# Lane-GNN: Integrating GNN for Predicting Drivers' Lane Change Intention

arXiv: https://arxiv.org/abs/2207.00824 · HTML: https://mlsanity.com/paper/2207.00824/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 31 references that are in the map.
- First posted on arXiv 2 Jul 2022.

## Builds on (most cited first, 9 of 9)

- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [Graph WaveNet for Deep Spatial-Temporal Graph Modeling](https://mlsanity.com/paper/1906.00121/) (2019)
- [Transfer Learning with Graph Neural Networks for Short-Term Highway Traffic Forecasting](https://mlsanity.com/paper/2004.08038/) (2020)
- [A Comparative Study of Using Spatial-Temporal Graph Convolutional Networks for Predicting Availability in Bike Sharing Schemes](https://mlsanity.com/paper/2104.10644/) (2021)
- [A Distributed and Privacy-Aware Speed Advisory System for Optimising Conventional and Electric Vehicles Networks](https://mlsanity.com/paper/1506.01436/) (2015)
- [On the Design of an Intelligent Speed Advisory System for Cyclists](https://mlsanity.com/paper/1804.08282/) (2018)
- [MPC-CSAS: Multi-Party Computation for Real-time Privacy-preserving Speed Advisory Systems](https://mlsanity.com/paper/2101.06451/) (2021)
- [An Intelligent Multi-Speed Advisory System using Improved Whale Optimisation Algorithm](https://mlsanity.com/paper/2103.00548/) (2021)

## Built on by (most cited first, 4 of 4)

- [Graph Neural Network for spatiotemporal data: methods and applications](https://mlsanity.com/paper/2306.00012/) (2023)
- [Topics in Deep Learning and Optimization Algorithms for IoT Applications in Smart Transportation](https://mlsanity.com/paper/2210.07246/) (2022)
- [Recent Advances in Graph-based Machine Learning for Applications in Smart Urban Transportation Systems](https://mlsanity.com/paper/2306.01282/) (2023)
- [Machine Learning in Micromobility: A Systematic Review of Datasets, Techniques, and Applications](https://mlsanity.com/paper/2508.16135/) (2025)

## Abstract (arXiv)

Nowadays, intelligent highway traffic network is playing an important role in modern transportation infrastructures. A variable speed limit (VSL) system can be facilitated in the highway traffic network to provide useful and dynamic speed limit information for drivers to travel with enhanced safety. Such system is usually designed with a steady advisory speed in mind so that traffic can move smoothly when drivers follow the speed, rather than speeding up whenever there is a gap and slowing down at congestion. However, little attention has been given to the research of vehicles' behaviours when drivers left the road network governed by a VSL system, which may largely involve unexpected acceleration, deceleration and frequent lane changes, resulting in chaos for the subsequent highway road users. In this paper, we focus on the detection of traffic flow anomaly due to drivers' lane change intention on the highway traffic networks after a VSL system. More specifically, we apply graph modelling on the traffic flow data generated by a popular mobility simulator, SUMO, at road segment levels. We then evaluate the performance of lane changing detection using the proposed Lane-GNN scheme, an attention temporal graph convolutional neural network, and compare its performance with a temporal convolutional neural network (TCNN) as our baseline. Our experimental results show that the proposed Lane-GNN can detect drivers' lane change intention within 90 seconds with an accuracy of 99.42% under certain assumptions. Finally, some interpretation methods are applied to the trained models with a view to further illustrate our findings.