Short-Term Passenger Flow Prediction in Urban Rail Transit based on Hybrid Deep Learning Models
DOI:
https://doi.org/10.54097/nqxs4628Keywords:
CNN-LSTM model; urban rail transit; passenger flow prediction.Abstract
Urban rail transit systems are essential for efficient, reliable, and environmentally friendly transportation in modern cities. Correct and effective short-term passenger flow prediction is crucial for optimizing operational efficiency, enhancing service quality, and ensuring passenger safety. Traditional prediction methods often fail to capture the complex, non-linear, and dynamic patterns in urban rail transit systems. This study uses a hybrid deep learning model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to predict short-term passenger flow in urban rail transit. By integrating the strengths of CNN in capturing spatial features and LSTM in modeling temporal dependencies, the hybrid model aims to improve prediction accuracy. Using historical data from Hangzhou Metro, the study demonstrates the model's effectiveness in predicting passenger flow, which reasonably has a good fit. Additionally, models with different convolutional layers show different performances. These improved predictions offer valuable insights for transit authorities, enabling them to make more informed decisions regarding train scheduling, resource allocation, and emergency response planning. By anticipating passenger demand more accurately, authorities can optimize the deployment of trains, reduce waiting times, enhance passenger comfort, and improve overall service reliability.
Downloads
References
[1] Wu Qianqian, Sun Lin, Jia Furong, Liu Yi, Zhan Zichen. Research on Dynamic Passenger Flow Distribution of Rail Transit Based on Multi-Dimensional Euclidean Distance. In Advances in Smart Vehicular Technology, Transportation, Communication and Applications, 2022.
[2] Zhao Shuzhi, Tonghe Ni, Yang Wang, Xiang-Tao Gao. A New Approach to the Prediction of Passenger Flow in a Transit System. Computers & Mathematics with Applications, 2011.
[3] Zhou Wenzhong, Gao Chunhai, Tang Tao. Parallel Interactive Attention Network for Short-Term Origin–Destination Prediction in Urban Rail Transit. Applied Sciences, 2024.
[4] Tan Yifan, Liu Haixu, Pu Yun, Wu Xuemei, Jiao Yubo. Passenger Flow Prediction of Integrated Passenger Terminal Based on K-Means–GRNN. Journal of Advanced Transportation, 2021.
[5] Ming Ni, He Qing, Gao Jing. Forecasting the Subway Passenger Flow Under Event Occurrences with Social Media. IEEE Transactions on Intelligent Transportation Systems, 2017.
[6] Wang Jiaxuan, Wang Rui, Zeng Xin. Short-term Passenger Flow Forecasting using CEEMDAN Meshed CNN‐LSTM‐attention Model Under Wireless Sensor Network. IET Communications, 2022.
[7] Ma Jingye,Zeng Xin, Xue Xiaoping, Deng Ranran. Metro Emergency Passenger Flow Prediction on Transfer Learning and LSTM Model. Applied Sciences, 2022.
[8] Wu Jinxin, Li Xianwang, He Deqiang, Li Qin, Xiang Weibin. Learning Spatial-Temporal Dynamics and Interactivity for Short-Term Passenger Flow Prediction in Urban Rail Transit. Applied Intelligence (Dordrecht, Netherlands), 2023.
[9] Zhao Qingliang, Feng Xiaobin, Zhang Liwen, Wang Yiduo. Research on Short-Term Passenger Flow Prediction of LSTM Rail Transit Based on Wavelet Denoising. Mathematics (Basel), 2023.
[10] Han Yong, Wang Cheng, Ren Yibin, Wang Shukang, Zheng Huangcheng, Chen Ge. Short-Term Prediction of Bus Passenger Flow Based on a Hybrid Optimized LSTM Network. ISPRS International Journal of Geo-Information, 2019.
[11] Liu Ruijian, Wang Yuhan, Zhou Hong, Qian Zeqiang. Short-Term Passenger Flow Prediction Based on Wavelet Transform and Kernel Extreme Learning Machine. IEEE Access, 2019.
Downloads
Published
Conference Proceedings Volume
Section
License
Copyright (c) 2024 Highlights in Science, Engineering and Technology
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.