Expressway Vehicle Arrival Time Estimation Algorithm Based on Electronic Toll Collection Data

Shukun Lai (), Hongke Xu, Yongyu Luo, Fumin Zou, Zerong Hu and Huan Zhong
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Shukun Lai: School of Electronics and Control Engineering, Chang’an University, Xi’an 710064, China
Hongke Xu: School of Electronics and Control Engineering, Chang’an University, Xi’an 710064, China
Yongyu Luo: Fujian Provincial Expressway Information Technology Co., Ltd., Fuzhou 350001, China
Fumin Zou: Fujian Key Laboratory of Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou 350108, China
Zerong Hu: Fujian Key Laboratory of Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou 350108, China
Huan Zhong: Fujian Key Laboratory of Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou 350108, China

Sustainability, 2024, vol. 16, issue 13, 1-30

Abstract: Precise travel time prediction benefits travelers and traffic managers by enabling anticipation of future roadway conditions, thus aiding in pre-trip planning and the development of traffic control strategies. This approach contributes to reducing travel time and alleviating traffic congestion issues. To achieve real-time state perception of vehicles on expressways, we propose an algorithm to estimate the arrival time of vehicles in the next segment using Electronic Toll Collection (ETC) data. Firstly, the characteristics of ETC data and GPS data are meticulously described. We devise algorithms for data cleaning and fusion, subsequently segmenting the vehicle journey into multiple sub-segments. In the following step, feature vectors are constructed from the fused data to detect service areas and analyze the expressway segment characteristics, vehicle traits, and the influence of service areas. Finally, an algorithm utilizing LightGBM is introduced for estimating the arrival time of vehicles at various segments, corroborated by empirical tests using authentic traffic data. The M A E of the algorithm is recorded as 20.1 s, with an R M S E of 32.6 s, affirming its efficacy. The method proposed in this paper can help optimize transportation systems for improving efficiency, alleviating congestion, reducing emissions, and enhancing safety.

Keywords: travel choice behavior in ITS; arrival time estimation; ETC data; expressway (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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