Research on passenger flow forecast of urban rail transit based on time-space correlation analysis

Xiaoxi Wang; Kun Zhi; Yunzhe Shi; Xiaoyan Qu

doi:10.1117/12.3011283

8 November 2023 Research on passenger flow forecast of urban rail transit based on time-space correlation analysis

Xiaoxi Wang, Kun Zhi, Yunzhe Shi, Xiaoyan Qu

Proceedings Volume 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023); 1292313 (2023) https://doi.org/10.1117/12.3011283
Event: 3rd International Conference on Artificial Intelligence, Virtual Reality and Visualization (AIVRV 2023), 2023, Chongqing, China

Abstract

The planning and construction of rail transit usually refer to passenger flow forecasting, so it has become an entry point for coordinating and improving rail transit operations. Based on the correlation between passenger flow and time and space, this paper conducts research on the prediction model of rail transit short-term passenger flow. This research selects Suzhou Rail Transit as the research object, processes and analyzes the data of the Automatic Fare Collection (AFC) system, and summarizes the temporal and spatial characteristics of passenger flow distribution at typical stations. On this basis, a combined model based on Radial Basis Function (RBF) neural network called parallel weighted neural network (PWNN) model is established. An example of data from Leqiao Station, a typical Suzhou Metro station, verifies the feasibility of the combined model and proves the superiority of the combined model.

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

Citation Download Citation

Xiaoxi Wang, Kun Zhi, Yunzhe Shi, and Xiaoyan Qu "Research on passenger flow forecast of urban rail transit based on time-space correlation analysis", Proc. SPIE 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023), 1292313 (8 November 2023); https://doi.org/10.1117/12.3011283

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