CPT Model-Based Prediction of the Temporal and Spatial Distributions of Passenger Flow for Urban Rail Transit under Emergency Conditions,Journal of Advanced Transportation

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CPT Model-Based Prediction of the Temporal and Spatial Distributions of Passenger Flow for Urban Rail Transit under Emergency Conditions
Journal of Advanced Transportation ( IF 2.0 ) Pub Date : 2020-12-15 , DOI: 10.1155/2020/8850541
Wei Li ₁ , Min Zhou ₁ , Hairong Dong ₁

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Emergencies have a significant impact on the passenger flow of urban rail transit. It is of great practical significance to accurately predict the urban rail transit passenger flow and carry out research on its temporal and spatial distributions under emergency conditions. Urban rail transit operating units currently use video surveillance information mainly to process emergencies and rarely use computer vision technology to analyze passenger flow information collected. Accordingly, this paper proposes a passenger flow-based temporal and spatial distribution model for urban rail transit emergencies based on the CPT. First, this paper clarifies the categories and classification of urban rail transit emergencies, analyzes the factors affecting passenger route selection, and establishes a generalized travel cost model for passengers under emergencies. Second, this paper establishes a passenger route choice behavior model for urban rail transit based on the cumulative prospect theory. Finally, taking Beijing as an example, this paper analyzes passenger travel behavior under emergencies based on multiple logistic regression models and analyzes the impact of emergencies on rail transit travel behavior. The research results show that the cumulative prospect theory can better describe the route choice behavior of rail transit passengers under emergencies than the existing models, and this model is of great significance for handling urban rail transit emergencies. The model proposed in this paper can provide a theoretical basis for the government and relevant departments to formulate traffic management measures.

更新日期：2020-12-15

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