Intelligent toll evasion behavior classification method, storage medium and terminal

Abstract

The invention provides an intelligent toll evasion behavior classification method, a storage medium and a terminal, and belongs to the field of highway billing and charging. The method comprises the steps of collecting historical toll evasion cases and normal passing cases, establishing a toll evasion occurrence and type judgment rule model, and verifying and applying the model. The final fee evasion occurrence and type judgment rule model is associated with a highway station charging system, real-time passing vehicles are monitored, whether fee evasion behaviors occur or not is judged, the fee evasion type is judged, and finally a monitoring result is visually displayed to workers. In the method, a machine learning technology is introduced, machine learning model training is carried out by utilizing extracted features through random forest, LASSO, a decision tree, logistic regression, GBDT and other algorithms in a model, and a corresponding machine learning model for detecting whether toll evasion occurs or not and judging the type is generated; besides, new abnormal features are screened in model application, a new type is manually judged and added, and the learning ability and the application range of the model are improved.

Description

technical field [0001] The invention relates to vehicle inspection technology for evasion fees on expressways, and in particular to an intelligent classification method for evasion fee behaviors, a storage medium and a terminal. Background technique [0002] The statements in this section merely provide background technical information related to the present invention and do not necessarily constitute prior art or prior art. [0003] With the continuous expansion of the expressway network, some illegal vehicles use various forms to evade tolls, and the evasion forms are diverse and highly concealed, mainly including ETC toll evasion, CPC toll evasion, fake free toll evasion, and applying for multiple OBUs at the same time And ETC card, screen door clip signal to achieve the minimum rate, etc. [0004] This disrupted the normal order of expressway toll collection and caused huge economic losses. In order to maintain the normal toll collection order and fair payment environm...

AI Technical Summary

Problems solved by technology

[0009] Disadvantage 3: The inspectors of each road section cannot independently configure the rules, cannot fully utilize the front-line business experience of the inspectors of the company's inspectors in each road section, and cannot allow the inspectors to efficiently invest in the link of proactively discovering toll-evading vehicles

[0010] Disadvantage 4: Post-event inspection is the main focus, and there is no early warning during the event: After-the-fact investigation often loses a lot of important information, which makes it difficult to obtain evidence and recover expenses, and cannot effectively deter escaping behavior

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