Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Recognition of Vehicles Entering Expressway Service Areas and Estimation of Dwell Time Using ETC Data

Version 1 : Received: 10 August 2022 / Approved: 12 August 2022 / Online: 12 August 2022 (03:53:23 CEST)

A peer-reviewed article of this Preprint also exists.

Cai, Q.; Yi, D.; Zou, F.; Zhou, Z.; Li, N.; Guo, F. Recognition of Vehicles Entering Expressway Service Areas and Estimation of Dwell Time Using ETC Data. Entropy 2022, 24, 1208. Cai, Q.; Yi, D.; Zou, F.; Zhou, Z.; Li, N.; Guo, F. Recognition of Vehicles Entering Expressway Service Areas and Estimation of Dwell Time Using ETC Data. Entropy 2022, 24, 1208.

Journal reference: Entropy 2022, 24, 1208
DOI: 10.3390/e24091208

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 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 s and 14 s, respectively, and the root mean square errors (RMSEs) are 69 s and 22 s, respectively. In addition, the confidence level of controlling the MAE of dwell time within 2 minutes is more than 97%. This work can effectively identify 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.

Keywords

VR-XGBoost; K-VDTE; ETC data; ESAs; data mining

Subject

ENGINEERING, Automotive Engineering

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