnerability in terms of connectivity reliability. While these studies provide insights on general design principles, there is lack of knowledge concerning the effectiveness of different strategies to reduce the impacts of disruptions. This paper proposes and demonstrates a methodology for evaluating the effectiveness of a strategic increase in capacity on alternative PTN links to mitigate the impact of unexpected network disruptions. The evaluation approach consists of two stages: identifying a set of important links and then for each identified important link, a set of capacity enhancement schemes is evaluated. The proposed method integrates stochastic supply and demand models, dynamic route choice and limited operational capacity. This dynamic agent-based modelling of network performance enables to capture cascading network effects as well as the adaptive redistribution of passenger flows. An application for the rapid PTN of Stockholm, Sweden, demonstrates how the proposed method could be applied to sequentially designed scenarios based on their performance indicators. The method presented in this paper could support policy makers and operators in prioritizing measures to increase network robustness by improving system capacity to absorb unexpected disruptions.     

Dynamic activity-travel assignment in multi-state supernetworks

The integration of activity-based modeling and dynamic traffic assignment for travel demand analysis has recently attracted ever-increasing attention. However, related studies have limitations either on the integration structure or the number of choice facets being captured. This paper proposes a formulation of dynamic activity-travel assignment (DATA) in the framework of multi-state supernetworks, in which any path through a personalized supernetwork represents a particular activity-travel pattern (ATP) at a high level of spatial and temporal detail. DATA is formulated as a discrete-time dynamic user equilibrium (DUE) problem, which is reformulated as an equivalent variational inequality (VI) problem. A generalized dynamic link disutility function is established with the accommodation of different characteristics of the links in the supernetworks. Flow constraints and non-uniqueness of equilibria are also investigated. In the proposed formulation, the choices of departure time, route, mode, activity sequence, activity and parking location are all unified into one time-dependent ATP choice. As a result, the interdependences among all these choice facets can be readily captured. A solution algorithm based on the route-swapping mechanism is adopted to find the user equilibrium. A numerical example with simulated scenarios is provided to demonstrate the advantages of the proposed approach.     

基于网络交通分配方法族谱的交通分配 一体化技术与工程应用

为满足交通规划、建设与管理等应用场景对交通分配多样化的需求，结合目前交通分配方 法族谱中的众多模型与方法，本文构建能够满足族谱中所有交通分配特征的一体化交通分配技 术框架，提出交通网络交通分配一体化技术体系，并将该体系嵌入交通分析平台软件“交运之星- TranStar”中。该技术体系包括：“模型关键参数”“交通阻抗函数”“交通网络交通分配基础模型与 快速算法”3部分模块组合的分析模型一体化；面向步行、自行车、机动车及公共交通等多模式交 通网络的分析对象一体化；针对城市土地开发，交通网络建设，交通管理控制，公共交通系统，以 及交通政策制定等应用场景一体化。选取南京市道路网络和公交网络进行实证分析。结果表 明，本文提出的交通分配一体化技术具有处理超万节点多模式交通网络的能力，对各类交通模 式、典型业务场景的分析结果可为城市交通系统规划、建设与管理提供决策支持。     

Integrated planning of static and dynamic charging infrastructure for electric vehicles

This paper investigates the optimal deployment of static and dynamic charging infrastructure considering the interdependency between transportation and power networks. Static infrastructure means plug-in charging stations, while the dynamic counterpart refers to electrified roads or charging lanes enabled by charging-while-driving technology. A network equilibrium model is first developed to capture the interactions among battery electric vehicles’ (BEVs) route choices, charging plans, and the prices of electricity. A mixed-integer bi-level program is then formulated to determine the deployment plan of charging infrastructure to minimize the total social cost of the coupled networks. Numerical examples are provided to demonstrate travel and charging plans of BEV drivers and the competitiveness of static and dynamic charging infrastructure. The numerical results on three networks suggest that (1) for individual BEV drivers, the choice between using charging lanes and charging stations is more sensitive to parameters including value of travel time, service fee markup, and battery size, but less sensitive to the charging rates and travel demand; (2) deploying more charging lanes is favorable for transportation networks with sparser topology while more charging stations can be more preferable for those denser networks.