The rail transit system in China has established network operations in many major cities, such as Shanghai and Beijing. With the automatic fare collection (AFC) system, a passenger can buy one ticket at the origin station and complete travel between two stations of different rail lines, an improvement in convenience for passengers. However, there may be many paths between the origin station and the destination station, and the path chosen by passengers cannot be determined simply. Thus, the problem of passenger flow assignment on the network arises. The AFC system can record the entry and exit times of passengers exactly, and the train operation plan can be obtained by train timetables. Because of the entry and exit times and the train operations, the range of feasible paths chosen by one passenger may be narrow. A passenger flow assignment model was devised, and an algorithm process was designed that calculates the chosen possibility of every feasible path and then matches the passengers to the most likely paths. Actual passenger data from the Beijing subway were used to verify the model and algorithm.

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With the successful application of automatic fare collection (AFC) system in urban rail transit (URT), the information of passengers’ travel time is recorded, which provides the possibility to analyze passengers’ path-selecting by AFC data. In this paper, the distribution characteristics of the components of travel time were analyzed, and an estimation method of path-selecting proportion was proposed. This method made use of single path ODs’ travel time data from AFC system to estimate the distribution parameters of the components of travel time, mainly including entry walking time (ewt), exit walking time (exwt), and transfer walking time (twt). Then, for multipath ODs, the distribution of each path’s travel time could be calculated under the condition of its components’ distributions known. After that, each path’s path-selecting proportion can be estimated. Finally, simulation experiments were designed to verify the estimation method, and the results show that the error rate is less than 2%. Compared with the traditional models of flow assignment, the estimation method can reduce the cost of artificial survey significantly and provide a new way to calculate the path-selecting proportion for URT.

Passenger flow data are necessary for making and coordinating operational plans for urban rail transit (URT) systems; the availability and the service state of those systems directly influence the activity of a city and its people. Although many transit assignment models have been developed, the results of passenger flows estimated by these models as well as assumptions made in the estimation process, especially for large-scale, complex, and dynamically changing URT networks, had not been validated. This paper proposes a methodology that can validate existing URT assignment models by using automatic fare collection data and a cluster analysis technique. Initial applications to the URT system of Shanghai, China, which is one of the largest in the world, show that the proposed approach works well and can efficiently find the origin–destination pairs in which passengers' route choices are misestimated by those assignment models. The analysis suggests that several factors result in errors (for the URT assignment model used in Shanghai). These factors include the threshold for the difference in travel costs, a misrepresentation of the transferring cost, and inadequate values for the standard deviation. This information is useful for detecting errors in existing URT assignment models, leading to improvements.

The load capacity of urban rail transit station is of great significance to provide reference in station design and operation management. However, it is difficult to carry out quantitative calculation quickly and accurately due to the complex interaction among passenger behaviors, facility layout, and the limit capacity of single facility. In this paper, the association network of facilities is set up based on the analysis of passenger service chain in station. Then the concept of cascading failure is introduced to the dynamic calculation model of load capacity, which is established on the user-equilibrium allocation model. The solution algorithm is optimized with node attack strategy of complex network to effectively reduce the computational complexity. Finally, a case study of Lujiabang Road Station in Shanghai is carried out and compared with the simulation results of StaPass, verifying the feasibility of this approach. The proposed method can not only search for the bottleneck of capacity, but also help to trace the loading variation of facilities network in different scenarios, providing theoretical supports on passenger flow organization.