基于Q-learning 的定制公交跨区域路径规划研究

考虑城市大客流通勤者跨区域出行需求，结合城市公交线网中乘客出行密集、客流走向规律等特点，提出一种跨区域定制公交的搭乘方案. 通过改进的Q-learning 模型对公交线路进行优化，为城市通勤者提供更加便捷和高效的出行服务. 通过综合路段拥堵状态、乘客需求及居民小区位置，设定了Q-learning 强化学习的奖惩函数，提升定制公交区域路径的直线系数、满载率、通行时间. 结果表明，所提出的改进方法能够降低通勤者跨区域通行的旅行时间，有效提高髙峰时段定制公交线网的通行效率.     

信号交叉口行人二次过街下右转车通行能力与延误估计模型

在充分分析典型四相位交叉口行人二次过街设置前、后的行人流与右转车流冲突的前提下，以行人过街时间占有率和行人群到达分布作为分析指标，利用可插车间隙理论得出行人单向通行和双向通行条件下的右转车通行能力计算公式；根据行人流随机消散和集中消散的不同特征，应用随机分布理论推导出右转车穿越行人流的延误模型；并通过算例对比分析行人二次过街设置前、后右转车通行能力和延误的变化值。结果表明，除了在少数行人流量比较大的情况下， 行人二次过街的设置会小幅度减少右转车的延误；在其他大多数情况下，行人二次过街设置后， 右转车的通行能力将受到限制，延误增大，其中，平均通行能力降低了16.68%，平均延误时间增大 了21%，所以，当右转车交通需求较大时，需同时考虑行人和右转车的交通运行状态，优化设计是否采用行人二次过街，避免右转车超出极限忍耐时间而增大与行人冲突的概率。     

基于行程时间影响的关键路段识别与查找

从路段实际功能出发，提出基于路段与路径行程时间序列的相关性识别关键路段的方法.借鉴蒙特卡洛思想，以真实数据构造10万条随机路径验证该方法的可行性，并识别出对上海市路网行程时间有关键影响的路段集合.以上述集合为参照，利用模糊聚类及迭代累计平方和算法提取路段行程时间序列特征并构造两个新变量，结合基础属性建立二项Logit模型，从而主动查找关键路段.比较该模型与基础模型、随机分类器查找效果表明：基于最大归一化行程时间曲线聚类，其结果对关键路段识别模型的性能有提升效用；行程时间对数差分序列的结构性变点在路网和路段级别均有明显时间聚集特性，虽然其个数与路段关键性无明显关系，但其与常见波动程度指标相关性小，可保留用于描述行程时间波动常发性和聚集性.     

The identification and empirical characterization of vehicular (Lagrangian) fundamental diagrams in multilane traffic flow

Traditional macroscopic traffic flow modeling framework adopts the spatial–temporal coordinate system to analyze traffic flow dynamics. With such modeling and analysis paradigm, complications arise for traffic flow data collected from mobile sensors such as probe vehicles equipped with mobile phones, Bluetooth, and Global Positioning System devices. The vehicle‐based measurement technologies call for new modeling thoughts that address the unique features of moving measurements and explore their full potential. In this paper, we look into the concept of vehicular fundamental diagram (VFD) and discuss its engineering implications. VFD corresponds to a conventional fundamental diagram (FD) in the kinematic wave (KW) theory that adopts space–time coordinates. Similar to the regular FD in the KW theory, VFD encapsulates all traffic flow dynamics. In this paper, to demonstrate the full potential of VFD in interpreting multilane traffic flow dynamics, we generalize the classical Edie's formula and propose a direct approach of reconstructing VFD from traffic measurements in the vehicular coordinates. A smoothing algorithm is proposed to effectively reduce the nonphysical fluctuation of traffic states calculated from multilane vehicle trajectories. As an example, we apply the proposed methodology to explore the next‐generation simulation datasets and identify the existence and forms of shock waves in different coordinate systems. Our findings provide empirical justifications and further insight for the Lagrangian traffic flow theory and models when applied in practice. Copyright © 2015 John Wiley & Sons, Ltd.     

