Inspection Method, Device, Computer Equipment and Readable Storage Medium for Fare Evasion Behavior

By obtaining and analyzing the charge evasion characteristics of ETC lanes, using logistic regression and decision tree algorithms to predict the probability of vehicle charge evasion, the problem of inefficiency of existing audit methods is solved, and efficient identification and early warning of charge evasion behavior is achieved.

CN119691414BActive Publication Date: 2025-07-29ANHUI YILU WEIHANG TECH CO LTD
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Patent Information

Application Number
CN202510210890.7
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-02-25
Publication Date
2025-07-29
Estimated Expiration
2045-02-25

AI Technical Summary

Technical Problem

The existing ETC audit methods are inefficient and lagging, making it difficult to effectively identify and warn of fee evasion, resulting in serious loss of tolls.

Method used

By obtaining multiple preset fee escape features related to passing ETC lane, the initial feature vector of the target vehicle is determined, and the target feature vector is calculated based on the preset fee escape weight and initial feature vector. The logistic regression model and fee escape decision tree are used to predict the probability of the vehicle's fee escape behavior in the ETC lane.

Benefits of technology

It improves the accuracy and efficiency of identifying fee evasion behaviors, can early warning of potential fee evasion behaviors, and reduces the cost and difficulty of post-processing.

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Abstract

This application relates to a method, device, computer equipment and readable storage medium for inspecting toll evasion behavior. The method includes: obtaining a plurality of preset toll evasion features related to passing through an ETC lane, and determining an initial feature vector of a target vehicle for each preset toll evasion feature; obtaining preset toll evasion weights for each of the plurality of preset toll evasion features; according to the preset toll evasion weights and the initial feature vector of the target vehicle for each preset toll evasion feature, determining a target feature vector of the target vehicle for each preset toll evasion feature; based on the target feature vector of the target vehicle for each preset toll evasion feature, determining the probability that the target vehicle has toll evasion behavior when passing through the ETC lane. Using this method can not only improve the speed and efficiency of identifying toll evading vehicles, but also early warn potential toll evasion behaviors, provide ex-ante inspection means, and reduce the cost and difficulty of ex-post processing.
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