Currently, urban rail transit is facing the challenges of relatively high energy consumption as well as relatively low passenger service levels. Optimizing train operations is crucial for achieving energy savings, enhancing service quality, and ensuring safety. This paper reviews research progress on optimization based on passenger flow and energy-saving objectives. In terms of passenger flow, existing studies are categorized into four areas: timetable design, transport organization, coordinated control, and real-time optimization. However, limitations remain in handling unexpected passenger surges, multi-mode coordination, and algorithmic reliability. Regarding energy efficiency studies are summarized at three levels: integration with passenger flow, single-train control, and multi-train coordination. Challenges include real-time performance of models, coordination across different control levels, and robustness of solutions. Future research should focus on integrated full-link optimization, stronger robustness, and improved applicability in engineering practice.

This study examines the challenges of uneven distribution of passenger flow and long-distance commuting travel on suburban rail lines in metropolises posed by location disconnect between residence and workplace. To address these challenges, a two-tier operation plan optimization model, including the upper and the lower tiers, termed SubTOPO is proposed for more efficient operation of suburban rail transit. The upper tier aims to maximize the compatibility between train capacity and traffic volume, while the lower tier aims to minimize the total travel cost of passengers. Then, respective optimization algorithms corresponding to the two tiers, i.e., a spatial imbalance coefficient-based routing optimization algorithm (SICRO) and a mutation concept-based stopping scheme optimization algorithm (MCSSO), are proposed to solve the two-tier optimization model SubTOPO, finally giving the optimal routing and stopping schemes. The proposed formulation is validated with the actual operational data from Beijing Metro Line 15. Three scenarios are crafted through the integration of distinct routing and stopping options. Scenario 1 adopts a single full-length routing with all stops and Scenario 2 employs the optimized routing scheme with all stops, while Scenario 3 integrates the optimal routing and stopping schemes. Results show that Scenario 3 lowers total travel costs by 8.48% and 2.61% compared to Scenario 1 and Scenario 2, respectively. It also reduces arrival time at the terminal station by 17.49 min compared to Scenario 2. The findings demonstrate the efficacy of the derived optimal operation scheme in dealing with the uneven passenger flow distribution during peak hours and meeting the growing demand for efficient and direct travel options for long-distance passengers.

Optimizing train skip-stop operations is a key strategy for enhancing urban rail transit service quality and reducing operational costs. This paper systematically reviews research progress in this field, first outlining major stopping patterns (all-stop, zonal-stop, express/local, stop-skipping) and their research evolution. It then details skip-stop benefits including reduced travel time, balanced passenger flow, and lower energy consumption and emissions, noting the growing emphasis on energy efficiency aligned with dual-carbon goals. Current limitations include computationally intensive models unsuitable for real-time application and inadequate adaptation to dynamic passenger flow. Future research should integrate big data and AI for dynamic adaptive optimization using short-term forecasting, while enhancing system recovery capability and social equity in operational strategies.

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 data-driven. 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.

Severe weather events constitute a significant safety hazard for urban rail transit systems, emphasizing the importance of risk management. This paper introduces a resilience evaluation method for urban rail transit lines during adverse weather conditions, leveraging Bayesian networks. The proposed method encompasses a comprehensive resilience indicator system tailored to severe weather scenarios, encompassing topological characteristics, passenger organization, and equipment factors. A risk analysis framework utilizing a risk matrix is then applied to evaluate subsystem vulnerabilities under various weather conditions. Furthermore, a Bayesian network-based resilience evaluation approach is designed to integrate and analyze these diverse factors. To demonstrate the effectiveness of this approach, a case study is conducted using real-world data from the Changping Line of Beijing Subway, resulting in resilience scores for critical components like elevators, 400V, traction power supply, and on-board equipment, as well as an overall resilience value for the entire line. The results indicate that, on rainy days, the resilience levels of Changping Line's key indicators and the entire line are precisely predicted by the proposed method. Furthermore, the key indicators and the overall line are generally anticipated to exhibit high resilience, which closely aligns with actual operational performance. These findings not only establish the accuracy of the resilience evaluation method but also reinforce its practical relevance and applicability in real-world settings.

