Paper Info
Title
Recognition of Vehicles Entering Expressway Service Areas and Estimation of Dwell Time Using ETC Data
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.
Year
DOI
Venue
2022
10.3390/e24091208
ENTROPY
Keywords
DocType
Volume
VR-XGBoost, K-VDTE, ETC data, ESAs, data mining
Journal
24
Issue
ISSN
Citations 
9
1099-4300
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Qiqin Cai100.34
Dingrong Yi200.34
Fumin Zou337.16
Zhaoyi Zhou400.34
Nan Li500.34
Feng Guo600.68