English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

With the increasing deployment of diverse positioning devices andlocation-based services, a huge amount of spatial and temporal information hasbeen collected and accumulated as trajectory data. Among many applications,trajectory-based location prediction is gaining increasing attention because ofits potential to improve the performance of many applications in multipledomains. This research focuses on trajectory sequence prediction methods usingtrajectory data obtained from the vehicles in urban traffic network. AsRecurrent Neural Network(RNN) model is previously proposed, we propose animproved method of Attention-based Recurrent Neural Network model(ARNN) forurban vehicle trajectory prediction. We introduce attention mechanism intourban vehicle trajectory prediction to explain the impact of network-leveltraffic state information. The model is evaluated using the Bluetooth data ofprivate vehicles collected in Brisbane, Australia with 5 metrics which arewidely used in the sequence modeling. The proposed ARNN model shows significantperformance improvement compared to the existing RNN models considering notonly the cells to be visited but also the alignment of the cells in sequence.

Attention-based Recurrent Neural Network for Urban Vehicle Trajectory Prediction