Considering the high complexity of actual railway line conditions, this paper proposes a two-level energy-efficient timetable optimization method empowered by time-energy Pareto solution to reduce energy consumption while maintaining the existing infrastructure unchanged. At the train-level, an efficient equivalence method for actual line conditions, including varying slopes, curves, and tunnels is proposed to formulate the train driving process as a multi-objective optimization model to balance the time cost and energy consumption. An improved non-dominated sorting genetic algorithm II (INSGA-II) enhanced by differential evolution (DE) and a new crowding distance (NCD) operator is further proposed to access the time-energy Pareto solution of the potential speed profile for a single train by comparing different control strategies. At the timetable-level, an integer linear programming (ILP) model is designed with the computed train-level Pareto solution to optimize the energy-efficient timetable by restricting the headway between trains, in which a novel operation time-based branch-and-bound (BBOT) method is proposed to enable quick search of optimal control strategy and thus allows for accurate output of the optimal timetable while retaining real simulation results. A case study on the actual operation data of the Beijing-Jinan section of the Beijing-Shanghai high-speed railway shows that the optimized timetable can save up to 18.69% of energy when the train is on time. In cases of train delays, the total savings under 8-min, 9-min, and 14-min delay scenarios are 4.69%, 9.93%, and 8.05%, respectively compared to the method using time-oriented strategies, which have demonstrated the effectiveness of the proposed two-level optimization formulation.

Urban rail transit is of great significance to alleviate the huge passenger flow pressure brought about by the urbanization process. However, the daily operation always consumes a considerable amount of energy. An effective and feasible energy-saving method is to optimize the train operation strategy while keeping the existing infrastructure unchanged. In this paper, considering the spatial distribution of passenger flow, a timetable optimization model for the energy-saving operation of trains is established to make the net energy consumption of trains and passengers’ traveling time achieve bi-objective optimization. A simulation-based non-dominated sorting genetic algorithm is designed to solve the model. With the optimal driving strategy of maximum acceleratio-cruising-coasting-maximum deceleration braking, a set of Pareto optimal solutions of time vs. energy consumption are obtained by optimizing the running time in the interstation sections and the dwell time at stations. A series of experiments are carried out using the actual data of the Changping Line in Beijing Metro. The results show that Pareto optimal solutions with uniform distribution and complete convergence under different passenger load conditions can be obtained by the proposed optimization model.Compared with the original timetable, the net energy consumption optimization rate of fixed train passenger load and the one considering the spatial difference of train passenger load can reach 5.5% and 18.79% respectively.

随着城市轨道交通网络的快速发展,客流需求与运力供给之间的矛盾愈发突出.文章针对城市轨道交通线网中发生大客流的线路,从供给侧和需求侧2个角度出发,研究客流调度策略的制定,提出3种客流调度策略:运力调配策略、协同限流策略、组合策略.在大客流发生时,可以单独采用运力调配策略或协同限流策略,当采用单一策略无法满足客流需求时采用组合策略,即在改变运力的基础上采用协同限流策略.文章首先构建运力调配模型和协同限流模型,然后进行案例分析,根据线路信息和客流数据对模型进行求解,并从乘客候车时间、列车走行公里、列车最大满载率等方面进行对比分析,验证模型的有效性.

大城市郊区线路的客流往往具有明显的长距离出行和时空分布不均衡特征,尤其在高峰时段存在因列车容量约束导致乘客滞留站台的现象,而开行不同站停方式的列车是解决上述问题的有效手段.在计算上下车、进站/换乘及留乘等客流数量基础上,本文建立以包括候车时间和旅行时间在内的乘客总出行成本最小为目标的优化模型;以是否停站的0-1变量对所有列车进行统一编码,通过设置最大触发灾变次数,设计基于灾变思想的改进遗传算法;定义研究时段相邻列车在各车站发车间隔的偏差均值和偏差离差为评价运行图发车间隔均衡性的指标,实现考虑列车容量约束的停站方案优化.以北京地铁15号线为案例,结果表明:改进遗传算法通过灾变操作可有效跳出局部最优;算法可得到设计的3种场景条件下的最优停站方案,相较于场景1,场景2和场景3目标值分别提升12.20%和4.28%;采用跳停策略,可以有效减少列车运行时间,相较于场景1,场景2和场景3的下行方向在到达终点站时分别节省17.49 s和17.97 s.

