Short-Term Prediction of Origin–Destination Passenger Flow in Urban Rail Transit Systems with Multi-Source Data: A Deep Learning Method Fusing High-Dimensional Features

Short-Term Prediction of Origin–Destination Passenger Flow in Urban Rail Transit Systems with Multi-Source Data: A Deep Learning Method Fusing High-Dimensional Features

Short-term origin–destination (OD) passenger flow forecasting is crucial for urban rail transit enterprises aiming to optimise transportation products and increase operating income. As there are large-scale OD pairs in an urban rail transit system, OD passenger flow cannot be obtained in real time (...

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Bibliographic Details
Main Authors: Huanyin Su, Shanglin Mo, Huizi Dai, Jincong Shen
Format: Article
Language:English
Published: MDPI AG 2024-10-01
Series:Mathematics
Subjects:
urban rail transit
OD passenger flow prediction
deep learning
high-dimensional features
multi-source data
Online Access:https://www.mdpi.com/2227-7390/12/20/3204
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