OD prediction of urban rail transit passenger flow based on passenger flow trend characteristics

Yubian Wang; Xiang Liu; Erofeev Alexander Alexandrovich

doi:10.1117/12.3011333

8 November 2023 OD prediction of urban rail transit passenger flow based on passenger flow trend characteristics

Yubian Wang, Xiang Liu, Erofeev Alexander Alexandrovich

Proceedings Volume 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023); 1292310 (2023) https://doi.org/10.1117/12.3011333
Event: 3rd International Conference on Artificial Intelligence, Virtual Reality and Visualization (AIVRV 2023), 2023, Chongqing, China

Abstract

In the construction of urban rail transit, the space-time distribution of average daily passenger flow in the traffic network is analyzed based on important indicators such as average daily passenger flow, average passenger flow and congestion flow. Based on the data indicators, the organization and management of rail transit line connection should be planned, designed and operated more reasonably, which is to further enhance the technicality of the rail transit operation. It is one of the effective methods to predict the average daily passenger flow of the running track in some periods basing on the distribution characteristics of the average daily passenger flow and the variation model. The model established in this paper first sets the entrance and exit of the subway station as the space trajectory. Then, it regards the fixed parameters as the local function variables, and considers the real-time dynamic parameters as the external function. Considering the characteristics of the external function parameters in space, it fits with the time model accuracy, and dilutes the parameter function. Considering the driving effect of urban subway on the overall traffic environment, the genetic variation is carried out in the spatial and temporal trajectories. The local and global prediction variables are mixed with the change of time and geographical location, which has influenced the average daily commuting passenger flow. The research content of this paper can formulate rail transit travel plans suitable for passenger demand and formulate access management rules for subway stations. It also optimizes station traffic rate, thereby reducing operating costs, improving the return on investment in subway operation, and giving full play to the contribution of the urban rail transit construction.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yubian Wang, Xiang Liu, and Erofeev Alexander Alexandrovich "OD prediction of urban rail transit passenger flow based on passenger flow trend characteristics", Proc. SPIE 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023), 1292310 (8 November 2023); https://doi.org/10.1117/12.3011333

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