In the extensive urban rail transit network, interruptions will lead to service delays on the current line and spread to other lines, forcing many passengers to wait, detour, or even give up their trips. This paper proposes an event-driven simulation method to evaluate the impact of interruptions on passenger flow distribution. With this method, passengers are regarded as individual agents who can obtain complete information about the current traffic situation, and the impact of the occurrence, duration, and recovery of interruption events on passengers’ travel decisions is analyzed in detail. Then, two modes are used to assign passenger paths: experience-based pre-trip mode and response-based entrap mode. In the simulation process, the train is regarded as an individual agent with a fixed capacity. With the advance of the simulation clock, the network loading is completed through the interaction of the three agents of passengers, platforms, and trains. Interruption events are considered triggers, affecting other agents by affecting network topology and train schedules. Finally, taking Chongqing Metro as an example, the accuracy and effectiveness of the model are analyzed and verified. And the impact of interruption on passenger flow distribution indicators such as inbound volume, outbound volume, and transfer volume is studied from both the individual and overall dimensions. The results show that this study provides an effective method for calculating the passenger flow distribution of an extensive urban rail transit network in the case of interruption.

In a congested large‐scale subway network, the distribution of passenger flow in space‐time dimension is very complex. Accurate estimation of passenger path choice is very important to understand the passenger flow distribution and even improve the operation service level. The availability of automated fare collection (AFC) data, timetable, and network topology data opens up a new opportunity to study this topic based on multisource data. A probability model is proposed in this study to calculate the individual passenger’s path choice with multisource data, in which the impact of the network time‐varying state (e.g., path travel time) on passenger path choice is fully considered. First, according to the number and characteristics of OD (origin‐destination) candidate paths, the AFC data among special kinds of OD are selected to estimate the distribution of passengers’ walking time and waiting time of each platform. Then, based on the composition of path travel time, its real‐time probability distribution is formulated with the distribution of walking time, waiting time, and in‐vehicle time as parameters. Finally, a membership function is introduced to evaluate the dependence between passenger’s travel time and the real‐time travel time distribution of each candidate path and take the path with the largest membership degree as passenger’s choice. Finally, a case study with Beijing Subway data is applied to verify the effectiveness of the model presented in this study. We have compared and analysed the path calculation results in which the time‐varying characteristics of network state are considered or not. The results indicate that a passenger’s path choice behavior is affected by the network time‐varying state, and our model can quantify the time‐varying state and its impact on passenger path choice.

The ever-increasing travel demand has brought great challenges to the organization, operation, and management of the subway system. An accurate estimation of passenger flow distribution can help subway operators design corresponding operation plans and strategies scientifically. Although some literature has studied the problem of passenger flow distribution by analyzing the passengers’ path choice behaviors based on AFC (automated fare collection) data, few studies focus on the passenger flow distribution while considering the passenger–train matching probability, which is the key problem of passenger flow distribution. Specifically, the existing methods have not been applied to practical large-scale subway networks due to the computational complexity. To fill this research gap, this paper analyzes the relationship between passenger travel behavior and train operation in the space and time dimension and formulates the passenger–train matching probability by using multi-source data including AFC, train timetables, and network topology. Then, a reverse derivation method, which can reduce the scale of possible train combinations for passengers, is proposed to improve the computational efficiency. Simultaneously, an estimation method of passenger flow distribution is presented based on the passenger–train matching probability. Finally, two sets of experiments, including an accuracy verification experiment based on synthetic data and a comparison experiment based on real data from the Beijing subway, are conducted to verify the effectiveness of the proposed method. The calculation results show that the proposed method has a good accuracy and computational efficiency for a large-scale subway network.

Capacity provides important information about the upper limit for the systematic optimization of multimodal transport networks. Extensive research has been conducted on single-mode capacity evaluation, ignoring the fact that the capacity of each transportation mode can be correlated. Despite some recent efforts to model the capacity of multimodal transport networks, existing methods still fail to fully explain multimodal combined travel. To tackle these issues, this study models the capacity of multimodal transport networks with combined travel. Firstly, a multimodal transport network with transfer is represented by a modified supernetwork. In order to describe the traveler’s travel process from original to destination, the hyperpath and subpath are defined and the disutility of these two paths is formulated. Secondly, combined travel is characterized in the relationship between O-D demands and links flows. The relationship matrix is obtained using a logit-based stochastic user equilibrium model. Then, the capacity of the multimodal transport network is modeled with the relationship matrix in link capacity constraints., An approximate iteration algorithm (AIA) is designed based on the proposed model. Finally, numerical examples show that the AIA performs better than the sensitivity analysis-based algorithm (SAB) and genetic algorithm (GA) on both the optimal value and the time-consuming, and the capacity of multimodal transport networks will be overestimated if the combined travel is not considered.