城市轨道交通作为城市公共交通体系的骨干,其便捷程度对社会经济发展和乘客服务水平产生较大影响.为评估城市轨道交通出行的便捷程度,文章提出一种基于时空分布的城市轨道交通出行便捷指数构建方法,该方法不仅可评估城市轨道交通全网出行便捷程度,还可对城市轨道交通站点出行便捷程度进行评估.此外,文章以北京市轨道交通为例,对站点层和全网层的城市轨道交通出行便捷程度进行定量和定性分析,证明该方法的有效性.相关研究可为城市轨道交通规划及建设提供参考.

Traffic infrastructure safety is a core topic in traffic construction and development. As the impact of global climate change becomes more and more significant, extreme weather brings more and more safety issues to the normal operation of subway systems. Therefore, it is an urgent issue in the construction of subway systems to fully prepare for extreme weather and improve system resilience under external disturbances. The resilience of a complex system generally refers to its ability to adapt to external disturbances and return to a functional state. As one of several key infrastructure systems in large cities, a subway system needs to be highly resilient to cope with various risks, and it needs to recover quickly under uncertain weather conditions and other external damage events. In order to achieve the goal of conducting a real-time resilience assessment of a subway system, this study adopts the Bayesian network and the traditional failure mode and effect analysis (FMEA) method to realize resilience assessment with multiple performance indicators. Combined with the risk matrix method from FMEA, multiple important indicators of a subway system under the influence of extreme weather are obtained. These important indicators are integrated into the resilience assessment of the subway system within a Bayesian method. In this paper, the feasibility and applicability of the proposed method are verified by taking the Changping Line of the Beijing subway under extreme rainfall weather (>10 mm) as a case.

In the condition of large passenger flow, subway station managers take measures of passenger flow control organization for reducing high safety operation risks at subway stations. The volume of passenger flow in urban railway network operation continues to increase and the Congestion of passenger flow is very high. Passenger flow control measures can greatly give birth to the pressure of transportation and ensure an urban rail transit system’s safe operation. In this paper, we develop a cloud model-based method for passenger flow control, which extends the four-level risk-control grade of a large passenger flow at facilities by considering its fuzzy and stochastic characteristics. Then, an efficient passenger flow control strategy for subway stations is made, where the control time and locations are simultaneously determined. Finally, a station in the Beijing subway is studied to test the validity of the proposed approach. The results show that the time of maximum queuing length is much shorter and the density of passenger flow is lower than existing methods in practice. With the in-depth study of complex network controllability, many studies have applied to control judgment and real network optimization. This paper analyzes the cloud-model-based method for passenger flow control at subway stations and therefore a new method can be incorporated for developing and optimizing control strategies. A few researchers have attempted to find the solution to the problem of crowding risk classification and the passenger flow control strategy. The focus of some studies simultaneously solves the passenger flow control with multiple stations.

由于存在人体之间相互遮挡、受环境变化影响较大、时序特征提取能力较弱等问题,现有的动作识别方法在精准度方面仍有不足,因此,提出了一种基于改进Slow-Only网络的骨骼点动作识别方法.首先,将骨骼关键点数据进行预处理,分别在时间和空间维度减少冗余信息;其次,基于Slow-Only网络,重新设计了时间卷积模块,以更好地提取视频帧所包含的时序信息;最后,增加了改进的注意力机制模块,以降低遮挡问题带来的影响.在NTU RGB+D数据集上进行了实验,实验结果表明该方法能有效地提升检测精度,并且在实际场景中具有应用价值.

