Hourly forecasting of traffic flow rates using spatial temporal graph neural networks
Procedia Computer Science , Volume 220 p. 102- 109
Traffic congestion forms a large problem in many major metropolitan regions around the world, leading to delays and societal costs. As people resume travel upon relaxation of COVID-19 restrictions and personal mobility returns to levels prior to the pandemic, policy makers need tools to understand new patterns in the daily transportation system. In this paper we use a Spatial Temporal Graph Neural Network (STGNN) to train data collected by 34 traffic sensors around Amsterdam, in order to forecast traffic flow rates on an hourly aggregation level for a quarter. Our results show that STGNN did not outperform a baseline seasonal naive model overall, however for sensors that are located closer to each other in the road network, the STGNN model did indeed perform better.
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|Procedia Computer Science|
|14th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2023|
|Organisation||Centrum Wiskunde & Informatica, Amsterdam (CWI), The Netherlands|
Belt, E.A, Koch, T. H. A, & Dugundji, E.R. (2023). Hourly forecasting of traffic flow rates using spatial temporal graph neural networks. In Procedia Computer Science, Special Issue 14th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 (Vol. 220, pp. 102–109). doi:10.1016/j.procs.2023.03.016