Context-aware graph convolutional network for dynamic origin-destination prediction

Context-aware graph convolutional network for dynamic origin-destination prediction

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 perfo...

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Bibliographic Details
Main Authors: NATHANIEL, Juan, ZHENG, Baihua
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2021
Subjects:
Graph Convolutional Network (GCN)
Markov Chain
public transportation
OD prediction
explainable AI (XAI)
Databases and Information Systems
Theory and Algorithms
Transportation
Online Access:https://ink.library.smu.edu.sg/sis_research/6922
https://ink.library.smu.edu.sg/context/sis_research/article/7925/viewcontent/Paper__1_.pdf
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Institution: Singapore Management University
Language: English
Description
Summary: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%.

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