A Data-Driven Approach to W-Beam Barrier Monitoring Data Processing: A Case Study of Highway Congestion Mitigation Strategy

Weiguang Mu and Chengzhu Gong ()
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Weiguang Mu: TECH Traffic Engineering Group Corporation, Beijing 100089, China
Chengzhu Gong: School of Economics and Management, China University of Geosciences, Wuhan 430074, China

Sustainability, 2023, vol. 15, issue 5, 1-14

Abstract: In this paper, a data-driven approach is used to process W-Beam Barrier monitoring data, expecting to achieve online estimation of the number of trucks and accurate identification of barrier impact events. By analyzing the data features, significant noise was found in the original data, hiding the useful information, so this paper proposes an improved wavelet thresholding algorithm to achieve data denoising. As there is no study of the same application, this paper compares three commonly used data fault diagnosis algorithms: Principal Component Analysis (PCA), Partial Least Squares (PLS) and Fisher Discrimination Analysis (FDA). By designing and conducting comparison experiments, the results show that the PCA model is more suitable for estimating the number of trucks and the FDA model is more suitable for identifying barrier impact events. The data processing results are shared with the highway operation management system as a trigger condition to enable the strategy of forbidden truck overtaking. Through long-term application, the results show that highway capacity is improved by 12.7% and the congestion index and emissions are slightly reduced after adopting this paper’s method.

Keywords: traffic information engineering and control; W-Beam barrier monitoring; data-driven; fault diagnosis; principal component analysis; partial least squares (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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