@conference{510,
  keywords = {Traffic Prediction, Congestion, Graph Neural Network},
  author = {Marko Oosthuizen and Alwyn Hoffman and Marelie Davel},
  title = {A Comparative Study of Graph Neural Network Speed Prediction during Periods of Congestion},
  abstract = {Traffic speed prediction using deep learning has been the topic of many studies. In this paper, we analyse the performance of Graph Neural Network-based techniques during periods of traffic congestion. We first compare a selection of recently proposed techniques that claim to achieve good results using the METR-LA and PeMS-BAY data sets. We then investigate the performance of three of these approaches – Graph WaveNet, Spacetime Neural Network (STNN) and Spatio-Temporal Attention Wavenet (STAWnet) – during congested periods, using recurrent congestion patterns to set a threshold for general congestion through the entire traffic network. Our results show that performance deteriorates significantly during congested time periods, which is concerning, as traffic speed prediction is usually of most value during times of congestion. We also found that, while the above approaches perform almost equally in the absence of congestion, there are much bigger differences in performance during peri ods of congestion.},
  year = {2022},
  journal = {In Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - NCTA},
  volume = {1},
  chapter = {331 - 338},
  month = {October 2022},
  issn = {2184-3236},
  isbn = {978-989-758-611-8},
  doi = {10.5220/0011374100003332},
}