This paper aims to incorporate travel time prediction in the next location prediction problem to enable the prediction of the city-wide movement trajectory of an individual vehicle by considering both where the vehicle will go next and when it will arrive. We propose two deep learning models based on long short-term memory (LSTM) neural networks with self-attention mechanism—namely, hybrid LSTM and sequential LSTM. These models capture patterns in location and time sequences in trajectory data and their dependencies to predict next locations and travel times simultaneously. Using Bluetooth vehicle trajectory data from Brisbane, Australia, we compare the prediction performance of the proposed models with several existing approaches including hidden Markov model and other LSTM-based models. The results show that the proposed models produce higher prediction accuracy for both location and time prediction tasks, with the sequential LSTM yielding the best performance. Compared to the conventional next location prediction problem, which considers location sequences only without travel time consideration, the study finds that jointly modelling location and travel time sequences actually improves the next location prediction performance itself, potentially because travel time observations capture the information on traffic conditions in the network, which may affect drivers’ location choices. We demonstrate an application of the proposed models in network traffic management, where important locations can be identified to mitigate congestion in a hot-spot by predicting where vehicles come from and go next in an urban road network.

本文旨在将行驶时间预测合并到下一个位置预测问题中，以通过考虑车辆的下一个行驶位置和到达的时间来预测单个车辆在全市范围内的运动轨迹。我们提出了两种基于具有自我注意机制的长短期记忆（LSTM）神经网络的深度学习模型，即混合LSTM和顺序LSTM。这些模型捕获轨迹数据中位置和时间序列中的模式及其相关性，以同时预测下一个位置和行驶时间。使用来自澳大利亚布里斯班的蓝牙车辆轨迹数据，我们将提出的模型的预测性能与几种现有方法（包括隐马尔可夫模型和其他基于LSTM的模型）进行了比较。结果表明，所提出的模型对位置和时间预测任务均具有较高的预测精度，而顺序LSTM产生了最佳性能。与仅考虑位置序列而不考虑出行时间的常规下一个位置预测问题相比，研究发现联合建模位置和出行时间序列实际上可以提高下一个位置预测性能本身，这可能是因为出行时间观测可以捕获交通状况信息在网络中，这可能会影响驾驶员的位置选择。我们演示了所提出的模型在网络流量管理中的应用，其中可以通过预测车辆在城市道路网络中的来往和往来地点，来确定重要位置，以减轻热点的拥堵。