# Attention-based Recurrent Neural Network for Urban Vehicle Trajectory Prediction

arXiv: https://arxiv.org/abs/1812.07151 · HTML: https://mlsanity.com/paper/1812.07151/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2018.

## Built on by (most cited first, 5 of 5)

- [TrajGAIL: Generating Urban Vehicle Trajectories using Generative Adversarial Imitation Learning](https://mlsanity.com/paper/2007.14189/) (2020)
- [Addressing Optimism Bias in Sequence Modeling for Reinforcement Learning](https://mlsanity.com/paper/2207.10295/) (2022)
- [Spatiotemporal Capsule Neural Network for Vehicle Trajectory Prediction](https://mlsanity.com/paper/2303.02880/) (2023)
- [Transformer-based Map Matching Model with Limited Ground-Truth Data using Transfer-Learning Approach](https://mlsanity.com/paper/2108.00439/) (2021)
- [Deep Learning Methods for Adjusting Global MFD Speed Estimations to Local Link Configurations](https://mlsanity.com/paper/2405.14257/) (2024)

## Abstract (arXiv)

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.