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

Bangyu Wu,Zhu Chen,Bin Li,Housong Ruan

doi:10.1109/access.2021.3070625

Bangyu Wu, Zhu Chen + Show 2 more

Open Access

https://doi.org/10.1109/access.2021.3070625

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Journal: IEEE Access	Publication Date: Jan 1, 2021
Citations: 50	License type: CC BY 4.0

Affiliation: Xi'an Jiaotong University, Chang'an University

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.

Highlights

With the development of China’s economy, the number of motor vehicles is increasing rapidly, which leads to a series of traffic problems: traffic congestions, traffic accidents, environmental pollution, and so on [1]
Tian et al [42] applied Long Short-Term Memory (LSTM) to short-term traffic flow prediction comparing with other models, such as random walk (RW), support vector machine (SVM), feed forward neural network (FFNN), and stacked autoencoder (SAE)
In this paper, we focus on the short-term traffic flow prediction based on the traffic data of Xinqiao toll station in Shanghai of China and deep learning method — LSTM

Summary

INTRODUCTION

With the development of China’s economy, the number of motor vehicles is increasing rapidly, which leads to a series of traffic problems: traffic congestions, traffic accidents, environmental pollution, and so on [1] To alleviate these traffic problems, researchers pay more and more attention to the Intelligent Transportation Systems (ITS) [2]–[5], which are the set of applications and technological systems created with the aim of improving safety and efficiency in road transport. In this paper, based on origin-destination (OD) data of the Xinqiao toll station in Shanghai, China, we use the Long Short-Term Memory (LSTM) model for short-term traffic flow prediction. 3) We use the LSTM model with various lengths of flow sequence and amounts of hidden layer neurons for three different scenes that are ETC and MTC charging systems individually, the mix of ETC and MTC.

LITERATURE REVIEW

INPUT MATRIX OF THE MODEL

SHORT-TERM TRAFFIC FLOW PREDICTION MODEL BASE ON LSTM

EXPERIMENTS

Findings

CONCLUSION