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A Tutorial on Network-Wide Multi-Horizon Traffic Forecasting with Deep Learning

EasyChair Preprint no. 4944

4 pagesDate: January 30, 2021


Traffic flow forecasting is fundamental to today's Intelligent Transportation Systems (ITS). It involves the task of learning traffic complex dynamics in order to predict future conditions. This is particularly challenging when it comes to predict the traffic status for multiple horizons into the future and at the same time for the entire transportation network. In this context deep learning models have recently shown promising results. This models can inherently capture the non-linear space-temporal correlations (ST) in traffic by taking advantage of the huge volume of data available.\\ In this study the authors present a LSTM encoder-decoder for multi-horizon traffic flow predictions. We adopted a direct approach in which the model simultaneously predict traffic conditions for the entire Belgian motorway transport network at each time step. The results clearly show the superiority of this model when compared with other deep learning models. In the workshop, conference attendees will learn how to process and visualise mobility data, obtain optimal features for traffic flow forecasting, build a LSTM encoder-decoder and perform predictions in an online manner.

Keyphrases: deep learning, Multi-horizon forecasting, traffic flow forecasting, transportation networks

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Giovanni Buroni and Gianluca Bontempi and Karl Determe},
  title = {A Tutorial on Network-Wide Multi-Horizon Traffic Forecasting with Deep Learning},
  howpublished = {EasyChair Preprint no. 4944},

  year = {EasyChair, 2021}}
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