The passenger flow assignment in the rail transit network is the basis for determining the passenger spatiotemporal distribution and train operation organization plan. In previous studies, the passenger flow assignment problem mainly focused on lines within the same rail system. Few studies focus on lines with the integrated mode of cross-line and skip-stop operation between state and suburban railway due to fewer cases in practice. In this study, passenger congestion and fare policy are taken into account in the generalized travel cost function, and a passenger flow assignment model based on the path-sized logit (PSL) model considering train capacity is proposed. Meanwhile, a schedule-based spatiotemporal digraph is established to search for the shortest spatiotemporal travel path. Furthermore, an improved method of successive averages (MSA) algorithm is designed to solve the proposed model. The proposed model is verified by a numerical example. The sensitivities of three main parameters which influence the results are also analyzed. The results show that the assignment model based on PSL is practicable in integrated operation mode. The higher the passenger’s familiarity with network information, the more accurate the assignment results. State trains are more attractive when the fare is lower than 0.5 CNY/km or the hourly wage is higher than 50 CNY/h.

The construction of urban rail transit (URT) guides and promotes urban development. Different URT line construction schedule, including construction sequence (priority order of line construction) and construction timing (when to build), will have different effects on urban traffic and development. Therefore, the planning of construction schedule is an important part of URT network planning. At present, the determination of construction schedule is mainly based on qualitative analysis methods (i.e., experience, comparisons with other cities, and expert opinion) in engineering practice. In this study, based on an analysis of the main factors affecting the construction sequence and the construction timing data of existing URT lines, a quantitative double-level model of a construction schedule is proposed. The model consists of construction sequence and construction timing sub-models. The construction sequence sub-model employs an improved Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) with Rough Set method; the construction timing sub-model takes the results of the construction sequence model and the factors associated with urban development characteristics into account and presents an improved Logistic-β method. The model is verified using the Chengdu rail transit network as the case study. The results of the study show that the double-level calculation model could provide quantitative theoretical support for the construction schedule planning of URT network.

The planning for the scale of the urban rail transit network (URTN) is one of the key tasks of URTN planning. The scale should match the urban development (UD). A reasonable scale can improve travel efficiency, increase economic activities, and promote UD, while an unreasonable scale may consume more urban resources, fail to meet urban transportation demands, and even inhibit UD. Currently, the URTN scale is primarily determined by qualitative analyses and static indicators, which leads to the scale does not match UD perfectly. To determine a reasonable scale, a System Dynamics–Lotka–Volterra (SD-LV) model is constructed. The SD model is adopted to simulate the dynamic interaction between the URT and UD. The LV (Lotka–Volterra) model is employed to calculate the scale, in which the mutualism coefficients are proposed to characterize the mutualistic relationships between the URT and UD. The model is validated by using a dataset of the Beijing URTN from 2017 to 2021. The simulation errors of the URTN scale range from −4.3% to 1.32%, which demonstrates the robustness and effectiveness of the proposed model. The study offers quantitative theoretical insights for determining the reasonable scale of the URTN.

Metro stations serve as critical hubs for passenger gathering and scattering. Under disturbing scenarios, a station’s ability to respond to disturbances, named resilience, fundamentally governs the operational stability, sustainability and emergency performance of the metro network. Existing metro network resilience studies typically treated stations merely as topological nodes, making it impossible to account for the internal passenger flow organization and facility capacities of the station. The resilience of the station itself cannot be characterized and quantified. This study focuses on the metro station’s resilience. From the perspective of sustainable operation, considering the passenger flow management of the station, the station’s resilience is defined as the ability of the station to maintain its basic service capabilities and minimize the number of delayed passengers within the station during disturbances. A passenger delay coefficient is introduced to quantify variations in passenger delay volumes within the station. The total number of passengers entering and leaving a station is used to quantify its service capacity. A resilience measurement model for the station is constructed by coupling the passenger delay coefficient and the service capacity. A case study of a transfer station experiencing a sudden passenger surge is conducted for model validation, considering passenger flow control measures and train capacity constraints. The results demonstrate that the model measures the station’s resilience across varying passenger flow management strategies effectively. This study provides a quantitative tool for measuring metro station resilience, enabling emergency responses, operational optimization and policy formulation that support the sustainable and stable operation of metro stations and networks.

There is a typical coopetition relationship between the urban rail transit (URT) network scale and the urban development (UD) level. A reasonable URT network scale is essential for promoting sustainable UD. Currently, the determination of the URT network scale for planning primarily relies on qualitative approaches such as static estimation and analogical methods, which fail to dynamically reflect the coopetition relationship between URT and UD. An improved time-varying parameter Lotka–Volterra (LV) model derived from ecological theory is employed to describe the coopetition relationship between the URT network scale and the UD level. The sliding-window least squares method is applied to estimate parameters of the model. Based on the improved LV model, the lower bound of the URT network scale is obtained by solving for the minimum network scale required to promote sustainable UD under a cooperative relationship; the upper bound of the URT network scale is obtained by solving for the maximum network scale that urban resources can support under competitive conditions. The proposed model is validated using eight Chinese cities with different UD levels. The study offers quantitative theoretical insights for determining the reasonable URT network scale for planning.