# NetTraj: A Network-based Vehicle Trajectory Prediction Model with Directional Representation and Spatiotemporal Attention Mechanisms

arXiv: https://arxiv.org/abs/2106.11175 · HTML: https://mlsanity.com/paper/2106.11175/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 45 references that are in the map.
- First posted on arXiv 21 Jun 2021.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks](https://mlsanity.com/paper/1506.03099/) (2015)
- [Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding](https://mlsanity.com/paper/1809.07426/) (2018)
- [Deep Learning for Spatio-Temporal Data Mining: A Survey](https://mlsanity.com/paper/1906.04928/) (2019)
- [OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks](https://mlsanity.com/paper/1611.01890/) (2016)
- [STAN: Spatio-Temporal Attention Network for Next Location Recommendation](https://mlsanity.com/paper/2102.04095/) (2021)
- [Sequence-to-Sequence Prediction of Vehicle Trajectory via LSTM Encoder-Decoder Architecture](https://mlsanity.com/paper/1802.06338/) (2018)
- [Deep Learning Methods for Vessel Trajectory Prediction based on Recurrent Neural Networks](https://mlsanity.com/paper/2101.02486/) (2021)
- [Deep Learning-based Vehicle Behaviour Prediction For Autonomous Driving Applications: A Review](https://mlsanity.com/paper/1912.11676/) (2019)
- [Predicting Vehicle Behaviors Over An Extended Horizon Using Behavior Interaction Network](https://mlsanity.com/paper/1903.00848/) (2019)
- [T-CONV: A Convolutional Neural Network For Multi-scale Taxi Trajectory Prediction](https://mlsanity.com/paper/1611.07635/) (2016)
- [A Scalable Framework for Trajectory Prediction](https://mlsanity.com/paper/1806.03582/) (2018)
- [Destination Prediction Based on Partial Trajectory Data](https://mlsanity.com/paper/2004.07473/) (2020)
- [Individual Mobility Prediction: An Interpretable Activity-based Hidden Markov Approach](https://mlsanity.com/paper/2101.03996/) (2021)

## Built on by (most cited first, 13 of 13)

- [Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions](https://mlsanity.com/paper/2503.03262/) (2025)
- [NEST: A Neuromodulated Small-world Hypergraph Trajectory Prediction Model for Autonomous Driving](https://mlsanity.com/paper/2412.11682/) (2024)
- [A deep inverse reinforcement learning approach to route choice modeling with context-dependent rewards](https://mlsanity.com/paper/2206.10598/) (2022)
- [VT-Former: An Exploratory Study on Vehicle Trajectory Prediction for Highway Surveillance through Graph Isomorphism and Transformer](https://mlsanity.com/paper/2311.06623/) (2023)
- [Dynamic Spatiotemporal Graph Convolutional Neural Networks for Traffic Data Imputation with Complex Missing Patterns](https://mlsanity.com/paper/2109.08357/) (2021)
- [Explainable Trajectory Representation through Dictionary Learning](https://mlsanity.com/paper/2312.08052/) (2023)
- [UVTM: Universal Vehicle Trajectory Modeling with ST Feature Domain Generation](https://mlsanity.com/paper/2402.07232/) (2024)
- [INTENT: Trajectory Prediction Framework with Intention-Guided Contrastive Clustering](https://mlsanity.com/paper/2503.04952/) (2025)
- [Traj-LLM: A New Exploration for Empowering Trajectory Prediction with Pre-trained Large Language Models](https://mlsanity.com/paper/2405.04909/) (2024)
- [Spatial Temporal Attention based Target Vehicle Trajectory Prediction for Internet of Vehicles](https://mlsanity.com/paper/2501.00890/) (2025)
- [Analyzing sequential activity and travel decisions with interpretable deep inverse reinforcement learning](https://mlsanity.com/paper/2503.12761/) (2025)
- [Semi-decentralized Federated Time Series Prediction with Client Availability Budgets](https://mlsanity.com/paper/2509.03660/) (2025)
- [SWIFT: A Small-World Interaction Framework for Flow-Aware Trajectory Prediction in Autonomous Driving](https://mlsanity.com/paper/2607.09741/) (2026)

## Abstract (arXiv)

Trajectory prediction of vehicles in city-scale road networks is of great importance to various location-based applications such as vehicle navigation, traffic management, and location-based recommendations. Existing methods typically represent a trajectory as a sequence of grid cells, road segments or intention sets. None of them is ideal, as the cell-based representation ignores the road network structures and the other two are less efficient in analyzing city-scale road networks. Moreover, previous models barely leverage spatial dependencies or only consider them at the grid cell level, ignoring the non-Euclidean spatial structure shaped by irregular road networks. To address these problems, we propose a network-based vehicle trajectory prediction model named NetTraj, which represents each trajectory as a sequence of intersections and associated movement directions, and then feeds them into a LSTM encoder-decoder network for future trajectory generation. Furthermore, we introduce a local graph attention mechanism to capture network-level spatial dependencies of trajectories, and a temporal attention mechanism with a sliding context window to capture both short- and long-term temporal dependencies in trajectory data. Extensive experiments based on two real-world large-scale taxi trajectory datasets show that NetTraj outperforms the existing state-of-the-art methods for vehicle trajectory prediction, validating the effectiveness of the proposed trajectory representation method and spatiotemporal attention mechanisms.