Prediction of urban taxi travel demand by using hybrid dynamic graph convolutional network model

HIGHLIGHTS

• who: Jinbao Zhao et al. from the School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo, China have published the research: Prediction of Urban Taxi Travel Demand by Using Hybrid Dynamic Graph Convolutional Network Model, in the Journal: Sensors 2022, 5982 of 30/06/2021
• what: The authors propose a traffic demand forecasting framework of a hybrid dynamic graph convolutional network (HDGCN) model to deeply capture the characteristics of urban travel demand and improve accuracy. This approach has a natural disadvantage as it destroys the complex structure of urban road networks : a residential . . .
  

   

  

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