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A spatial-temporal framework including traffic diffusion for short-term traffic prediction

Published: 16 May 2020 Publication History

Abstract

With the increasing popularity of Intelligent Transportation Systems, how to achieve accurate and real-time traffic prediction has become more and more important. In this paper, we intend to improve the accuracy of traffic prediction by appropriate integration of diffusion process. The spatial-temporal features of traffic flow are captured within an encoder-decoder framework. Specifically, (1) a 1-dimension Convolutional Network (CNN) is exploited to capture the spatial features when fed by the congestion matrix; (2) two long short-term memory methods (LSTMs) are applied to mine the temporal closeness and period properties; (3) external factors such as traffic diffusion, time characteristics are also considered to enhance prediction performance; (4) CNN, LSTMs and external factors are integrated into the final CNN-LSTM based encoder-decoder framework. Experiment results on a public dataset indicate that the consideration of traffic diffusion has advantage in short-term traffic prediction.

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  • (2021)Short-term demand forecasting for online car-hailing using ConvLSTM networksPhysica A: Statistical Mechanics and its Applications10.1016/j.physa.2021.125838570(125838)Online publication date: May-2021

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    cover image ACM Other conferences
    ICCAE 2020: Proceedings of the 2020 12th International Conference on Computer and Automation Engineering
    February 2020
    231 pages
    ISBN:9781450376785
    DOI:10.1145/3384613
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    • The University of Western Australia, Department of Electronic Engineering, University of Western Australia
    • Macquarie U., Austarlia
    • University of Technology Sydney

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    Association for Computing Machinery

    New York, NY, United States

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    Published: 16 May 2020

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    Author Tags

    1. Spatial-temporal framework
    2. Traffic diffusion
    3. Traffic prediction

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    • (2021)Short-term demand forecasting for online car-hailing using ConvLSTM networksPhysica A: Statistical Mechanics and its Applications10.1016/j.physa.2021.125838570(125838)Online publication date: May-2021

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