Daily long-term traffic flow forecasting based on a deep neural network

Abstract

Daily traffic flow forecasting is critical in advanced traffic management and can improve the efficiency of fixed-time signal control. This paper presents a traffic prediction method for one whole day using a deep neural network based on historical traffic flow data and contextual factor data. The main idea is that traffic flow within a short time period is strongly correlated with the starting and ending time points of the period together with a number of other contextual factors, such as day of week, weather, and season. Therefore, the relationship between the traffic flow values within a given time interval and a combination of contextual factors can be mined from historical data. First, a predictor was trained using a multi-layer supervised learning algorithm to mine the potential relationship between traffic flow data and a combination of key contextual factors. To reduce training times, a batch training method was proposed. Finally, a Seattle-based case study shows that, overall, the proposed method outperforms the conventional traffic prediction method in terms of prediction accuracy.