For subway stations and other places with dense traffic, the effect of pedestrian detection is easily affected by occlusion. At the same time, the head and shoulder detection has strong anti occlusion ability and low computational requirements. In view of the existing problems, this paper proposes a head and shoulder detection algorithm based on yolov5 in complex scenes, and designs and adds a deformable convolution network and attention mechanism module in the feature extraction network to improve the ability of feature extraction. The experiment is designed to train and test the pedestrian head and shoulder data set in the subway complex environment. The experimental results show that the accuracy of pedestrian head and shoulder detection for small target is improved in subway complex scene.

行车调度员能力综合评价可为行车调度员的定级、选拔提供可靠依据.文章首先根据行车调度员的岗位职责,将行车调度员能力划分为基本能力、专业能力和管理能力,并作为一级评价指标.综合考虑行车调度岗位的作业特点,优选调度命令发布、列车运行计划管理、非正常情况下的行车调度、施工检修计划管理等11项重点作业内容作为二级评价指标,构建行车调度员能力综合评价指标体系.其次,结合行车调度岗位作业内容的多因素、模糊性等特点,运用AHP-模糊评价法对行车调度员能力进行综合评价.最后,以北京地铁某行车调度员考核成绩为例进行能力综合评价,判定该行车调度员的能力综合水平为优秀.

为提升城轨行车调度员在突发事件下的应急处置能力,需要加强行车调度员的实操能力培训.文章在总结分析城轨行车调度培训现状的基础上,利用应急预案数字化、语音识别等关键技术实现应急场景中虚拟岗位联动响应和智能推演处置流程,从行车调度、智能交互、智能评测和教员管理功能模块构建适用于行车调度员的多岗位配合联动、多故障场景下城轨行车调度模拟演练系统,最后对所搭建的北京地铁行车调度模拟演练系统总体架构及功能模块进行简要介绍.初步培训测试结果表明,该系统可有效提升行车调度员的应急处置能力,具有较高的应用及推广价值.

利用城市轨道交通线路的富余能力提供货运服务的理论与实践研究方兴未艾,大体可分为客货混运和客货混跑两种模式.目前理论研究多聚焦在客货混跑方案的优化上,实践则以服务于小批量货物的客货混运案例居多.基于日本北越快线、高铁极速达、桃园机场捷运以及德国包裹城际项目实证案例,归纳客货混运和客货混跑两种运输组织方式的特点.客货混运组织方案主要用于满足零散化的货运需求,利用车厢空间时应有必要的固定设施以保障安全;建议采用"运装分离"方案,以提升装卸效率,减少对客运作业的干扰.在客货混跑案例中,采用与客运列车速度接近的较高速度的货运专列,并在夜间进行运输,以保障时效性.

In urban rail transit system, comprehensive analysis of passenger flow status in station and accurate determination of facilities services level offer technological support for station safety operation management, passenger flow control, emergency disposal, etc. Firstly, on the basis of defining the layout of station structure, the evaluation method for service level of facility network is proposed by selecting facilities services level indicators and cloud synthesis theory. Secondly, the dynamic change of the facilities service level is analyzed based on the state membership relation and Markova state transition theory. We integrally realize the evaluation and dynamic prediction of the facilities service level. Finally, combined with station survey and data analysis, feasibility and practicality of service level evaluation method of facility network is verified by a simulation case study.

针对高峰时期城市轨道交通因有限运能,不足以满足乘客出行需求而引发的安全问题,需要采取客流控制策略来调节进入车站的客流量,以缓解车站拥挤.提出一种基于强化学习深度Q网络的多站协同控制模型,用来优化每个车站在一定时间内的进站量,以最小化地铁车站乘客的站台超限量、平均等待时间,提高客流控制强度的综合效益.以北京地铁八通线为例进行仿真实验,验证该方法的有效性.仿真结果表明,所提出的模型可以在客流控制强度较低的条件下有效地降低乘客等待时间,提高乘客出行效率,有助于缓解车站的乘客拥堵.