In search of surrogate safety indicators for vulnerable road users: a review of surrogate safety indicators

Surrogate indicators are meant to be alternatives or complements of safety analyses based on accident records. These indicators are used to study critical traffic events that occur more frequently, making such incidents easier to analyse. This article provides an overview of existing surrogate indicators and specifically focuses on their merit for the analyses of vulnerable road users and the extent to which they have been validated by previous research. Each indicator is evaluated based on its ability to consider the collision risk, which can be further divided into the initial conditions of an event, the magnitude of any evasive action and the injury risk in any traffic event. The results show that various indicators and their combinations can reflect different aspects of any traffic event. However, no existing indicator seems to capture all aspects. Various studies have also focused on the validity of different indicators. However, due to the use of diverse approaches to validation, the large difference in how many locations were investigated and variations in the duration of observation at each location, it is difficult to compare and discuss the validity of the different surrogate safety indicators. Since no current indicator can properly reflect all the important aspects underlined in this article, the authors suggest that the choice of a suitable indicator in future surrogate safety studies should be made with considerations of the context-dependent suitability of the respective indicator.     

基于OpenStreetMap的城市自行车网建模与多判据路径规划

自行车交通是一种健康、绿色、环保的交通出行方式,合理地规划自行车出行路线对于满足骑行者多元化出行需求、构建安全规范的城市交通出行环境具有重要的意义.在对真实骑行者路径选择行为深入分析的基础上,提出了基于Open Street Map的城市自行车网络的构建方法及基于其上的多判据自行车路径优化的数学模型,并给出了求解该模型的一种基于聚类的最优多判据路径规划方法.实验仿真结果表明,该模型及方法能够生成满足骑行者真实需求的多判据路线方案.     

Including congestion effects in urban road traffic CO2 emissions modelling: Do Local Government Authorities have the right options?

Tailpipe emissions from vehicles on urban road networks have damaging impacts, with the problem exacerbated by the common occurrence of congestion. This article focuses on carbon dioxide because it is the largest constituent of road traffic greenhouse gas emissions. Local Government Authorities (LGAs) are typically responsible for facilitating mitigation of these emissions, and critical to this task is the ability to assess the impact of transport interventions on road traffic emissions for a whole network.This article presents a contemporary review of literature concerning road traffic data and its use by LGAs in emissions models (EMs). Emphasis on the practicalities of using data readily available to LGAs to estimate network level emissions and inform effective policy is a relatively new research area, and this article summarises achievements so far. Results of the literature review indicate that readily available data are aggregated at traffic level rather than disaggregated at individual vehicle level. Hence, a hypothesis is put forward that optimal EM complexity is one using traffic variables as inputs, allowing LGAs to capture the influence of congestion whilst avoiding the complexity of detailed EMs that estimate emissions at vehicle level.Existing methodologies for estimating network emissions based on traffic variables typically have limitations. Conclusions are that LGAs do not necessarily have the right options, and that more research in this domain is required, both to quantify accuracy and to further develop EMs that explicitly include congestion, whilst remaining within LGA resource constraints.     

车路协同下考虑绿波协调的公交优先控制

在绿波协调控制交叉口群中,为分析公交优先控制对后续交叉口群的扰动,基于车流运行时间偏移分布,以概率期望描述了交叉口各相位绿时左端和右端时长变化引起的后续交叉口群在绿波带内、绿波带间的延误变化;采用组合优化的方法,以交叉口群在车速引导下的公交通行效益优化为上层模型,以交叉口群在公交优先控制下的延误优化为下层模型,对公交引导车速和信号控制参数进行协同优化.通过算例分析表明,公交优先控制模型有效提升了交叉口整体通行效益,最大化减小了对周边交叉口群的不利影响.     

考虑移步需求的无桩型共享单车动态调度研究

共享单车系统自发的不均衡性导致单车数量分布与用户需求分布之间产生偏移，降低了系统服务能力，需要调度实现再平衡. 现有动态调度算法缺乏考虑起点车辆供给不足，用户在出行过程中“再次”取车的移步需求，难以准确识别用户真实的出行需求分布，降低了调度效果. 本文提出以用户出行选择行为为下层，以调度车辆路径规划为上层的双层规划模型框架，设计结合仿真系统的启发式求解算法. 算例场景基于上海市虹口、杨浦区共享单车历史出行数据搭建，并进行网格化处理. 算例结果表明，模型能有效识别移步需求，提高共享单车的供需匹配能力. 针对各类调度资源配置情况，共享单车的供需匹配率提升18.07%~ 19.89%，提高了共享单车系统的管理效率.     

基于时延特性建模的多断面短时交通流预测

针对现有交通流预测方法未充分考虑多断面车流演变规律，提出基于时延特性建模的时空相关性计算方法. 该方法采用对不同断面、不同时刻交通流的分布相似性度量，对输入的车辆到达数据序列进行切割构建时空相似度矩阵，得到相邻断面之间的时延参数. 基于时延特性建模，将多断面之间的流量信息进行融合，使用长短时记忆(LSTM)网络进行流量预测. 通过对实际路段数据的预测和结果分析，验证所提方法的有效性和实用性.