A deep learning hierarchical approach to road traffic forecasting

Traffic forecasting is a crucial task of an Intelligent Transportation System (ITS), which remains very challenging as it is affected by the complexity and depth of the road network. Although the decision‐makers focus on the accuracy of the top‐level roads, the forecasts on the lower levels also improve the overall performance of ITS. In such a situation, a hierarchical forecasting strategy is more appropriate as well as a more accurate prediction methods to reach an efficient forecast. In this paper, we present a deep learning (DL) approach for hierarchical forecasting of traffic flow by exploring the hierarchical structure of the road network. The proposed approach is considered an improved variation on the top‐down strategy for the reconciliation process. We propose a model based on two deep neural network components to generate a coherent forecast for the total number of road segments. We use N‐BEATS, a pure deep learning forecasting method, at the highest levels for traffic time series, then disaggregate these downwards to get coherent forecasts for each series of the hierarchy using a combination of CNN and LSTM. Experiments were carried out using Beijing road traffic dataset to demonstrate the effectiveness of the approach.