Understanding the Resilience of Urban Rail Transit:Concepts,Reviews,and Trends

As the scale of urban rail transit(URT)networks expands,the study of URT resilience is essential for safe and efficient operations.This paper presents a comprehensive review of URT resilience and highlights potential trends and directions for future research.First,URT resilience is defined by three primary abilities:absorption,resistance,and recovery,and four properties:robustness,vulnerability,rapidity,and redundancy.Then,the metrics and assessment approaches for URT resilience were summarized.The metrics are divided into three categories:topology-based,characteristic-based,and performance-based,and the assessment methods are divided into four categories:topological,simulation,optimization,and datadriven.Comparisons of various metrics and assessment approaches revealed that the current research trend in URT resilience is increasingly favoring the integration of traditional methods,such as conventional complex network analysis and operations optimization theory,with new techniques like big data and intelligent computing technology,to accurately assess URT resilience.Finally,five potential trends and directions for future research were identified:analyzing resilience based on multisource data,optimizing train diagram in multiple scenarios,accurate response to passenger demand through new technologies,coupling and optimizing passenger and traffic flows,and optimal line design.

Research on the Impact and Strategies of Rail Transit Network Integration in the Context of Metropolitan Area Development:A Case Study of the Wuhan Metropolitan Area

The metropolitan area is one of the key focal points in the construction and development of China’s new urbanization.Urban integration is an emerging trend in metropolitan areas.This paper explores the traffic demand characteristics and economic aspects of rail transit within metropolitan regions and argues that the construction of an integrated urban rail transit network is an effective approach to support their development.Rail transit in metropolitan areas offers both technical and economic advantages,improving the efficiency of time and space resource utilization,fostering economic cooperation,and ultimately contributing to an integrated development model.However,the integration of rail transit networks faces several challenges,including road network planning,technical standards,and operational organization.Using the Wuhan metropolitan area as a case study,this paper analyzes the challenges of rail transit network integration and proposes strategic solutions for development.

Importance Analysis of Urban Rail Transit Network Station Based on Passenger

Current urban rail transit has become a major mode of transportation, and passenger is an important factor of urban rail transport, so this article is based on passenger and the degree of the road network structure, calculating the point intensity of stations of urban rail transit, and then reaching a station importance by integrating many point intensities in a survey cycle time, and getting the station importance of urban rail transit network through concrete examples.

Modified stochastic user-equilibrium assignment algorithm for urban rail transit under network operation

Based on the framework of method of successive averages(MSA), a modified stochastic user-equilibrium assignment algorithm was proposed, which can be used to calculate the passenger flow distribution of urban rail transit(URT) under network operation. In order to describe the congestion's impact to passengers' route choices, a generalized cost function with in-vehicle congestion was set up. Building on the k-th shortest path algorithm, a method for generating choice set with time constraint was embedded, considering the characteristics of network operation. A simple but efficient route choice model, which was derived from travel surveys for URT passengers in China, was introduced to perform the stochastic network loading at each iteration in the algorithm. Initial tests on the URT network in Shanghai City show that the methodology, with rational calculation time, promises to compute more precisely the passenger flow distribution of URT under network operation, compared with those practical algorithms used in today's China.

Rational scale of urban rail transit network based on system dynamics

Urban rail transit(URT) has been playing an important role in urban sustainable development with its advantages of high speed,large capacity,high efficiency and low pollution.Estimating URT network scale is the key to ensure the scientificity and feasibility of its construction.The existing studies on rational scale of URT network have not dealt with the interaction of supply and demand.This paper describes the establishment of a system dynamics model of rational URT network scale determination,considering the interaction between URT construction and city social economic development as well as the dynamic equilibrium of capital supply and traffic demand,and the verification of the model validity by applying it to the case of Wuhan City's URT construction.

Estimation method for a skip-stop operation strategy for urban rail transit in China

The skip-stop operation strategy （SOS） is rarely applied to Chinese urban rail transit networks because it is a simple scheme and a less universally popular transportation service. However, the SOS has performance advantages, in that the total trip time can be reduced depending on the number of skipped stations, crowds of passengers can be rapidly evacuated at congested stations in peak periods, and the cost to transit companies is reduced. There is a contradiction between reducing the trip time under the SOS and increasing the passengers＇ waiting times under an all-stop scheme. Given this situation, the three objectives of our study were to minimize the waiting and trip times of all passengers and the travel times of trains. A comprehensive estimation model is presented for the SOS. The mechanism through which the trip time for all passengers is affected by the SOS is analyzed in detail. A 0-I integer programming formulation is established for the three objectives, and is solved using a tabu search algorithm. Finally, an example is presented to demonstrate that the estimation method for the SOS is capable of optimizing the timetable and operation schemes for a Chinese urban rail transit network.

