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author:

Cai, Q. (Cai, Q..) [1] | Yi, D. (Yi, D..) [2] | Zou, F. (Zou, F..) [3] | Zhou, Z. (Zhou, Z..) [4] | Li, N. (Li, N..) [5] | Guo, F. (Guo, F..) [6]

Indexed by:

Scopus

Abstract:

To scientifically and effectively evaluate the service capacity of expressway service areas (ESAs) and improve the management level of ESAs, we propose a method for the recognition of vehicles entering ESAs (VeESAs) and estimation of vehicle dwell times using electronic toll collection (ETC) data. First, the ETC data and their advantages are described in detail, and then the cleaning rules are designed according to the characteristics of the ETC data. Second, we established feature engineering according to the characteristics of VeESA and proposed the XGBoost-based VeESA recognition (VR-XGBoost) model. Studied the driving rules in depth, we constructed a kinematics-based vehicle dwell time estimation (K-VDTE) model. The field validation in Part A/B of Yangli ESA using real ETC transaction data demonstrates that the effectiveness of our proposal outperforms the current state-of-the-art. Specifically, in Part A and Part B, the recognition accuracies of VR-XGBoost are 95.9% and 97.4%, respectively, the mean absolute errors (MAEs) of dwell time are 52 and 14 s, respectively, and the root mean square errors (RMSEs) are 69 and 22 s, respectively. In addition, the confidence level of controlling the MAE of dwell time within 2 min is more than 97%. This work can effectively recognize the VeESA and accurately estimate the dwell time, which can provide a reference idea and theoretical basis for the service capacity evaluation and layout optimization of the ESA. © 2022 by the authors.

Keyword:

data mining ESAs ETC data K-VDTE VR-XGBoost

Community:

  • [ 1 ] [Cai, Q.]School of Mechanical Engineering and Automation, Huaqiao University, Xiamen, 361021, China
  • [ 2 ] [Cai, Q.]Fujian Key Laboratory for Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou, 350118, China
  • [ 3 ] [Cai, Q.]Digital Fujian Traffic Big Data Research Institute, Fujian University of Technology, Fuzhou, 350118, China
  • [ 4 ] [Yi, D.]School of Mechanical Engineering and Automation, Huaqiao University, Xiamen, 361021, China
  • [ 5 ] [Zou, F.]Fujian Key Laboratory for Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou, 350118, China
  • [ 6 ] [Zou, F.]Digital Fujian Traffic Big Data Research Institute, Fujian University of Technology, Fuzhou, 350118, China
  • [ 7 ] [Zhou, Z.]Digital Fujian Traffic Big Data Research Institute, Fujian University of Technology, Fuzhou, 350118, China
  • [ 8 ] [Li, N.]Digital Fujian Traffic Big Data Research Institute, Fujian University of Technology, Fuzhou, 350118, China
  • [ 9 ] [Guo, F.]College of Computer and Data Science, Fuzhou University, Fuzhou, 350108, China

Reprint 's Address:

  • [Zou, F.]Fujian Key Laboratory for Automotive Electronics and Electric Drive, China

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Source :

Entropy

ISSN: 1099-4300

Year: 2022

Issue: 9

Volume: 24

2 . 7

JCR@2022

2 . 1 0 0

JCR@2023

ESI HC Threshold:55

JCR Journal Grade:2

CAS Journal Grade:3

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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