Short-Term Passenger Flow Prediction for Urban Rail Transit Based on Machine Learning

Xiangxiang Wang; Jingxiao Tian; Yaqian Qi; Hanzhe Li; Yuan Feng

doi:10.5281/zenodo.11003963

Authors

Xiangxiang Wang University of Texas at Arlington
Jingxiao Tian San Diego State University
Yaqian Qi Baruch College
Hanzhe Li New York University
Yuan Feng Duke University

DOI:

https://doi.org/10.5281/zenodo.11003963

References:

31

Keywords:

Urban Rail Transit, Machine Learning, Passenger Flow Prediction, CNN, LSTM

Abstract

Short-term demand forecasting, often defined as less than an hour into the future, is critical to implementing dynamic control strategies and providing useful customer information in transportation applications. By understanding expected demand, bus operators can deploy real-time control strategies before demand surges and minimize the impact of anomalies on service quality and passenger experience. One of the most useful applications of traffic demand forecasting models is to predict congestion and vehicle congestion at station platforms.This paper explores the integration of machine learning into urban rail transit systems to enhance efficiency, reliability, and sustainability. By leveraging machine learning paradigms, the paper examines how advanced data analytics can revolutionize passenger flow prediction, train operations, maintenance strategies, and system optimization. Ultimately, the goal is to propel urban rail transit into a new era of intelligent and resilient transportation, contributing to sustainable and livable cities.

Author Biographies

Xiangxiang Wang, University of Texas at Arlington

Computer Science, University of Texas at Arlington, Arlington, TX, USA.

Jingxiao Tian, San Diego State University

Electrical and Computer Engineering, San Diego State University, CA, USA.

Yaqian Qi, Baruch College

Interdisciplinary Data Science , Duke University , North Carolina, USA.

Hanzhe Li, New York University

Computer Engineering, New York University, NY, USA.

Yuan Feng, Duke University

Interdisciplinary Data Science , Duke University , North Carolina, USA.

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