پیاده‌سازی یک مدل چندعامله فازی برای مدیریت ترافیک تخلیه شهر با استفاده از آتوماتای احتمالی
محورهای موضوعی : مهندسی برق و کامپیوتر
امیررضا کرباسچیان
1
,
سعيد ستايشي
2
,
آرش شريفي
3
1 - دانشگاه آزاد اسلامی واحد علوم و تحقیقات تهران
2 - دانشگاه صنعتي امير كبير
3 - دانشگاه آزاد اسلامی واحد علوم و تحقیقات تهران
کلید واژه: مدیریت ترافیک مدل‌سازی مبتنی بر عامل سیستم فازی سیستم چندعامله آتوماتای احتمالی,
چکیده مقاله :
به دلیل اهمیت تخلیه سریع شهر هنگام وقوع حوادث طبیعی یا غیر طبیعی، اعمال یک سیاست کنترلی بهینه برای جلوگیری از بروز تراکم و توقف وسایل نقلیه امری لازم و ضروری است. روش‌های موجود برای مدیریت ترافیک در شرایط بحران کمتر به استفاده از رویکردهای هوش مصنوعی پرداخته‌اند و به همین دلیل، هدف اصلی مؤلفین در این پژوهش ارائه یک رویکرد کنترلی بهینه و هوشمند برای ترافیک تخلیه شهر است. در این رویکرد از سیستم استنتاج فازی برای تصمیم‌گیری هر عامل و از آتوماتای احتمالی برای بهینه‌کردن عملکرد عامل‌ها با توجه به ترجیحات هر کدام از آنها در طول زمان استفاده شده است. برای بررسی میزان موفقیت رویکرد کنترلی پیشنهادی، شبیه‌سازی مبتنی بر عامل در محیط‌های RStudio و NetLogo و با استفاده از بسته‌های RNetlogo و frbs در زبان R انجام شده است. نتایج شبیه‌سازی نشان‌دهنده توزیع بار ترافیک، استفاده حداکثری از ظرفیت معابر و پیش‌گیری از بروز تراکم توسط رویکرد پیشنهادی است. با توجه به فناوری‌های ارتباطی نظیر GPS، گوشی‌های تلفن همراه هوشمند، سیستم‌های پرداخت عوارض خودکار الکترونیکی در معابر و ... که در سال‌های اخیر گسترش یافته‌اند، امکان پیاده‌سازی روش کنترل ترافیک بحران پیشنهادی در عمل نیز وجود خواهد داشت.
Because of importance of quickly city evacuation during natural or unnatural happenings, it’s essential to apply an optimized control policy to prevent congestion and stop of vehicles. Existing works for traffic management in critical conditions have paid little attention to artificial intelligence approaches. Therefore, the main goal of authors in this research is offering an optimized and intelligent control policy for city evacuation traffic. This policy uses fuzzy inference system for decision making of each agent and probabilistic automata for optimizing performance of agents as for their preferences during time. To check degree of success of offered control policy, Agent Base Simulation in RStudio and Netlogo environments have been implemented using RNetlogo and frbs packages in R language. Simulation results show traffic load distribution, using maximum capacity of roads and congestion prevention by suggested policy. With regard to communication technologies such as GPS, smart phones, automatic tax payment systems in roads and … that have been developed in recent years, it is also possible to implement suggested critical traffic control policy in real world.
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