A Path-Depended Passenger Flow Forecasting Model for Metro Rail Systems Using LSTM Neural Network

Jaison Mulerikkal, Deepa Dixon, Sajanraj Thandassery

2023

Abstract

The primary goal of this work is to develop a framework for short term passenger flow prediction for metro rail transport systems. A reliable prediction of short-term passenger flow could greatly support metro authorities’ decision process. Both inflow and outflow of the metro stations are strongly associated with the travel demand within metro networks. Sequestered station-wise analysis ignores the spatial correlations existing between the stations. This paper tries to merge the spatial with the temporal by employing an indirect method of computing flow through O-D estimates for the same. Path-depended station-pairs of O-D flow are considered for employing a customized LSTM network. Experimental results indicate that the proposed passenger flow prediction model is capable of better generalization on short-term passenger flow than standard models of learning compared. This work also establishes that O-D prediction provides an indirect estimation procedure for passenger flow. The specific use case for this work is Kochi Metro Rail Limited (KMRL). A highlight of the work is that the whole analytics and modelling procedures are written on a customized scalable big-data platform (Jaison Paul Data Analytics Platform) JP-DAP which was developed prior to this work.

Paper Citation

in Harvard Style

Mulerikkal J., Dixon D. and Thandassery S. (2023). A Path-Depended Passenger Flow Forecasting Model for Metro Rail Systems Using LSTM Neural Network. In Proceedings of the 9th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS, ISBN 978-989-758-652-1, SciTePress, pages 257-264. DOI: 10.5220/0011840800003479

in Bibtex Style

@conference{vehits23,
author={Jaison Mulerikkal and Deepa Dixon and Sajanraj Thandassery},
title={A Path-Depended Passenger Flow Forecasting Model for Metro Rail Systems Using LSTM Neural Network},
booktitle={Proceedings of the 9th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,},
year={2023},
pages={257-264},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011840800003479},
isbn={978-989-758-652-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,
TI - A Path-Depended Passenger Flow Forecasting Model for Metro Rail Systems Using LSTM Neural Network
SN - 978-989-758-652-1
AU - Mulerikkal J.
AU - Dixon D.
AU - Thandassery S.
PY - 2023
SP - 257
EP - 264
DO - 10.5220/0011840800003479
PB - SciTePress