پایش و مقایسه رویکردهای مختلف پیش‌بینی کوتاه مدت پارامترهای ترافیک شهری و شبیه-سازی آن به کمک سیستم اطلاعات جغرافیایی : (مطالعه موردی شهر لندن)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشیار، گروه نقشه‌برداری، دانشکده فنی و مهندسی مرند، دانشگاه تبریز، تبریز، ایران

2 دانش آموخته کارشناسی ارشد‌، دانشکده فنی و مهندسی مرند، دانشگاه تبریز، تبریز، ایران

چکیده

هدف این تحقیق، مقایسه روش‌های مختلف برای پیش‌بینی کوتاه‌مدت پارامترهای ترافیک شهری و همچنین شبیه‌سازی پارامترهای ترافیکی در محیط متلب و انتخاب بهینه پارامترهای مؤثر آن با سیستم اطلاعات مکانی، به عنوان مکمل سیستم اطلاعات حمل‌ونقل است. برای این منظور از سه روش مختلف پیش‌بینی کوتاه‌مدت پارامترهای ترافیکی، روش چندجمله‌ای کلاسیک، الگوریتم شبکه های عصبی و چندجمله‌ای مبتنی بر ژنتیک به همراه دو روش کاهش خطا استفاده شد. همچنین، پارامترهای ترافیک شهری جریان و سرعت برای کنترل ترافیک آینده شبیه‌سازی شدند. به دلیل عدم دسترسی به داده های ترافیکی منظم در ایران، داده های تحقیق برای این مطالعه از داده های سال 2012 تا 2014 در لندن با رفتار ترافیکی مشابه در طول هفته انتخاب گردید. مسیرهای مورد مطالعه در این پژوهش، جمعاً بطول 84/15 کیلومتر، تحت نام‌های LM561-LM563-LM557-LM555 مورد بررسی قرار گرفت. داده‌های سال‌های 2012، 2013 و 2014 به‌عنوان داده‌های آموزشی، داده‌های اعتبارسنجی و داده‌های مرجع برای اعتبارسنجی، به ترتیب مورد استفاده قرار گرفتند. به طور کلی، نتایج نشان داد که مدل چندجمله‌ای کلاسیک در پیش‌بینی پارامترهای ترافیکی جریان و سرعت خیلی موفق و کارآمد نیست، اما مدل چند جمله‌ای مبتنی بر ژنتیک و شبکه‌های عصبی موفق عمل کردند. علاوه بر این، یافته‌های کمی چهار مسیر مطالعاتی بر حسب خطای جذر میانگین مربعات نشان داد که سه روش چندجمله‌ای کلاسیک، چندجمله‌ای بر مبنای ژنتیک و شبکه-های عصبی برای پارامتر ترافیکی جریان به ترتیب برابر با 91/13، 78/0و 22/0 و برای پارامتر سرعت به ترتیب برابر با 20/5، 78/0و 19/0 می باشند. به عبارت دیگر، دقت پیش‌بینی پارامتر جریان ترافیک در روش‌های چندجمله‌ای مبتنی بر ژنتیک و شبکه‌های عصبی به ترتیب تقریباً 18 و 63 برابر بهتر از روش چند جمله‌ای کلاسیک و دقت پیش‌بینی پارامتر سرعت تقریباً 7 و 27 برابر بهتر بود.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Monitoring and comparing various approaches for short-term forecasting of urban traffic parameters and simulation using GIS: (Case study of the city of London)

نویسندگان [English]

  • Hassan Emami 1
  • Amir Rafati 2
1 Associate Professor, Marand Engineering Faculty, University of Tabriz, Tabriz-Iran.
2 M.Sc., Graduated, Marand Engineering Faculty, University of Tabriz, Tabriz-Iran.
چکیده [English]

The main objective of this research is to compare different methods for short-term forecasting of urban traffic parameters, as well as simulation of traffic parameters in the MATLAB environment and optimal selection of their effective parameters with a Geographic Information System (GIS) as a supplement to a transportation information system. To that end, three distinct short-term traffic parameter forecasting algorithms, traditional polynomials (TP), genetic basis traditional polynomials (GBT), and neural networks (NN), were used to predict traffic parameters using two error reduction strategies. In addition, to control future traffic, urban traffic flow and velocity parameters were simulated. Due to a lack of regular traffic data in Iran, the research data for this study was drawn from data from 2012 to 2014 in London, with similar traffic patterns during the week. The routes investigated total 15.84 km and are known as LM561-LM563-LM557-LM555. Training, validation, and reference data were obtained in 2012, 2013, and 2014, respectively. Overall, the findings revealed that the TP approach failed to forecast traffic flow and speed characteristics, but the GBT and NN methods were effective. Furthermore, the quantitative findings of the study routes in terms of root mean square error revealed that the three techniques of TP, GBT, and NN for traffic flow parameters were 13.91, 0.78, and 0.22, respectively, and for the speed parameter, 5.20, 0.78, and 0.19. In other words, the accuracy of the traffic flow parameter in GBT and NN is about 18 and 63 times better than the TP technique, while the accuracy of the speed parameter is approximately 7 and 27 times better than the TP method, respectively.

کلیدواژه‌ها [English]

  • Short-term prediction of Urban Traffic parameters
  • neural networks
  • simulation
  • geographic information system

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