Travel Characteristics Identification Method for Expressway Passenger Cars Based on Electronic Toll Collection Data

Xiaoyu Cai (), Yihan Zhang, Xin Zhang and Bo Peng
Additional contact information
Xiaoyu Cai: College of Smart City, Chongqing Jiaotong University, Chongqing 400074, China
Yihan Zhang: College of Traffic & Transportation, Chongqing Jiaotong University, Chongqing 400074, China
Xin Zhang: Cmcu Engineering Co., Ltd., Chongqing 400039, China
Bo Peng: College of Smart City, Chongqing Jiaotong University, Chongqing 400074, China

Sustainability, 2023, vol. 15, issue 15, 1-28

Abstract: Passenger cars have emerged as a substantial segment of the vehicles traversing expressways, generating extensive traffic data on a daily basis. Accurately identifying individual vehicles and their travel patterns and characteristics is crucial in addressing the issues that impede the sustainable development of expressways, including traffic accidents, congestion, environmental pollution, and losses of both personnel and property. Regrettably, the utilization of electronic toll collection (ETC) data on expressways is currently not adequate, and data analysis and feature mining methods are underdeveloped, leading to the undervaluation of data potential. Focusing on ETC data from expressways, this study deeply analyzes the spatiotemporal characteristics of travel by passenger car users. Here, we propose an advanced user classification model by combining the traditional clustering algorithm with the feature grouping recognition model based on a back propagation neural network (BPNN) algorithm. Real-world data on expressway vehicle travel are used to validate our models. The results show a significant improvement in iteration efficiency of over 26.4% and a 23.17% accuracy improvement compared to traditional algorithms. The travel feature grouping recognition model yielded an accuracy of 95.23%. Furthermore, among the identified groups, such as “Public and commercial affairs” and “Commuting”, there is a notable characteristic of high travel frequency and concentrated travel periods. This indicates that these groups have placed significant pressure on the construction of a safe, efficient, and sustainable urban transportation system.

Keywords: expressway; electronic toll collection data; feature identification; passenger car; K-means and BP neural network combination model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/15/11619/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/15/11619/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:15:p:11619-:d:1204110

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().