  With the increasing deployment of diverse positioning devices and
location-based services, a huge amount of spatial and temporal information has
been collected and accumulated as trajectory data. Among many applications,
trajectory-based location prediction is gaining increasing attention because of
its potential to improve the performance of many applications in multiple
domains. This research focuses on trajectory sequence prediction methods using
trajectory data obtained from the vehicles in urban traffic network. As
Recurrent Neural Network(RNN) model is previously proposed, we propose an
improved method of Attention-based Recurrent Neural Network model(ARNN) for
urban vehicle trajectory prediction. We introduce attention mechanism into
urban vehicle trajectory prediction to explain the impact of network-level
traffic state information. The model is evaluated using the Bluetooth data of
private vehicles collected in Brisbane, Australia with 5 metrics which are
widely used in the sequence modeling. The proposed ARNN model shows significant
performance improvement compared to the existing RNN models considering not
only the cells to be visited but also the alignment of the cells in sequence.
