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Urban road traffic condition forecasting based on sparse ride-hailing service data

Urban road traffic condition forecasting based on sparse ride-hailing service data

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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.

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