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دسته بندی:
یادگیری تقویتی - Reinforcement-Learning
سال انتشار:
2020
عنوان انگلیسی مقاله:
A reinforcement learning model for personalized driving policies identification
ترجمه فارسی عنوان مقاله:
یک مدل یادگیری تقویتی برای شناسایی شخصیت های سیاسی محور
منبع:
Sciencedirect - Elsevier - International Journal of Transportation Science and Technology, Corrected proof. doi:10.1016/j.ijtst.2020.03.002
نویسنده:
Dimitris M. Vlachogiannis a,⇑, Eleni I. Vlahogianni b, John Golias b
چکیده انگلیسی:
Optimizing driving performance by addressing personalized aspects of driving behavior
and without posing unrealistic restrictions on personal mobility may have far reaching
implications to traffic safety, flow operations and the environment, as well as significant
benefits for users. The present work addresses the problem of delivering personalized driving
policies based on Reinforcement Learning for enhancing existing Intelligent
Transportation Systems (ITS) to the benefit of traffic management and road safety. The proposed
framework is implemented on appropriate driving behavior metrics derived from
smartphone sensors’ data streams. Aggressiveness, speeding and mobile usage are considered
to describe the driving profile per trip and are presented as inputs to the Q-learning
algorithm. The implementation of the proposed methodological approach produces personalized
quantified driving policies to be exploited for self-improvement. Finally, this
paper establishes validation measures of the quality and effectiveness of the produced policies
and methodological tools for comparing and classifying the examined drivers.
Keywords: Reinforcement learning | Q-learning | Machine learning | Intelligent transportation systems | Traffic data
قیمت: رایگان
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