Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

12-2021

Abstract

A robust Origin-Destination (OD) prediction is key to urban mobility. A good forecasting model can reduce operational risks and improve service availability, among many other upsides. Here, we examine the use of Graph Convolutional Net-work (GCN) and its hybrid Markov-Chain (GCN-MC) variant to perform a context-aware OD prediction based on a large-scale public transportation dataset in Singapore. Compared with the baseline Markov-Chain algorithm and GCN, the proposed hybrid GCN-MC model improves the prediction accuracy by 37% and 12% respectively. Lastly, the addition of temporal and historical contextual information further improves the performance of the proposed hybrid model by 4 –12%.

Keywords

Graph Convolutional Network (GCN), Markov Chain, public transportation, OD prediction, explainable AI (XAI)

Discipline

Databases and Information Systems | Theory and Algorithms | Transportation

Research Areas

Data Science and Engineering

Publication

2021 IEEE International Conference on Big Data (BigData): Orlando, FL, Virtual, December 15-18: Proceedings

First Page

1718

Last Page

1724

ISBN

9781665439022

Identifier

10.1109/BigData52589.2021.9671752

Publisher

IEEE Computer Society

City or Country

Los Alamitos, CA

Additional URL

https://doi.org/10.1109/BigData52589.2021.9671752

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