مقاله انگلیسی رایگان در مورد یادگیری به سمت تعادل شبکه حمل و نقل – IEEE 2019

IEEE

 

مشخصات مقاله
ترجمه عنوان مقاله یادگیری به سمت تعادل شبکه حمل و نقل: یک مطالعه مقایسه مدل
عنوان انگلیسی مقاله Learning Towards Transportation Network Equilibrium: A Model Comparison Study
انتشار مقاله سال ۲۰۱۹
تعداد صفحات مقاله انگلیسی ۱۱ صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
پایگاه داده نشریه IEEE
نوع نگارش مقاله
مقاله پژوهشی (Research Article)
مقاله بیس این مقاله بیس نمیباشد
نمایه (index) Scopus – Master Journals List – JCR
نوع مقاله ISI
فرمت مقاله انگلیسی  PDF
ایمپکت فاکتور(IF)
۴٫۶۴۱ در سال ۲۰۱۸
شاخص H_index ۵۶ در سال ۲۰۱۹
شاخص SJR ۰٫۶۰۹ در سال ۲۰۱۸
شناسه ISSN ۲۱۶۹-۳۵۳۶
شاخص Quartile (چارک) Q2 در سال ۲۰۱۸
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نوع ارائه مقاله
ژورنال
مجله / کنفرانس دسترسی – IEEE Access
دانشگاه  School of Information Management, Wuhan University, Wuhan 430072, China
کلمات کلیدی بازی انتخاب مسیر، تجربه آزمایشگاهی، یادگیری تقویتی، هماهنگی ضمنی، تعادل نش
کلمات کلیدی انگلیسی  Route choice game, laboratory experiment, reinforcement learning, tacit coordination, nash equilibrium
شناسه دیجیتال – doi
https://doi.org/10.1109/ACCESS.2019.2949576
کد محصول  E13919
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
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فهرست مطالب مقاله:
Abstract
I. Introduction
II. A Brief Literature Review
III. Data and Models
IV. Model Comparison
V. Results
Authors
Figures
References

 

بخشی از متن مقاله:
Abstract

As an interdisciplinary topic, human travel-choice behavior has attracted the interests of transportation managers, theoretical computer science researchers and economists. Recent studies on tacit coordination in iterated route choice games (i.e., a large number of subjects could achieve the transportation network equilibrium in limited rounds) have been driven by two questions. (1) Will learning behavior promote tacit coordination in route choice games? (2) Which learning model can best account for these choices/behaviors? To answer the first question, we choose a set of learning models and conduct extensive simulations to determine their success in accounting for major behavioral patterns. To answer the second question, we compare these models to one another by competitively testing their predictions on four different datasets. Although all the selected models account reasonably well for the slow convergence of the mean route choice to equilibrium, they account only moderately well for the mean frequencies of the roundto-round switches from one route to another and fail to appropriately account for substantial individual differences. The implications of these findings for model construction and testing are briefly discussed.

Introduction

In both transportation and communication networks, where the route choices are decentralized, utility-maximizing players facing strategic uncertainty often strive to avoid congestion [1]. Examples include choosing a restaurant on Saturday evening, selecting of a route in a traffic network, and deciding whether to enter a capacitated market. The notion of equilibrium in such scenarios, once they are modeled appropriately as non-cooperative n-person games, leads us naturally to ask how players achieve this ‘‘meeting of the minds.’’ The focus of the present paper is on the choice of routes in directed networks. We focus on computer-controlled experimental studies of a class of network games, called iterative route choice games. These games have multiple equilibria that, depending on the architecture of the network and the number of network users, are counted in thousands or occasionally in millions. The study of such games falls in the intersection of behavioral economics, transportation science [2], computer science [3], and operations management [4]. If tacit coordination in large groups is neither reached by communication nor deduced by introspection, then it is achieved by learning ‘‘day by day’’ [۵], [۶]. Most previous experimental studies of route choice games largely support this assertion [3].

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