Qi Zhang

Virtual coupling (VC) has attracted increasing research attention as a train control concept for rail systems, enabling trains to operate at very short separations using cooperative, relative distance-based control. This paper reviews how VC affects railway operations across three interconnected layers: operation control, transport organization and system-level performance evaluation. At the control layer, we synthesize research on separation and collision avoidance models, trajectory planning, formation and cooperative control, and uncertainty-aware control methods, covering both model predictive control-based designs and alternative control strategies that address parametric uncertainty, heterogeneous braking, delays and cyber-attacks. At the planning layer, we survey line planning, timetabling, rolling stock circulation, train constitution and rescheduling models that embed VC-enabled headway relaxation, dynamic (de)coupling and platoon composition as decision variables, linking VC to passenger-oriented capacity allocation and disruption management. At the evaluation layer, we review signaling system safety assessment, capacity and headway analysis, and energy and environmental studies that quantify VC impacts under realistic signaling, infrastructure, and disturbance conditions. A thematic–methodological mapping highlights the prevalence of optimization and model predictive control, the limited integration between control and passenger-oriented planning, and the currently limited body of evidence for high-speed and heavy-haul applications. The review identifies consistent modeling patterns, critical gaps, and implementation challenges, and proposes a realistic research roadmap for treating VC as an operational and service innovation rather than a purely signaling upgrade.

The advancement of virtual coupling technology in urban rail transit has facilitated the online coupling and decoupling of trains, enabling a range of flexible transportation configurations, including various route types and adjustable formations. This study targets the fluctuating passenger demands on urban rail lines, aiming to minimize both passenger travel and operational costs. The model integrates constraints associated with virtual coupling, train operations, rolling stock circulation, and the interaction between virtually coupled trains and passenger arrivals. New decision variables are introduced to depict the train formation state under virtual coupling scenarios. An integrated optimization model for train diagrams and rolling stock circulation under virtual coupling conditions is developed, employing a genetic-simulated annealing algorithm informed by train operation simulations. A case study on an urban rail line during the morning peak examines the optimization of train diagrams for full-length and short-turn routes. Findings confirm that virtual coupling technology effectively adapts to lines with uneven passenger flow distribution, significantly enhancing the match between supply and demand, equalizing spatial and temporal traffic variations, and harmonizing the quality of passenger services with operational efficiency.

It has been proven that developing urban rail transit (URT) is the key to solving the traffic problems in big cities, and transport efficiency is essential for scientific line planning and construction, operation management and sustainable development of urban rail transit. This paper focuses on the transport efficiency of the URT network and adopts shadow efficiency theory creatively to combine factors of passengers, capacity, and efficiency effectively. A quantitative calculation method of system utility based on “double time penalty” is proposed. According to the characteristics of Beijing URT, this paper puts forward a single optimization strategy and combinatorial optimization strategy and analyzes the results with DEA. The simulation of Beijing subway network proves the availability of the method and its optimization effects.

In the urban rail transit system, train timetabling according to the dynamic passenger demand is important to offer high-quality service. Timetable optimization with the predetermined train route plan cannot synchronously consider the spatial and temporal characteristics of demand. Based on the Automated Fare Collection (AFC) data, this study proposes the integrated optimization (IO) to make route plan and timetable. A mixed-integer nonlinear programming (MINLP) model is formulated with the aim of reducing passenger penalty travel time and operation penalty cost. An improved non-dominated sorted genetic algorithm-II (Improved NSGA-II) is applied to deal with the bi-objective problem. A numerical experiment is used to prove the effectiveness and efficiency of the model and the algorithm. Then, the IO approach is applied in a bi-directional urban rail transit line. The results show that the IO performs the best compared with two other feasible strategies, namely the current optimization (CO) and phased optimization (PO).

