Traffic flows of the urban transport system are randomly influenced by many internal/external factors, which bring in a huge challenge to accurately forecasting road conditions. This study combines the CANDECOMP/PARAFAC weighted optimisation and diffusion convolution gated recurrent unit (DCGRU) models to conduct the traffic condition forecasting based on the sparse ride‐hailing service data. A data completion method based on the tensor decomposition is modified by adding factor tensor in the regular terms, which contains the characteristics of weekday, time period, road segment. Subsequently, the DCGRU model of multiclass predicting is adopted in the data set to predict the traffic conditions. A numerical experiment is conducted based on the one‐month ride‐hailing service data, collected around the Nanjing South railway station. The predicting results indicate that the method in this study outperforms other traditional models in different tested traffic conditions.
Urban road traffic condition forecasting based on sparse ride‐hailing service data
IET Intelligent Transport Systems ; 14 , 7 ; 668-674
2020-07-01
7 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
data analysis , road segment , urban transport system , diffusion convolution , factor tensor , sparse ride‐hailing service data , road conditions , forecasting theory , road traffic , railways , recurrent unit models , traffic engineering computing , data completion method , DCGRU model , convolutional neural nets , optimisation , transportation , tested traffic conditions , tensor decomposition , regression analysis , recurrent neural nets , Nanjing South railway station , urban road traffic condition forecasting , tensors , rail traffic , one‐month ride‐hailing service data
Urban road traffic condition forecasting based on sparse ride-hailing service data
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