The Research of Urban Rail Transit Sectional Passenger Flow Prediction Method

This paper studies the short-term prediction methods of sectional passenger flow, and selects BP neural network combined with the characteristics of sectional passenger flow itself. With a case study, we design three different schemes. We use Matlab to realize the prediction of the sectional passenger flow of the Beijing subway Line 2 and make comparative analysis. The empirical research shows that combining data characteristics of sectional passenger flow with the BP neural network have good prediction accuracy.

Urban rail transit disruption management: Research progress and future directions

Urban rail transit (URT) disruptions present considerable challenges due to several factors: i) a high probability of occurrence, arising from facility failures, disasters, and vandalism;ii) substantial negative effects, notably the delay of numerous passengers;iii) an escalating frequency, attributable to the gradual aging of facilities;and iv) severe penalties, including substantial fines for abnormal operation. This article systematically reviews URT disruption management literature from the past decade, categorizing it into pre-disruption and post-disruption measures. The pre-disruption research focuses on reducing the effects of disruptions through network analysis, passenger behavior analysis, resource allocation for protection and backup, and enhancing system resilience. Conversely, post-disruption research concentrates on restoring normal operations through train rescheduling and bus bridging services. The review reveals that while post-disruption strategies are thoroughly explored, pre-disruption research is predominantly analytical, with a scarcity of practical pre-emptive solutions. Moreover, future research should focus more on increasing the interchangeability of transport modes, reinforcing redundancy relationships between URT lines, and innovating post-disruption strategies.

2023年世界城市轨道交通运营统计与分析综述

参考国际上较为通用的统计标准,将城市轨道交通分为地铁、轻轨和有轨电车三大类,对世界城轨交通运营现状进行统计。分析表明:截至2023年底,全球有79个国家和地区的563座城市开通了城市轨道交通系统,总里程超过43400.40 km,其中地铁、轻轨、有轨电车分别占50.07%、10.69%和39.24%;中国(含港澳台)累计有66座城市开通运营轨道交通,运营里程达11900.29 km,其中中国内地运营里程11232.65 km。2022年,全球59个国家的183座城市地铁累计运送乘客586.52亿人次,平均负荷强度0.81万人次/(km·d),其中中国(含港澳台)地铁年客流量为212.51亿人次。我国城市轨道交通持续稳步发展,线网规模和客流规模继续居全球第一。统计国务院52号文件发布后的中国轨道交通的线网规模数据,预测低运量城市轨道交通系统将在中国内地有较好的发展前景,特别是中西部地区,以促进城市绿色、可持续发展。同时,根据疫情前后全球主要国家(地区)和城市的客流数据,预计中国内地客流量将在2024—2025年恢复至疫情前水平。

跨线条件下城市轨道交通列车交路计划优化方法

跨线运营是城市轨道交通重点发展方向。针对跨线条件下列车交路计划编制问题,通过构造乘客出行时空网络图准确刻画复杂交路下的客流行为,建立以最小化列车走行公里与乘客出行时间成本为目标的混合整数规划模型。模型以列车交路方案、开行比例与频率为决策变量,并针对跨线交路与本线交路的行车冲突,考虑非等间隔发车,确定列车发车时刻。设计基于交路及开行频率搜索与运行间隔协调的定制分解算法。案例结果表明,列车跨线方向与乘客换乘主方向一致时,跨线交路可同时提升企业运营与乘客出行效益。换乘出行导致的断面客流量差异是跨线交路开行方案设置的主要依据。研究结论可为运营企业制定列车交路计划提供依据,有助于提高网络运输效率。

城市轨道交通列车加开计划编制模型研究

本文主要研究城市轨道交通在计划编制阶段由于客流不均衡需要在初始运行图加开列车的问题,在确保列车运行安全降低运营成本的前提下合理地加开列车实现运输能力的灵活配置。将列车运行过程形式化描述为事件-活动网络,构建了分时段多目标优化列车加开计划编制模型,提出了启发式决策规则与禁忌搜索算法相结合的两阶段求解算法。结合地铁运行实际数据,在高峰时段和平峰时段分别构建不同算例场景,验证了模型和算法的可行性和有效性。

