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Student Project | Computer Science & Engineering | India | Volume 12 Issue 7, July 2023

Traffic Forecasting with Graph Convolutional Network and Gated Recurrent Unit using Internal and External Factors in Different Domains

Amrutha S Aravind

Abstract: For intelligent transportation systems (ITS), accurate real-time traffic forecasting is essential, and it also forms the basis of many other smart applications. Deep Learning techniques have shown to be adaptable for modelling complicated issues. Urban traffic planning, traffic management, and traffic control greatly benefit from accurate and real-time traffic forecasts, which is essential to the ITS. In recent years, research on traffic forecasting has focused heavily on spatio-temporal models that integrate dynamic feature modelling neural networks and spatial feature modelling networks. The majority of models in use today are network- or city-specific. As a result, information about various cities may be transferred using traffic forecasting models across several cities.This can increase the forecasting's precision. As a result, a traffic forecasting model using geographical and temporal traffic data from several source domains.

Keywords: Traffic Forecasting, Graph Convolutional Network, GatedRecurrent Unit, Gradient Reversal Layer

Edition: Volume 12 Issue 7, July 2023,

Pages: 1495 - 1500