考虑均衡性的城市轨道交通线路负荷水平评估研究

线路负荷水平的评价工作是优化运输组织方案的重要前提。首先，本文分析了采用单一均值性指标表征负荷水平不能体现线路内负荷分布偏差的问题。为此，从城市轨道交通线路客流与运输能力匹配的角度出发，以列车负荷为评价单元，建立包括负荷均值和标准差率二维指标的线路负荷水平评估模型。负荷均值指标考虑了各评价单元加权计算，以反映每个评价单元对线路负荷水平的贡献程度，标准差率指标表征负荷均衡性。模型分析得出，线路所有区间的运力同等规模变化不影响标准差率指标，相对提高大客流区间或大客流方向上的输送能力能够改善线路负荷的均衡性。算例分析表明：客流方向不均衡系数超过 1.4 时，应考虑运力优化措施以提高运输能力利用的均衡；组织列车大小交路方案和不成对行车模式，能够提高线路负荷的均衡程度。     

公共交通设施配套规模研究——以中山市公共交通设施配套标准为例

针对城市公共交通场站用地紧张和居民公交出行不便等问题,文中根据部分城市公共交通配套标准经验,并以土地利用和交通需求等相关理论为基础,构建建设项目公共交通设施配套规模的计算方法。方法根据建设项目的规模,推算项目需要的公共交通设施配套规模,最后以中山市城区的相关交通参数为例,代入模型进行计算,得出中山市公共交通设施配套标准。     

Analyzing passenger train arrival delays with support vector regression

We propose machine learning models that capture the relation between passenger train arrival delays and various characteristics of a railway system. Such models can be used at the tactical level to evaluate effects of various changes in a railway system on train delays. We present the first application of support vector regression in the analysis of train delays and compare its performance with the artificial neural networks which have been commonly used for such problems. Statistical comparison of the two models indicates that the support vector regression outperforms the artificial neural networks. Data for this analysis are collected from Serbian Railways and include expert opinions about the influence of infrastructure along different routes on train arrival delays.     

Transport resilience and vulnerability: The role of connectivity

This paper aims to adopt a critical stance on the relevance and interpretation of the recently emerging concepts of resilience and vulnerability in transportation studies. It makes a clear distinction between engineering and ecological interpretations of these concepts and offers a systematic typology of various studies in this field. A core element in the study is the linkage between the aforementioned concepts and connectivity/accessibility in transport networks. The methodological findings in the study are put in perspective by addressing also such concepts as robustness, reliability and friability of transport systems.     

城市道路交通承载力研究综述

多年来，广泛的应用场景和领域使交通承载力的基础理论和量化研究方法等成为学术界的热点研究问题。作为城市交通可持续发展的重要指标，交通承载力向上与城市交通规划，向下与机动车需求管理融合，可以得到多个延伸的科研选题。在特定的时空网络中，一定的资源和环境约束条件下，城市道路交通承载力就是道路网络或基础设施现有或未来的承载状态，即交通资源达到最优配置、交通环境稳定时，道路交通设施单元或系统的承载能力或阈值。本文通过系统梳理城市道路交通承载力的国内外研究现状，从基础理论、量化研究方法和实践应用这3方面提出存在问题和发展建议。现有城市道路交通承载力研究存在理论体系不完善，评价量化方法有效性难以保证，评价指标选取缺少标准化，复杂系统承载力缺乏综合的内外部耦合协调分析，以及应用技术方法落后等问题。首先，未来可以建立较完善的交通承载力理论体系；其次，标准化评价指标，建立有效评价模型和耦合协调模型，研究交通系统内外部的耦合协调机制，提出协同优化策略；最后，改进应用技术方法，拓展自身及交叉领域应用。为城市道路交通承载力研究提供理论保障，为促进我国交通可持续文明发展建设进程提供有力支撑。     

考虑驾驶风格差异的高原公路危险路段识别研究

针对高原环境中驾驶人风格、生理变化与危险路段特征之间的潜在关联，提出一种基于驾驶状态的危险路段识别方法，辨识和分析不同风格驾驶人具有潜在风险的路段，并提出优化方案。首先，通过实车实验采集驾驶人行为及生理指标数据，使用DBSCAN(Density Based Spatial Clustering of Applications with Noise)得出驾驶风格类型，并依据行为特征对驾驶风格进行差异性 分析；其次，采用卷积神经网络、双向长短时记忆神经网络与注意力机制搭建危险状态识别模型，通过GPS(Global Positioning System)点位对应实现危险路段辨识，并基于驾驶风格差异，从驾驶人感知、操纵与生理角度对危险路段进行致因分析；最后，将生理与道路线形作为优化参考，以车速建议为着力点进行多元回归分析，并按照生理舒适域确定车速建议区间。结果表明：驾驶人根据行为特点分为谨慎、稳健和激进型，3类驾驶人在上行和下行途中的危险路段多为具有弯坡特征的组合型路段；海拔提升可加速危险驾驶状态的出现，各类驾驶人在上行时的紧张状态多源于弯坡组合值和转角值的增长，激进型驾驶人在坡度大于6%的直纵坡路段时亦会开始高度紧张；下行时，谨慎与激进型驾驶人在直纵坡坡度大于3%时易出现危险状态，激进型驾驶人在转角值大于80°且弯坡组合值大于50时亦存在驾驶风险。研究成果可满足高原公路人因事故预防的需求，为线形设计与交通管理措施制定提供理论依据。     

