A Recursive Framework-based Approach for Real-time Traffic Flow Forecasting for Highways

Real-time and accurate traffic flow forecasting is a prerequisite for intelligent management and control of highways, which requires an effective approach for data processing as well as for meeting the real-time requirement. However, few studies have considered the accuracy of traffic flow forecasting for highways from a real-time perspective. Based on this consideration, a recursive framework for traffic flow forecasting is developed combining adaptive Kalman filter (KF) and long short-term memory (LSTM) autoencoder to meet the real-time and accuracy requirements of intelligent transportation systems. Historical data of traffic flow and speed are adopted, and smoothed by a KF method to enhance the prediction accuracy. An unsupervised machine learning algorithm, LSTM autoencoder, is introduced to model the time-varying characteristics of highway traffic flow efficiently. Considering the real-time requirement of traffic flow forecasting for highways, a recursive forecasting framework is proposed. The output of the KF algorithm is replaced by the predicted value of LSTM autoencoder. Based on the real-time data, the adaptive KF algorithm is conducted to correct the current optimal state value. A case study is conducted based on a real-world traffic dataset collected from the Minnesota Twin Cities, USA. Study results show that the recursive framework of forecasting the highway traffic flow proposed in this study has relatively competitive advantages in terms of both computational cost and prediction accuracy. The mean absolute percentage error of prediction is 5.0% (< 7.4% of the combined KF and LSTM autoencoder model); and total training time is 85 s, which is lower than the standard LSTM (101 s).