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.

中文翻译：

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基于稀疏乘车服务数据的城市道路交通状况预测

城市交通系统的交通流量受到许多内部/外部因素的随机影响，这给准确预测道路状况带来了巨大挑战。这项研究结合了CANDECOMP / PARAFAC加权优化和扩散卷积门控递归单元（DCGRU）模型，基于稀疏的乘车服务数据进行交通状况预测。通过对正则项添加因子张量来修正基于张量分解的数据完成方法，该方法包含工作日，时段，路段的特征。随后，在数据集中采用多类预测的DCGRU模型来预测交通状况。根据南京火车南站附近一个月的乘车服务数据进行了数值实验。