Crowd at metro stations is usually a mixture of individuals and small groups of families or friends. However, limited research has focused on small group behaviours for metro safe evacuation evaluation and planning. In this study, a field observation at metro stations and a questionnaire survey were conducted to reveal the small group behaviour characteristics with different decision patterns and compactness. A cellular automaton (CA) based simulation model was proposed to reproduce small group behaviours of independent or joint decision pattern, with loose or close contact, reflecting the real-time trade-off between individual efficiency and group coherence. Impacts of small group behaviours on crowd dynamics were investigated by simulation experiments under diverse scenarios, in terms of the total number of evacuees, proportion of groups and different exits layouts. Simulation experiments revealed that joint decision pattern and close contact of small groups were more likely to lead to longer evacuation time, lower average speed and stronger interference on the individuals without a partner in the crowd. Since obvious deviations of evacuation efficiency estimation were observed under different small group conditions, it is necessary and important to consider small group behaviour effects on evacuation dynamics.

不耐烦情绪是一种因人而异的心理因素，且随环境实时变化。本研究构建了基于元胞自动机的行人微观模型研究双向行人流在不耐烦行为决策影响下的动态特征。模型定义了潜在势能场表达行人对可用空间的占用倾向，合理体现双向流行人决策的前摄效应。提出在行人运动过程中，根据瞬时状态测量、记录和更新行人个体不耐烦水平的方法，并以此触发个性化行为。对比研究了三种不同的不耐烦行为模式的作用效果。仿真试验结果显示，与不考虑不耐烦行为相比，当不耐烦水平用于触发横跨行为时，双向流在速度、流率和等待时间方面的表现均有改善；当不耐烦用于直接计算行人对有限空间的竞争力时，这三项指标则在大部分密度水平下均呈恶化。针对不耐烦作用强度与决策阈值的参数灵敏度分析显示了模型在不耐烦作用表现方面的灵活性。同时，分场景的仿真试验还揭示了低比例慢速行人、步道宽度及混合行为模式等对双向流效率的作用。

Crowd at metro stations is usually a mixture of individuals and small groups of families or friends. However, limited research has focused on small group behaviours for metro safe evacuation evaluation and planning. In this study, a field observation at metro stations and a questionnaire survey were conducted to reveal the small group behaviour characteristics with different decision patterns and compactness. A cellular automaton (CA) model was proposed to reproduce small group behaviours of independent or joint decision pattern, with loose or close contact, reflecting the real-time trade-off between individual efficiency and group coherence. Impacts of small group behaviours on crowd dynamics were investigated by simulation experiments under diverse scenarios, in terms of the total number of evacuees, proportion of groups and different exits layouts. Simulation experiments revealed that joint decision pattern and close contact of small groups were more likely to lead to longer evacuation time, lower average speed and stronger interference on the individuals without a partner in the crowd. Since obvious deviations of evacuation efficiency estimation were observed under different small group conditions, it is necessary and important to consider small group behaviour effects on evacuation dynamics.

This paper presents a framework to identify affected commuter passengers and their rerouting decisions under metro operational disruption. A cumulative prospect theory (CPT) based model is proposed to capture commuters’ mental activity and behavior from individual perspective considering heterogeneities, including time value, requirement of arrival time, sensitivity of cost and attitudes toward risk. Thus, commuters’ rerouting decision among multiple modes such as metro, bus, taxi and walking, can be effectively and accurately identified, which successfully overcomes the limitation of simplification and assumption in traditional methods. Case study of Beijing metro network and corresponding data of Automatic Fare Collection System (AFC) under disturbance in real world proves the effectiveness of the presented method.

This paper presents a grid-based micro-simulation model for passengers in Beijing metro stations. Potential field is introduced to the passenger movement modeling by the definition of tendency probability. Field data collected in Fuxingmen metro station in Beijing are used for the model parameter calibration and verification. Simulation experiments under typical scenarios run for the study of interactive effect between transfer performance and passenger number, and interval time between trains. According to the experiment results, relationship of transfer performance and influence factors are analysed. The study indicates that efficiency evaluation of transfer stations should consider integrated effects of passenger number, facility attributes and train timetable. The presented model is helpful for revealing and evaluation of transfer performance in stations.