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Establishing a real-time traffic alarm in the city of Valencia with Deep Learning

arXiv 2309.02010 ↗First posted 5 Sep 2023cs.LGcited by 0

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Abstract (arXiv)

Urban traffic emissions represent a significant concern due to their detrimental impacts on both public health and the environment. Consequently, decision-makers have flagged their reduction as a crucial goal. In this study, we first analyze the correlation between traffic flux and pollution in the city of Valencia, Spain. Our results demonstrate that traffic has a significant impact on the levels of certain pollutants (especially NOx). Secondly, we develop an alarm system to predict if a street is likely to experience unusually high traffic in the next 30 minutes, using an independent three-tier level for each street. To make the predictions, we use traffic data updated every 10 minutes and Long Short-Term Memory (LSTM) neural networks. We trained the LSTM using traffic data from 2018, and tested it using traffic data from 2019.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 41 references that are in the map. First posted on arXiv 5 Sep 2023.

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13 of 41 references.

References · 13

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
  3. 2013Spectral Networks and Locally Connected Networks on Graphscited by 1,102
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  5. 2018Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecastingcited by 146
  6. 2018Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Predictioncited by 61
  7. 2020Stacked Bidirectional and Unidirectional LSTM Recurrent Neural Network for Forecasting Network-wide Traffic State with Missing Valuescited by 32
  8. 2018Forecasting Transportation Network Speed Using Deep Capsule Networks with Nested LSTM Modelscited by 11
  9. 2019DDP-GCN: Multi-Graph Convolutional Network for Spatiotemporal Traffic Forecastingcited by 10
  10. 2018A Deep Generative Adversarial Architecture for Network-Wide Spatial-Temporal Traffic State Estimationcited by 7
  11. 2022A spatial-temporal short-term traffic flow prediction model based on dynamical-learning graph convolution mechanismcited by 5
  12. 2016Generalizing the Convolution Operator to extend CNNs to Irregular Domainscited by 2
1 more
  1. 2022Predicting the traffic flux in the city of Valencia with Deep Learningcited by 1

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