基于多维可预知特征的TCN-LSTM城轨短期客流预测

地铁客流量波动受众多因素影响,准确的客流预测数据有利于制定更高效的行车控制方案和客流管控方案。为提高客流预测精度,提出一种基于多维可预知特征的时序卷积神经网络-长短期记忆神经网络模型(TCNLSTM)地铁短期客流预测方法。考虑外部因素的影响,引入Spearman相关系数分析并提取日期、天气等可预知特征及其状态集,以提升预测精度,缩小特征空间,克服了冗余特征数据导致的模型过于复杂问题;通过融合时序卷积神经网络(TCN)提取的客流时间序列特征和可预知特征状态集构建了长短期记忆神经网络(LSTM)层输入,组合模型学习客流与外部影响因素的长短期依赖,从而实现常规日、节假日、不同天气等多场景下的短期客流预测。基于某西南城市地铁刷卡交易数据,对比差分整合移动平均自回归模型(ARIMA)、TCN、LSTM及TCN-LSTM模型的短期客流预测结果,得出组合模型的总体平均绝对误差(MAE)值比其他方法低27%~48%,均方误差(MSE)值低13%~35%,平均绝对百分比误差(MAPE)值低2.8%~6.7%,上述3项指标均表明TCN-LSTM模型的客流预测效果更好。此外,对比实验表明通过融入提取的可预知特征数据,TCN-LSTM模型在测试集上的预测误差评价指标明显降低,所提方法能有效提高地铁短期客流预测精度。

考虑站点耦合关系的大型城市轨道交通网络级联失效分析

为了解析站点分布密集的大型城市轨道交通网络级联失效传播机理,提出改进的耦合映射格子模型.该模型结合大型城市轨道交通网络站点分布密集特征,量化耦合站点间客流演化及耦合站点抵抗力.为了衡量级联失效在大型城市轨道交通网络中的传播过程与结果,提出失效站点数量比与网络效率比指标.以上海市城市轨道交通网络为例,验证该模型的可行性.结果表明:当节点度高或进站客流量大的站点失效后,耦合站点间的级联失效规模是邻接站点间的约2倍,且由于耦合站点间的级联失效的影响,介数中心性大的站点失效所引起的级联失效传播速度最快.在考虑耦合站点间级联失效时,客流对大型城市轨道交通网络级联失效的作用力是拓扑结构的约9倍.研究结论可以为城市轨道交通网络的规划设计与事故管控提供科学的建议.

基于改进浮动单元的城市职住平衡时间尺度估计——以广州市为例

已有研究对城市职住平衡的测度往往存在可塑性面积单元问题,且忽略了交通网络在职住联系中的媒介作用。提出一种改进的浮动单元法,构建基于道路和城市轨道交通网络的数据集,以时间为职住分析尺度生成浮动单元,运用手机信令数据对职住平衡进行测度。以广州市为例,对就业中心和居住区的职住平衡时间尺度进行估计。研究发现:广州市中心城区职住平衡时间尺度分布呈圈层式空间特征,职住平衡时间尺度从一级就业中心向外围先减小再增大;城市轨道交通缩小了就业中心的职住平衡时间尺度;一级、二级(不处于一级就业中心的辐射范围内)和三级就业中心的职住平衡时间尺度分别为32~40 min、14~28 min和10~20 min,而居住组团的职住平衡时间尺度主要依附于就业中心且受到城市轨道交通的影响。最后,提出在合适的时间尺度下对职住空间结构进行评估和优化住房和就业等资源配置,并重视城市轨道交通对职住空间结构的影响。

北京市轨道交通线网规划探索与实践

作为超大城市的北京市进入新发展阶段,面临新发展要求,其轨道交通线网规划也需要进行适应性转变。为充分发挥轨道交通线网规划对于北京城市高质量发展的服务和引领作用,文章针对北京市轨道交通线网规划所面临的主要问题和新发展要求,在分析和总结轨道交通线网在规划对象、手段和范畴方面转型特征的基础上,从服务首都、城市协同、多网融合、精细服务、创新实施5方面探索北京市轨道交通线网规划新理念,并据此有针对性地提出相关实践建议,以期为进一步优化完善北京市轨道交通线网规划工作、推动轨道交通与城市的高质量融合发展提供参考和借鉴。

