Chen Zhe, Zhao Bin, Wang Yuehan, Duan Zongtao, Zhao Xin
School of Information Engineering, Chang'an University, Xi'an 710064, China.
Sensors (Basel). 2020 Jul 5;20(13):3776. doi: 10.3390/s20133776.
The accurate forecasting of urban taxi demands, which is a hot topic in intelligent transportation research, is challenging due to the complicated spatial-temporal dependencies, the dynamic nature, and the uncertainty of traffic. To make full use of the global and local correlations between traffic flows on road sections, this paper presents a deep learning model based on a graph convolutional network, long short-term memory (LSTM), and multitask learning. First, an undirected graph model was formed by considering the spatial pattern distribution of taxi trips on road networks. Then, LSTMs were used to extract the temporal features of traffic flows. Finally, the model was trained using a multitask learning strategy to improve the model's generalizability. In the experiments, the efficiency and accuracy were verified with real-world taxi trajectory data. The experimental results showed that the model could effectively forecast the short-term taxi demands on the traffic network level and outperform state-of-the-art traffic prediction methods.
城市出租车需求的准确预测是智能交通研究中的一个热门话题，由于复杂的时空依赖性、动态特性和交通的不确定性，这一预测具有挑战性。为了充分利用路段交通流之间的全局和局部相关性，本文提出了一种基于图卷积网络、长短期记忆（LSTM）和多任务学习的深度学习模型。首先，通过考虑出租车出行在道路网络上的空间模式分布形成了一个无向图模型。然后，使用LSTM来提取交通流的时间特征。最后，采用多任务学习策略对模型进行训练，以提高模型的泛化能力。在实验中，利用真实世界的出租车轨迹数据验证了该模型的效率和准确性。实验结果表明，该模型能够有效地预测交通网络层面的短期出租车需求，并且优于现有最先进的交通预测方法。
