A STUDY ON ESTIMATING EFFECTIVE METHODS OF TRAFFIC ENFORCEMENT ACTIVITIES USING DEEP Q-NETWORK

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  • Deep Q-Network を用いた効果的な取締り活動方法の推計に関する研究

Abstract

<p>In the past, the police have conducted traffic enforcement activities as a countermeasure against traffic accidents, and have achieved a certain degree of success. However, the relationship between traffic accidents and traffic enforcement activities is still unclear, and traffic enforcement activities based on clear evidence have not yet been implemented. In this context, there is a movement toward the use of artificial intelligence to predict the occurrence of traffic accidents and to implement traffic effective enforcement activities. In this study, we developed an accident prediction model that takes into account traffic enforcement activities, and evaluated the relationship between traffic accidents and traffic enforcement activities. In addition, we developed a model to estimate effective methods of traffic enforcement activities using Deep Q-Network, a type of artificial intelligence. As a result, we were able to quantitatively understand the deterrent effect of traffic enforcement activities on traffic accidents, and we were able to specifically propose appropriate traffic enforcement activity methods according to traffic conditions.</p>

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