Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China.
Research Institute of Tsinghua, Pearl River Delta, Guangzhou 510530, China.
Sensors (Basel). 2023 May 19;23(10):4893. doi: 10.3390/s23104893.
Taxis are an important component of the transportation system, and empty taxis represent a significant waste of transportation resources. To alleviate the imbalance between supply and demand and relieve traffic congestion, real-time prediction of taxi trajectories is necessary. Most existing trajectory prediction studies focus on extracting time-series information but do not capture spatial information sufficiently. In this paper, we focus on the construction of an urban network and propose an urban topology-encoding spatiotemporal attention network (UTA) to address destination prediction problems. Firstly, this model discretizes the production and attraction units of transportation, combining them with key nodes in the road network to form an urban topological network. Secondly, GPS records are matched to the urban topological map to construct a topological trajectory, which significantly improves trajectory consistency and endpoint certainty, helping to model destination prediction problems. Thirdly, semantic information concerning surrounding space is attached to effectively mine the spatial dependencies of trajectories. Finally, after the topological encoding of city space and trajectories, this algorithm proposes a topological graph neural network to model the attention calculation with the trajectory context, comprehensively considering the spatiotemporal characteristics of the trajectories and improving prediction accuracy. We solve the prediction problems with the UTA model and also compare it with some other classical models, such as the HMM, RNN, LSTM, and transformer. The results suggest that all the models work well in combination with the proposed urban model (with a rough increase of 2%), while the UTA model is less affected by data sparsity.
出租车是交通系统的重要组成部分，空驶的出租车代表了交通运输资源的巨大浪费。为了缓解供需失衡和交通拥堵问题，需要对出租车轨迹进行实时预测。大多数现有的轨迹预测研究都侧重于提取时间序列信息，但对空间信息的捕捉不够充分。本文聚焦于城市网络的构建，提出了一种城市拓扑编码时空注意力网络（UTA）来解决目的地预测问题。首先，该模型对交通的生产和吸引单元进行离散化，将其与路网中的关键节点相结合，形成城市拓扑网络。其次，将 GPS 记录与城市拓扑图相匹配，构建拓扑轨迹，这显著提高了轨迹的一致性和终点确定性，有助于对目的地预测问题进行建模。第三，附加了关于周围空间的语义信息，以有效地挖掘轨迹的空间依赖性。最后，在对城市空间和轨迹进行拓扑编码后，该算法提出了一种拓扑图神经网络来对轨迹上下文进行注意力计算，综合考虑了轨迹的时空特征，提高了预测精度。我们使用 UTA 模型解决了预测问题，并与一些其他经典模型（如 HMM、RNN、LSTM 和 Transformer）进行了比较。结果表明，所有模型与所提出的城市模型结合使用效果都很好（大致提高了 2%），而 UTA 模型受数据稀疏性的影响较小。
