Highlights • Deep neural network models that jointly predict the next location and arrival time of a vehicle. • Learns sequential patterns in trajectory data using LSTM networks and self-attention mechanism. • Compares the effect of different model structures - hybrid LSTM and sequential LSTM. • Shows the importance of considering travel time in the next location prediction task.

Abstract 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.