Road Traffic Forecast Based on Meteorological Information through Deep Learning Methods

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.

Automated summary

Forecasting road flow is important for ensuring safety and efficiency, especially in summer resorts where traffic is directly influenced by weather conditions. This study evaluates machine learning methods to predict traffic flows using radar and meteorological sensor information. The results show that weather conditions are essential for accurate predictions, and the CNN model performs the best in terms of prediction accuracy and efficiency.