# Mining Topological Dependencies of Recurrent Congestion in Road Networks

arXiv: https://arxiv.org/abs/2107.09554 · HTML: https://mlsanity.com/paper/2107.09554/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 37 references that are in the map.
- First posted on arXiv 20 Jul 2021.

## Builds on (most cited first, 4 of 4)

- [Spatio-Temporal Data Mining: A Survey of Problems and Methods](https://mlsanity.com/paper/1711.04710/) (2017)
- [Discovery of Important Crossroads in Road Network using Massive Taxi Trajectories](https://mlsanity.com/paper/1407.2506/) (2014)
- [Scalable Deep Traffic Flow Neural Networks for Urban Traffic Congestion Prediction](https://mlsanity.com/paper/1703.01006/) (2017)
- [TA-Dash: An Interactive Dashboard for Spatial-Temporal Traffic Analytics -- Demo Paper](https://mlsanity.com/paper/2008.00002/) (2020)

## Built on by (most cited first, 1 of 1)

- [MGL2Rank: Learning to Rank the Importance of Nodes in Road Networks Based on Multi-Graph Fusion](https://mlsanity.com/paper/2305.14375/) (2023)

## Abstract (arXiv)

The discovery of spatio-temporal dependencies within urban road networks that cause Recurrent Congestion (RC) patterns is crucial for numerous real-world applications, including urban planning and scheduling of public transportation services. While most existing studies investigate temporal patterns of RC phenomena, the influence of the road network topology on RC is often overlooked. This article proposes the ST-Discovery algorithm, a novel unsupervised spatio-temporal data mining algorithm that facilitates the effective data-driven discovery of RC dependencies induced by the road network topology using real-world traffic data. We factor out regularly reoccurring traffic phenomena, such as rush hours, mainly induced by the daytime, by modelling and systematically exploiting temporal traffic load outliers. We present an algorithm that first constructs connected subgraphs of the road network based on the traffic speed outliers. Second, the algorithm identifies pairs of subgraphs that indicate spatio-temporal correlations in their traffic load behaviour to identify topological dependencies within the road network. Finally, we rank the identified subgraph pairs based on the dependency score determined by our algorithm. Our experimental results demonstrate that ST-Discovery can effectively reveal topological dependencies in urban road networks.