Traffic Flow Prediction at Varied Time Scales via Ensemble Empirical Mode Decomposition and Artificial Neural Network

Xinqiang Chen,Zhijian Qu,Jiansen Zhao,Yongsheng Yang,Jiangfeng Xian,Jinquan Lu

doi:10.3390/su12093678

Abstract

Accurate traffic flow data is crucial for traffic control and management in an intelligent transportation system (ITS), and thus traffic flow prediction research attracts significant attention in the transportation community. Previous studies have suggested that raw traffic flow data may be contaminated by noises caused by unexpected reasons (e.g., loop detector damage, roadway maintenance, etc.), which may degrade traffic flow prediction accuracy. To address this issue, we proposed an ensemble framework via ensemble empirical mode decomposition (EEMD) and artificial neural network (ANN) to predict traffic flow under different time intervals ahead. More specifically, the proposed framework firstly employed the EEMD model to suppress the noises in the raw traffic data, which were then processed to predict traffic flow at time steps under different time scales (i.e., 1, 2, and 10 min). We verified our model performance on three loop detectors’ data, which were supported by the Department of Transportation, Minnesota. The research findings can help traffic participants collect more accurate traffic flow data and thus benefits transportation practitioners by helping them to make more reasonable traffic decisions.

Highlights

Rapid economic development has motivated a sharp increase in traffic demand and, led to various traffic problems
This study aims to propose a simple but efficient traffic flow prediction framework based on ensemble empirical mode decomposition (EEMD) and an artificial neural network (ANN)
We proposed an ensemble framework with EEMD and an ANN model to prediction traffic flow data at different yet typical time scales (i.e., 1-step, 2-step, 6-step, and 10-step)

Summary

Introduction

Rapid economic development has motivated a sharp increase in traffic demand and, led to various traffic problems (e.g., traffic congestion, air pollution, and traffic accidents). Traffic flow prediction provides critical traffic state information for the ITS system, which helps traffic participants make better traveling decisions and enhance traffic operation efficiency [1,2,3,4]. We can develop better traffic control strategies (e.g., adaptive traffic signal control, dynamic speed-limit setting, etc.) and fine-tune more appropriate traffic parameters that consider roadway traffic condition fluctuation interference with the help of traffic flow data. In this manner, the successfulness of traffic control strategy is highly depended on the resolution of traffic flow prediction data [5]. Lane-level traffic flow data is more sensitive to microscopic traffic state estimation accuracy (such as traffic speed, volume, and occupancy) and, has become a hot topic in the traffic community [6,7,8]

Objectives

Methods

Findings

Conclusion