Road Traffic Forecast Based on Meteorological Information through Deep Learning Methods

Sensors (Basel). 2022 Jun 14;22(12):4485. doi: 10.3390/s22124485.


Forecasting road flow has strong importance for both allowing authorities to guarantee safety conditions and traffic efficiency, as well as for road users to be able to plan their trips according to space and road occupation. In a summer resort, such as beaches near cities, traffic depends directly on weather conditions, variables that should be of great impact on the quality of forecasts. Will the use of a dataset with information on transit flows enhanced with meteorological information allow the construction of a precise traffic flow forecasting model, allowing predictions to be made in advance of the traffic flow in suitable time? The present work evaluates different machine learning methods, namely long short-term memory, autoregressive LSTM, and a convolutional neural network, and data attributes to predict traffic flows based on radar and meteorological sensor information. The models trained to predict the traffic flow have shown that weather conditions were essential for this forecast, and thus, these variables were employed in the evaluated deep-learning models. The results pointed out that it is possible to forecast the traffic flow at a reasonable error level for one-hour periods, and the CNN model presented the lowest prediction error values and consumed the least time to build its predictions.

Keywords: deep learning; highway traffic; method comparison; weather-based traffic prediction.

MeSH terms

  • Deep Learning*
  • Forecasting
  • Meteorology
  • Neural Networks, Computer
  • Weather

Grants and funding

This work is supported by the European Regional Development Fund (FEDER), through the Competitiveness and Internationalization Operational Programme (COMPETE 2020) of the Portugal 2020 framework (Project STEROID with No. 069989 (POCI-01-0247-FEDER-069989)), by European Structural Investment Funds (ESIF), through the Regional Operational Programme of Centre (CENTRO 2020) (Project No. CENTRO-01-0246-FEDER-000008), and by the Fundação de Amparo à Pesquisa e Inovação do Estado de Santa Catarina (Project No. FAPESC 1378/2021).