It is of great practical significance to analyze the passenger route decision of Urban Rail Transit under the condition of operation interruption. On the basis of defining the research scope of urban rail transit emergencies, based on AFC data, a recognition method for the spatiotemporal impact scope of rail passenger flow under emergencies is proposed. Taking the passengers affected by emergencies as the research object, this paper explores the components of generalized travel cost of passenger travel path, and builds a generalized travel cost function. Using the path search algorithm matching with the urban rail transit system, the probability of passengers choosing the path is calculated by logit model, so as to better predict the travel path of passengers. Combined with the operation interruption data of Chongqing rail transit, a new train timetable is formulated to calculate the number of passenger flow affected by emergencies. The travel path of the affected passengers is re planned, and the passenger flow is redistributed, so as to ensure the travel of passengers to the maximum extent and reduce the impact of emergencies. It has a certain reference value for emergency management of Urban Rail Transit under emergencies.
Passenger demand is the necessary foundation of urban rail transit operation management, and it’s very important to study the accuracy of short-term passenger flow prediction. However, the passenger flow is affected by many factors, so more comprehensive research is needed. This paper is based on multi-source data and the combined model. Firstly, the preliminary correlation between the passenger flow sequence and external factors is analyzed before the prediction, and the required features are extracted by using the convolutional neural network (CNN) and inputted into the prediction process. Also, the model is combined by Bi-directional Long Short-term memory network (Bi-LSTM) and attention mechanism (Attention), which can adapt to the nonlinearity and periodicity of short-term passenger flow prediction. Finally, the real example analysis shows that there is a certain correlation between subway passenger flow and multi-source data, and the CNN-BiLSTM-Attention model can improve the accuracy of passenger flow prediction and reduce errors, which is better than other traditional prediction methods.
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