IEEE Trans Cybern. 2021 Sep;51(9):4602-4610. doi: 10.1109/TCYB.2020.3000929. Epub 2021 Sep 15.
The accurate prediction of online taxi-hailing demand is challenging but of significant value in the development of the intelligent transportation system. This article focuses on large-scale online taxi-hailing demand prediction and proposes a personalized demand prediction model. A model with two attention blocks is proposed to capture both spatial and temporal perspectives. We also explored the impact of network architecture on taxi-hailing demand prediction accuracy. The proposed method is universal in the sense that it is applicable to problems associated with large-scale spatiotemporal prediction. The experimental results on city-wide online taxi-hailing demand dataset demonstrate that the proposed personalized demand prediction model achieves superior prediction accuracy.
准确预测在线打车需求具有挑战性，但对智能交通系统的发展具有重要价值。本文专注于大规模在线打车需求预测，并提出了一种个性化需求预测模型。我们提出了一种具有两个注意力块的模型，以捕捉空间和时间视角。我们还探讨了网络架构对打车需求预测准确性的影响。所提出的方法在普遍性方面具有优势，因为它适用于与大规模时空预测相关的问题。在全市在线打车需求数据集上的实验结果表明，所提出的个性化需求预测模型具有优越的预测精度。
