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ISSN 1004-9037
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Edited by: Editorial Board of Journal of Data Acquisition and Processing
P.O. Box 2704, Beijing 100190, P.R. China
Sponsored by: Institute of Computing Technology, CAS & China Computer Federation
Undertaken by: Institute of Computing Technology, CAS
Published by: SCIENCE PRESS, BEIJING, CHINA
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      07 April 2023, Volume 38 Issue 2   
    Article

    SURVEY ON SPATIO-TEMPORAL TRANSPORTATION USING DEEP CONVOLUTION NETWORK FOR TRAFFIC FLOW
    Tukaram K. Gawali1, Shailesh S. Deore2
    Journal of Data Acquisition and Processing, 2023, 38 (2): 10-20 . 

    Abstract

    Spatio-temporal transportations have various issues like traffic congestion, weather and wind direction. The measure problem is to prevent from traffic based accidents. The traffic may be in homogenous and heterogeneous format. In this paper the complete focus is based on heterogeneous traffic flow. In first stage we studied various research and we studied deep convolution network for identifying and measures the traffic accidents and developed a unique spatiotemporal graph-based model for predicting the probability of future traffic accidents. We used hybrid approach to improve the reliability and sustainability of large-scale networks through improving both recurrent and non-recurrent traffic conditions.

    Keyword

    Fully Connected traffic, K-hop neighbours, Dynamic Spatial Attention, Autonomous Vehicle, Deep Convolution Network


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ISSN 1004-9037

         

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