基于改进LSTM的城市轨道短时流量预测研究

针对LSTM应用于城市轨道短时流量预测存在的模型参数确定困难、对预测精度影响大的问题,采用改进的BA算法对模型参数进行优化。对传统BA算法,采用自适应策略来动态调整脉冲频率和蝙蝠速率,同时蝙蝠位置更新模型中引入随机扰动项,提高了BA的优化性能。采用BA对LSTM参数进行优化,提出了基于改进LSTM的城市轨道短时流量预测模型。将提出的模型应用于郑州地铁1号线,通过和BP神经网络预测模型、LSTM预测模型的对比,验证了所提出的改进LSTM预测模型具有更高的预测精度。

考虑双维波动性的地铁冲击负荷网储协同平抑策略

地铁列车频繁启停使其牵引负荷出现短时间大幅度的冲击,容易导致变压器绝缘加速老化、引发主变过载风险。针对这一问题,本文提出一种协同网储的地铁负荷冲击优化策略。首先,基于地铁负荷时序特性剖析冲击性负荷形成机理,建立考虑功率曲线特征的双维波动性指标,定量描述冲击程度。其次,分别以系统经济性最优与负荷冲击最小为上下层目标,建立网架拓扑与储能装置协调控制的双层优化模型,求解最优开关变动及储能调度策略。最后,以上海某地铁线路实际供电负荷为例,结合多场景进行验证分析,结果表明所提方法能有效平抑冲击性负荷,实现供电可靠性与经济性的提升。

基于WaveNet网络的盾构刀盘扭矩超前预测方法

[目的]刀盘扭矩是表征盾构掘进安全和设备运行状态的关键参数,为了解决刀盘扭矩预测难、掘进参数难以及时修正的问题,提出一种基于WaveNet网络的盾构刀盘扭矩超前预测方法。[方法]介绍了工况数据的预处理方法,并提出基于WaveNet网络的初始静态模型基本架构和构建方式;提取初始50 m掘进距离内的盾构施工监控历史数据,并构建训练集;基于当前状态前20个施工步的盾构监控数据,超前预测5个施工步后的刀盘扭矩;随着盾构掘进距离的增加,每隔5个施工步就利用新产生的数据集重新训练和更新模型,提出刀盘扭矩超前预测的长期动态模型;以济南轨道交通1号线玉符河站—王府庄站区间盾构隧道左线数据为例,对刀盘扭矩预测效果进行分析及验证。[结果及结论]前50 m掘进距离超前预测的刀盘扭矩值与实际值变化趋势基本一致,且其平均相对误差为10.07%,初始静态模型具有较高的预测精度。随着掘进距离的增加,初始静态模型相对误差从10%上升至30%左右,而连续更新的长期动态模型相对误差始终稳定在10%左右。长期动态模型每次的更新时间基本分布在1~6 s,平均耗时为3.92 s,可满足模型高效动态更新的需求。

基于再生能利用的地铁多车协同折返优化研究

折返站作为影响线路发车上限的重要因素,影响着整条线路的运营效率,也是能量浪费较为严重的地方。因此,有必要在兼顾线路运营效率的前提下减少列车折返运行的能耗。为此,通过提出基于再生制动能量利用的列车折返问题对列车的节能运营进行研究。通过建立底层网络图,构建以列车折返为基础考虑再生制动能量利用的能耗优化模型。为提高模型的求解能力,引入辅助变量将其转变为线性化模型。以上海地铁某折返站作为实例对象,借助Gurobi进行求解并对能耗等数据进行分析,证实模型有效。由该模型得到的折返时刻图在保证折返能力的同时,列车能耗与原始折返方式相比降低了5.74%,为考虑再生制动能量利用的列车折返运营方式提供一定的理论支撑与依据。

基于时空关联的时空图卷积神经网络城市轨道交通进站客流预测

[目的]准确的短时客流预测对于提高超大规模城市轨道交通线网的运营和管理效率具有重要意义,而目前现有研究对于深度挖掘时空关联性仍不够充分,为此基于短时客流的时空规律提出了基于客流时空关联特征的STGCN(时空图卷积神经网络)模型。[方法]首先,通过切比雪夫图卷积网络捕捉超大规模城市轨道交通网络的空间相关性,借助门控循环单元挖掘多时空关联特征下客流的时间相关性;然后,分析待预测车站历史客流数据相关性及OD(起讫点)客流数据相关性,以深入提取时空相关性;最后,结合客流时空关联特征建立STGCN模型。[结果及结论]以上海地铁江苏路站为例,进行短时进站客流预测,结果表明采用时空关联特征参数的预测结果较未加入特征参数的预测精度提高了16%,预测效果较优。