A Hybrid Model for Lane-Level Traffic Flow Forecasting Based on Complete Ensemble Empirical Mode Decomposition and Extreme Gradient Boosting

Wenqi Lu,Yikang Rui,Yuanli Gu,Bin Ran,Ziwei Yi

doi:10.1109/access.2020.2977219

Abstract

Accurate and efficient lane-level traffic flow prediction is a challenging issue in the framework of the connected automated vehicle highway system. However, most existing traffic flow forecasting methods concentrate on mining the spatio-temporal characteristics of the traffic flow rather than increasing predictability of traffic flow. In this paper, we propose a novel hybrid model (CEEMDAN-XGBoost) for lane-level traffic flow prediction based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and extreme gradient boosting (XGBoost). The CEEMDAN method is introduced to decompose the raw traffic flow data into several intrinsic mode function components and one residual component. Then, the XGBoost methods are trained and make predictions on the decomposed components respectively. The final prediction results are obtained by integrating the prediction outputs of the XGBoost methods. For illustrative purposes, the ground-truth lane-level traffic flow data captured by remote traffic microwave sensors installed on the 3 rd Ring Road of Beijing are utilized to evaluate the effectiveness of the CEEMDAN-XGBoost model. The experimental results confirm that the CEEMDAN-XGBoost model is capable of fitting the complex volatility of traffic flow efficiently at different types of lane sections. Moreover, the proposed model outperforms the state-of-the-art models (e.g., artificial neural networks and long short-term memory neural network) and other XGBoost-based models in terms of prediction accuracy and stability.

Highlights

Accurate and timely traffic flow forecasting is critical for the successful development of intelligent transportation systems (ITS)
Validated by real-world traffic flow data of lanes captured by several remote traffic microwave sensors (RTMS) installed on the 3rd Ring Road of Beijing with the sampling time interval of 2 min, the CEEMDAN-XGBoost model outperforms both the traditional and state-of-the-art benchmark models in terms of prediction accuracy and stability
It can be found that complicated original traffic data shown in Figure.3 is decomposed into 15 low-noise intrinsic mode functions (IMF) components and one residual component

Summary

Introduction

Accurate and timely traffic flow forecasting is critical for the successful development of intelligent transportation systems (ITS). It can benefit both traffic management agencies and travelers by contributing to various kinds of key applications such as variable speed limit control and route guidance systems. The associate editor coordinating the review of this manuscript and approving it for publication was Huiling Chen. These data has brought on immense development and spread of ITS technologies [1]. A novel data-driven research area has been systematically growing in parallel to the ell-founded mathematical models that are based on macroscopic and microscopic theories of traffic flow [2]. In the environment of the CAVH system, the traffic flow on the road is generally mixed with human-driven

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Conclusion