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The “left-behind” phenomenon occurs frequently in Urban Rail Transit (URT) networks with booming travel demand, especially during peak hours in a complex URT network, which makes passenger travel patterns more complicated. This paper proposes a methodology to mine passenger travel patterns based on fare transaction records from automatic fare collection (AFC) systems and Automatic Vehicle Location (AVL) data from Communication Based Train Control (CBTC) Systems or tracking systems. By introducing the concept of a sequence, a space-time-sequence trajectory model is proposed to simulate a passenger’s travel activities, including when they are left-behind. The paper analyzes passenger travel trajectory links and estimates the weight of each feasible trajectory under tap-in/tap-out constraints. The station time parameters, including access/egress and transfer walking-time parameters, are important inputs for the model. The paper also presents a maximum-likelihood approach to estimate these parameters from AFC transaction data and AVL data. The methodology is applied to a case study using AFC and AVL data from the Beijing URT network during peak hours to test the proposed model and algorithm. The estimation results are consistent with the results obtained from the authorities, and this finding verifies the feasibility of our approach.

Timetable is the core technical plan of the railway transportation organization, and it is the basis for the daily work. However, at present, China’s timetable management technology is relatively backward, which cannot meet the safety and efficiency of actual transportation. This paper analyzes the problems in the current situation of tiemetable management. Starting from the problem, the application research of big data platform is proposed. Firstly, the data is collected and normalized, then the safety analysis and index analysis are carried out, and finally the intelligent optimization is carried out.

This paper introduces a simulation system for real-world control problems during high-speed railway train operation, based on a semi-physical model of scale miniatures of railway lines, stations, and sections, signals and trains. This paper also designs a control command set based on the LocoNet-Protocol, puts forward an algorithm which translates train operation to a control command, builds an integrated model of high-speed train operation and control. The system could verify the principal parameters of high-speed train operation, and analyzes safety performances of high-speed railway. It can also simulate a whole transportation plan of a high-speed railway in off-line mode.

The evaluation index system of the train operation plan is a very important reference for high-speed railway management. With the high-speed railway being rapidly placed in the net, the study on the evaluation index system of the train operation plan has been put on the agenda. However, there was no comprehensive experience that could be directly followed. Based on this background, this paper establishes a comprehensive assessment system for management and designs new calculation methods for new indicators which include the equilibrium of the train diagram, the balance of EMU operation, and the adaptability of station operation.