Prediction of toll evasion behaviors based on electronic toll collection data

YiHeng Su; FuMin Zou; LvChao Liao

doi:10.1117/12.2674826

2 May 2023 Prediction of toll evasion behaviors based on electronic toll collection data

YiHeng Su, FuMin Zou, LvChao Liao

Proceedings Volume 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023); 126421D (2023) https://doi.org/10.1117/12.2674826
Event: Second International Conference on Electronic Information Engineering, Big Data and Computer Technology (EIBDCT 2023), 2023, Xishuangbanna, China

Abstract

In order to improve the efficiency of the audit of toll evasion, a toll evasion prediction model is established based on historical ETC(Electronic Toll Collection)portal flow transaction data. Now it has become the most perfect Internet of Things system for expressways, which can be used to strengthen the management of traffic toll supervision. First, the complete trajectory data set of vehicle travel was constructed based on the ETC transaction data, the trajectory data set of vehicle travel were divided into multiple sections for analysis. Second, density clustering was used to partition toll evasion data set. Finally, a random forest algorithm was used to construct a prediction model of toll evasion behaviors. The correct prediction ratio of the model for toll evasion behaviors was 91.3%.To avoid training over-fitting, ETC plate recognition data is used for proving and accurate discovery of toll evasion behaviors validates the feasibility of the method.

Citation Download Citation

YiHeng Su, FuMin Zou, and LvChao Liao "Prediction of toll evasion behaviors based on electronic toll collection data", Proc. SPIE 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023), 126421D (2 May 2023); https://doi.org/10.1117/12.2674826

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available