诱导信息下城市轨道交通乘客路径选择行为研究

为研究城市轨道交通诱导信息对乘客的影响，考虑信息提供时间、方式、更新频率及其与 路径属性之间的交互关系，提出融合诱导信息的城市轨道交通乘客路径选择行为决策框架。考 虑乘客在决策时表现出遵循混合决策规则的倾向差异和不同属性感知差异，建立改进的混合效用-后悔模型。将路径属性与诱导信息同时纳入调研情景设计，基于调研结果标定模型中关键参数，结果表明，基于混合决策规则的模型拟合效果最高(调整优度比达0.396)。进一步分析乘客对信息的偏好：相较于下载手机APP，乘客更喜欢社交媒体推送的形式接收诱导信息；引入信息推送频率时间价值，并对比得出，女性、中老年及非通勤乘客更倾向于接受更高频率的诱导信息推送服务；分析各属性弹性值，表明诱导信息提供方式和更新频率应作为管理者的辅助手段，在特定场景下(例如高峰拥堵和突发事故)诱导信息将会发挥更大的作用。研究揭示了诱导信息提供方式如何影响轨道交通乘客的路径决策机理，有助于设计更加精准和高效的诱导信息策略辅助客流组织管理。     

A nested recursive logit model for route choice analysis

We propose a route choice model that relaxes the independence from irrelevant alternatives property of the logit model by allowing scale parameters to be link specific. Similar to the recursive logit (RL) model proposed by Fosgerau et al. (2013), the choice of path is modeled as a sequence of link choices and the model does not require any sampling of choice sets. Furthermore, the model can be consistently estimated and efficiently used for prediction.A key challenge lies in the computation of the value functions, i.e. the expected maximum utility from any position in the network to a destination. The value functions are the solution to a system of non-linear equations. We propose an iterative method with dynamic accuracy that allows to efficiently solve these systems.We report estimation results and a cross-validation study for a real network. The results show that the NRL model yields sensible parameter estimates and the fit is significantly better than the RL model. Moreover, the NRL model outperforms the RL model in terms of prediction.     

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.     

基于客流导向的长大市域轨道交通组织优化方法研究

以北京市轨道交通平谷线为依托,基于线路功能定位和客流需求,对车辆选型、运营交路方案进行探索性创新研究。提出一种行车组织优化模型及求解算法,并以运力运量、工程投资、客流-车流匹配率等关键性指标为比选目标,确定该市域快线的最佳行车组织方案,以期为类似市域快线行车组织方案的合理选择提供理论支撑。     

Equilibrium analysis and route guidance in large-scale networks with MFD dynamics

Recent studies have demonstrated that Macroscopic Fundamental Diagram (MFD), which provides an aggregated model of urban traffic dynamics linking network production and density, offers a new generation of real-time traffic management strategies to improve the network performance. However, the effect of route choice behavior on MFD modeling in case of heterogeneous urban networks is still unexplored. The paper advances in this direction by firstly extending two MFD-based traffic models with different granularity of vehicle accumulation state and route choice behavior aggregation. This configuration enables us to address limited traffic state observability and to scrutinize implications of drivers’ route choice in MFD modeling. We consider a city that is partitioned in a small number of large-size regions (aggregated model) where each region consists of medium-size sub-regions (more detailed model) exhibiting a well-defined MFD. This paper proposes a route guidance advisory control system based on the aggregated model as a large-scale traffic management strategy that utilizes aggregated traffic states while sub-regional information is partially known. In addition, we investigate the effect of equilibrium conditions (i.e. user equilibrium and system optimum) on the overall network performance, in particular MFD functions.     

日常路径选择行为的模拟研究

假设司机只能根据自己的历史经验做出判断，本文据此设计了日常路径选择行为中的四种路径更新规则。应用基于多智能体的模拟环境-SeSAm，在简单网络上对四种规则进行模拟，比较和分析了模拟结果。     

磁浮列车悬浮导向方案研究

本文介绍了磁浮列车的基本原理、优越性及国内外研究状况，在对两种典型的常导悬浮与导向方案进行分析的基础上，着重比较了它们的一些重要性指标，以及实心导轨由于电磁铁运动产生涡流所引起的电磁阻力的分析与计算，最后选定方案。     

基于混沌蚁群算法的最短路径选择研究

如何解决最短路径选择问题一直是城市交通流诱导系统的关键之一.基于群体仿生理论的蚁群算法是解决此问题的一种方法,针对采用蚁群算法进行最短路径选择时易出现的陷入局部最优解问题,引入混沌理论,采用混沌蚁群算法利用混沌初始化进行改善个体质量和利用混沌扰动避免在蚁群算法搜索过程中陷入局部极值,同时降低了蚁群算法的时间复杂度,从而更好的解决了最短路径选择问题.     

深圳快速公交（BRT）1号线总体规划方案

深圳市汽车拥有量与机动化出行需求迅猛增长，迫切要求大力发展公共交通，规划在市区北部建设BBT1号线。论文阐明了深圳BBT1号线建设必要性，提出了线路方案，场站布局方案与运营线路组织方案。     

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交通流诱导系统的应用，使得传统的路网系统随机用户平衡发生了变化。本文把交通流诱导系统的效益费用和交通流诱导条件下的随机用户平衡结合起来，以交通流诱导效果和建设费用问题之间的相互关系为基础，寻找交通流诱导系统建设和随机网络平衡的综合优化方案。文章采用双层规划模型描述了系统最优和交通流诱导条件下的随机用户平衡之间的相互作用，并给出了灵敏度分析算法。