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

Topics: Analytics for Intelligent Transportation; Big Data & Vehicle Analytics; Big Data Analytics for Intelligent Transportation; Decision Support Systems; Information Systems and Technologies; Public Transportation Management; Reporting Tools; Traffic management Reporting Systems

Authors: Jaison Mulerikkal 1 ; Deepa Dixon 2 and Sajanraj Thandassery 2

Affiliations: 1 Dept. of Information Technology, Rajagiri School of Engineering and Technology,Kochi, 682 039, Kerala, India ; 2 Dept. of Computer Science and Engineering, Rajagiri School of Engineering and Technology, Kochi, 682 039, Kerala, India

Keyword(s): Passenger Flow, Short-Term, Long Short-Term Memory Network, Support Vector Regression.

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 spec ific 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. (More)

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Paper citation in several formats:
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 - VEHITS; ISBN 978-989-758-652-1; ISSN 2184-495X, SciTePress, pages 257-264. DOI: 10.5220/0011840800003479

@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 - VEHITS},
year={2023},
pages={257-264},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011840800003479},
isbn={978-989-758-652-1},
issn={2184-495X},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS
TI - A Path-Depended Passenger Flow Forecasting Model for Metro Rail Systems Using LSTM Neural Network
SN - 978-989-758-652-1
IS - 2184-495X
AU - Mulerikkal, J.
AU - Dixon, D.
AU - Thandassery, S.
PY - 2023
SP - 257
EP - 264
DO - 10.5220/0011840800003479
PB - SciTePress

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