# Recent Advances in Graph-based Machine Learning for Applications in Smart Urban Transportation Systems

arXiv: https://arxiv.org/abs/2306.01282 · HTML: https://mlsanity.com/paper/2306.01282/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 97 references that are in the map.
- First posted on arXiv 2 Jun 2023.

## Builds on (most cited first, 25 of 25)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [Spectral Networks and Locally Connected Networks on Graphs](https://mlsanity.com/paper/1312.6203/) (2013)
- [Graph WaveNet for Deep Spatial-Temporal Graph Modeling](https://mlsanity.com/paper/1906.00121/) (2019)
- [GMAN: A Graph Multi-Attention Network for Traffic Prediction](https://mlsanity.com/paper/1911.08415/) (2019)
- [Relation Networks for Object Detection](https://mlsanity.com/paper/1711.11575/) (2017)
- [Graph Neural Network for Traffic Forecasting: A Survey](https://mlsanity.com/paper/2101.11174/) (2021)
- [Graph networks as learnable physics engines for inference and control](https://mlsanity.com/paper/1806.01242/) (2018)
- [Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey](https://mlsanity.com/paper/2005.00935/) (2020)
- [Graph-to-Sequence Learning using Gated Graph Neural Networks](https://mlsanity.com/paper/1806.09835/) (2018)
- [STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting](https://mlsanity.com/paper/1905.10069/) (2019)
- [Coupled Layer-wise Graph Convolution for Transportation Demand Prediction](https://mlsanity.com/paper/2012.08080/) (2020)
- [Bike Flow Prediction with Multi-Graph Convolutional Networks](https://mlsanity.com/paper/1807.10934/) (2018)
- [Predicting Station-level Hourly Demands in a Large-scale Bike-sharing Network: A Graph Convolutional Neural Network Approach](https://mlsanity.com/paper/1712.04997/) (2017)
- [Scale-aware Fast R-CNN for Pedestrian Detection](https://mlsanity.com/paper/1510.08160/) (2015)
- [Spatio-Temporal Scene-Graph Embedding for Autonomous Vehicle Collision Prediction](https://mlsanity.com/paper/2111.06123/) (2021)
- [Uncertainty Quantification of Sparse Travel Demand Prediction with Spatial-Temporal Graph Neural Networks](https://mlsanity.com/paper/2208.05908/) (2022)
- [Inferring transportation modes from GPS trajectories using a convolutional neural network](https://mlsanity.com/paper/1804.02386/) (2018)
- [Social-WaGDAT: Interaction-aware Trajectory Prediction via Wasserstein Graph Double-Attention Network](https://mlsanity.com/paper/2002.06241/) (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)
- [Lane-GNN: Integrating GNN for Predicting Drivers' Lane Change Intention](https://mlsanity.com/paper/2207.00824/) (2022)
- [Bayesian Classifier for Route Prediction with Markov Chains](https://mlsanity.com/paper/1808.10705/) (2018)
- [A Distributed and Privacy-Aware Speed Advisory System for Optimising Conventional and Electric Vehicles Networks](https://mlsanity.com/paper/1506.01436/) (2015)
- [An Intelligent Multi-Speed Advisory System using Improved Whale Optimisation Algorithm](https://mlsanity.com/paper/2103.00548/) (2021)

## Abstract (arXiv)

The Intelligent Transportation System (ITS) is an important part of modern transportation infrastructure, employing a combination of communication technology, information processing and control systems to manage transportation networks. This integration of various components such as roads, vehicles, and communication systems, is expected to improve efficiency and safety by providing better information, services, and coordination of transportation modes. In recent years, graph-based machine learning has become an increasingly important research focus in the field of ITS aiming at the development of complex, data-driven solutions to address various ITS-related challenges. This chapter presents background information on the key technical challenges for ITS design, along with a review of research methods ranging from classic statistical approaches to modern machine learning and deep learning-based approaches. Specifically, we provide an in-depth review of graph-based machine learning methods, including basic concepts of graphs, graph data representation, graph neural network architectures and their relation to ITS applications. Additionally, two case studies of graph-based ITS applications proposed in our recent work are presented in detail to demonstrate the potential of graph-based machine learning in the ITS domain.