Expressway Exit Traffic Flow Prediction for ETC and MTC Charging System Based on Entry Traffic Flows and LSTM Model

Abstract

The Expressway (controlled-access highways) of China is the longest in the world and plays an important role in people’s daily life. Accurate short-term traffic prediction is essential for travel schedule and active traffic management. There are two coexisting charging systems for expressway in China, Electronic Toll Collection (ETC) and Manual Toll Collection (MTC), which have different passing capacity and variation pattern. In this work, we demonstrate that the exit traffic flow prediction at Shanghai Xinqiao toll station using entry traffic flows from multiple close-related stations with Long Short-Term Memory (LSTM) model. Based on the origin-destination (OD) traffic data of a month, we present a new method to predict the exit station’s traffic flow in the future 5 minutes. After deleting abnormal data, we select 12 of the 109 entry toll stations for the experiment. The traffic flow of these 12 entry stations account for 86% of the total exit traffic flow. This method uses the spatial-temporal matrix to deal with different three scenes that are ETC and MTC charging systems individually, the mix of ETC and MTC. We use the LSTM model with various lengths of flow sequence and amounts of hidden layer neurons for three different scenes. Lastly, we validate our model and carefully select the hyperparameters for better prediction accuracy by three evaluation metrics. The experimental results demonstrate that predicting the ETC is the best in the three scenes.

Keywords

• Short-term traffic flow prediction
• spatial-temporal matrix
• LSTM
• OD traffic data
• ETC and MTC