research-article

Transfer Learning in Traffic Prediction with Graph Neural Networks

Authors:

Xiaozhuang Song,

James J.Q. YuAuthors Info & Claims

2021 IEEE International Intelligent Transportation Systems Conference (ITSC)

Pages 3732 - 3737

https://doi.org/10.1109/ITSC48978.2021.9564890

Published: 19 September 2021 Publication History

Abstract

Statistics on urban traffic speed flows are essential for thoughtful city planning. Recently, data-driven traffic prediction methods have become the state-of-the-art for a wide range of traffic forecasting tasks. However, many small cities have a limited amount of traffic data available for building data-driven models due to lack of data collection methods. With the acceleration of urbanization, the need for traffic construction of small and medium-sized cities is imminent. To tackle the above problems, we propose a TransfEr lEarning approach with graPh nEural nEtworks (TEEPEE) for traffic prediction that can forecast the traffic speed in data-scarce areas with massive value data from developed cities. In particular, TEEPEE uses graph clustering to divide the traffic network map into multiple sub-graphs. Graph clustering captures more spatial information in the transfer process. To evaluate the effectiveness of TEEPEE, we conduct experiments on two realworld datasets and compare them with other baseline models. The results demonstrate that TEEPEE is among the best efforts of baseline models. We provide a comprehensive analysis of the experimental results in this work.

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Cited By

Ouyang XYang YZhang YZhou WWan JDu S(2023)Domain adversarial graph neural network with cross-city graph structure learning for traffic predictionKnowledge-Based Systems10.1016/j.knosys.2023.110885278:COnline publication date: 25-Oct-2023
https://dl.acm.org/doi/10.1016/j.knosys.2023.110885
Lv ZWang XCheng ZLi JLi HXu Z(2023)A new approach to COVID-19 data miningData & Knowledge Engineering10.1016/j.datak.2023.102193146:COnline publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1016/j.datak.2023.102193

Index Terms

Transfer Learning in Traffic Prediction with Graph Neural Networks
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information systems applications
    1. Data mining

Index terms have been assigned to the content through auto-classification.

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2021 IEEE International Intelligent Transportation Systems Conference (ITSC)

Sep 2021

4060 pages

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Published: 19 September 2021

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Cited By

Ouyang XYang YZhang YZhou WWan JDu S(2023)Domain adversarial graph neural network with cross-city graph structure learning for traffic predictionKnowledge-Based Systems10.1016/j.knosys.2023.110885278:COnline publication date: 25-Oct-2023
https://dl.acm.org/doi/10.1016/j.knosys.2023.110885
Lv ZWang XCheng ZLi JLi HXu Z(2023)A new approach to COVID-19 data miningData & Knowledge Engineering10.1016/j.datak.2023.102193146:COnline publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1016/j.datak.2023.102193

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