考虑匹配优先级的共享混合停车位租用与分配问题研究

为充分利用混合停车场的停车和充电资源，本文依据出行者需求的差异性设置匹配优先级，在此基础上以共享平台接受并出租车位的收益扣除拒绝请求的惩罚成本的总收益最大化为目标，以充电量不超过停车场负荷为约束条件，建立考虑匹配优先级的共享混合停车位租用与分 配(MPRA)模型。基于匹配优先级原则和模型特征改进蚁群算法，设置分块冲突矩阵，根据分块冲突矩阵设计蚂蚁信息素更新策略和路径选择策略并进行求解。最后算例证实了考虑匹配优先级的必要性，并分析了问题规模对MPRA分配方案的影响。研究结果表明：在充电需求匹配率方面，MPRA方案高于利润最大化方案，平均提升率为28.96%，这一优势与出行者数量或车位数量成反比，且利润提升率随着问题规模的增加而增加；车位数量有限时，MPRA方案相较于先到先停方案可以极大化提升车位利用率和充电需求匹配率，平均提升率分别为17.87%和113.96%。     

Categorizing bicycling environments using GPS-based public bicycle speed data

A promising alternative transportation mode to address growing transportation and environmental issues is bicycle transportation, which is human-powered and emission-free. To increase the use of bicycles, it is fundamental to provide bicycle-friendly environments. The scientific assessment of a bicyclist’s perception of roadway environment, safety and comfort is of great interest. This study developed a methodology for categorizing bicycling environments defined by the bicyclist’s perceived level of safety and comfort. Second-by-second bicycle speed data were collected using global positioning systems (GPS) on public bicycles. A set of features representing the level of bicycling environments was extracted from the GPS-based bicycle speed and acceleration data. These data were used as inputs for the proposed categorization algorithm. A support vector machine (SVM), which is a well-known heuristic classifier, was adopted in this study. A promising rate of 81.6% for correct classification demonstrated the technical feasibility of the proposed algorithm. In addition, a framework for bicycle traffic monitoring based on data and outcomes derived from this study was discussed, which is a novel feature for traffic surveillance and monitoring.     

Analysis of air traffic control operational impact on aircraft vertical profiles supported by machine learning

The Air Traffic Management system is under a paradigm shift led by NextGen and SESAR. The new trajectory-based Concept of Operations is supported by performance-based trajectory predictors as major enablers. Currently, the performance of ground-based trajectory predictors is affected by diverse factors such as weather, lack of integration of operational information or aircraft performance uncertainty.Trajectory predictors could be enhanced by learning from historical data. Nowadays, data from the Air Traffic Management system may be exploited to understand to what extent Air Traffic Control actions impact on the vertical profile of flight trajectories.This paper analyses the impact of diverse operational factors on the vertical profile of flight trajectories. Firstly, Multilevel Linear Models are adopted to conduct a prior identification of these factors. Then, the information is exploited by trajectory predictors, where two types are used: point-mass trajectory predictors enhanced by learning the thrust law depending on those factors; and trajectory predictors based on Artificial Neural Networks.Air Traffic Control vertical operational procedures do not constitute a main factor impacting on the vertical profile of flight trajectories, once the top of descent is established. Additionally, airspace flows and the flight level at the trajectory top of descent are relevant features to be considered when learning from historical data, enhancing the overall performance of the trajectory predictors for the descent phase.     

Stability of transportation networks under adaptive routing policies

Growing concerns regarding urban congestion, and the recent explosion of mobile devices able to provide real-time information to traffic users have motivated increasing reliance on real-time route guidance for the online management of traffic networks. However, while the theory of traffic equilibria is very well-known, fewer results exist on the stability of such equilibria, especially in the context of adaptive routing policy. In this work, we consider the problem of characterizing the stability properties of traffic equilibria in the context of online adaptive route choice induced by GPS-based decision making. We first extend the recent framework of “Markovian Traffic Equilibria” (MTE), in which users update their route choice at each intersection of the road network based on traffic conditions, to the case of non-equilibrium conditions, while preserving consistency with known existence and uniqueness results on MTE. We then exhibit sufficient conditions on the network topology and the latency functions for those MTEs to be stable in the sense of Lyapunov for a single destination problem. For various more restricted classes of network topologies motivated by the observed properties of travel patterns in the Singapore network, under certain assumptions we prove local exponential stability of the MTE, and derive analytical results on the sensitivity of the characteristic time of convergence to network and traffic parameters. The results proposed in this work are illustrated and validated on synthetic toy problems as well as on the Singapore road network with real demand and traffic data.