Learning Identification Strategies for Traffic Flow Model: A Review Study
محورهای موضوعی : مجله فناوری اطلاعات در طراحی مهندسی
محبوبه زارع فیض آبادی
1
,
سید عابد حسینی
2
,
محبوبه هوشمند
3
1 - گروه مهندسی برق، واحد مشهد، دانشگاه آزاد اسلامی، مشهد، ایران.
2 - گروه مهندسی برق، واحد مشهد، دانشگاه آزاد اسلامی، مشهد، ایران.
3 - گروه مهندسی کامپیوتر، واحد مشهد، دانشگاه آزاد اسلامی، مشهد، ایران
کلید واژه: Traffic flow model prediction, ARIMA model, Hybrid model, Deep learning, Nonlinear macroscopic traffic model.,
چکیده مقاله :
ترافیک سفر و نتایج آن که شامل آلودگی هوا و اوضاع نابسامان اقتصادی می شود از جمله عواملی است که توسعه شهرهای سالم و پایدار را محدود می کند. پارامترهای مدل جریان ترافیک برای مدیریت شبکه راه های شهری مهم هستند. یادگیری استراتژی شناسایی باید به گونه ای باشد که در مدل سازی شبکه ترافیک موارد سادگی، دقت و اعتبار مدل را تضمین کند. در این مقاله روش‌های مختلف شناسایی سیستم جریان ترافیک از جمله مدل‌سازی جریان ترافیک و پیش‌بینی آن در مقالات مختلف بررسی و تحلیل می‌شود. در انتها مزایا و معایب روش های مختلف در این حوزه دسته بندی می شوند.
چکیده انگلیسی:
Travel traffic and its results, which include air pollution and the chaotic economic situation, are among the factors that limit the development of healthy and sustainable cities. Traffic flow model parameters are important for urban road network management. Learning the identification strategy should be such that the modeling of the traffic network guarantees the simplicity, accuracy, and validation of the model. In this paper, different methods of traffic flow system identification, including traffic flow modeling and its prediction, are reviewed and analyzed in articles. At the end, the advantages and disadvantages of different methods in this field are categ orized.
منابع و مأخذ:
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