Short‐term traffic flow prediction approach incorporating vehicle functions from RFID‐ELP data for urban road sections

Abstract

Abstract Short‐term traffic flow prediction is particularly important for urban traffic control and congestion management. To improve the accuracy of traffic flow prediction, a new traffic flow prediction approach is proposed from the perspective of vehicle functions by using Radio Frequency Identification (RFID) electronic license plate (ELP) data. First, the improved Wavelet Neural Network (WNN) is introduced by adding three momentum terms to avoid local minimum and overfitting. The Long Short‐Term Memory Neural Network (LSTM) is constructed to train the time series with long time lags. Second, a novel short‐term traffic flow prediction method is developed by combining the improved WNN and LSTM in an urban road section. The proposed method performs a weighted combination to obtain a new prediction value according to whether the difference between the prediction values of the two methods satisfy the defined threshold, and then selects the one with the smallest error among the three prediction values as the final prediction result. Third, the experiments explore the prediction performance of the weekdays and weekends with 5 min‐interval, 15 min‐interval and 30 min‐interval via using RFID‐ELP data from Chongqing, China. Compared with existing prediction methods, the proposed approach can promote prediction performance and meet real‐time requirements.