پژوهشنامه حمل و نقل

پژوهشنامه حمل و نقل

پیش‌بینی حجم تردد در ایام تعطیل با استفاده از مدل شبکه عصبی مصنوعی (نمونه موردی: راه‌های برون‌شهری ایران)

نوع مقاله : مقاله پژوهشی

نویسندگان
1 استادیار، گروه مهندسی حمل‌ونقل، دانشکده مهندسی عمران، آب و محیط زیست، دانشگاه شهید بهشتی، تهران، ایران
2 دانشجوی دکتری، دانشکده مهندسی صنایع، دانشگاه صنعتی شریف، تهران، ایران
3 دانشجوی کارشناسی ارشد، دانشکده مهندسی عمران، آب و محیط زیست، دانشگاه شهید بهشتی، تهران، ایران
چکیده
حجم ترافیک از جمله مهم‌ترین پارامترهای ترافیکی است که در بسیاری از تحلیل‌های حمل‌ونقلی از جمله طراحی، برنامه‌ریزی، سیاست‌گذاری و نیز توسعه مدل‌های مختلف مورداستفاده قرار می‌گیرد. در ایام تعطیل، با اوج‌گرفتن سفرهای برون‌شهری، الگوی حجم ترافیک در شبکه راه‌های برون‌شهری کشور دچار تغییرات قابل‌توجهی می‌شود. در ایران، تعطیلات با تطابق مناسبت‌های قمری و شمسی الگوهای متفاوتی به خود می‌گیرند و با چینش‌های مختلف روزهای تعطیل، حجم تردد در راه‌های برون‌شهری کشور تغییر محسوسی می‌یابد. هدف از این مطالعه، بررسی الگوی تغییرات حجم تردد در ایام تعطیل و نیز ارائه مدلی برای پیش‌بینی حجم تردد در تعطیلات است. بدین منظور، مدل شبکه عصبی مصنوعی جهت پیش‌بینی حجم تردد در جاده‌های برون‌شهری استان‌های کشور توسعه داده شد که نسبت به تعطیلات حساس است. مقیاس زمانی مدل‌ها، روزانه است و به تفکیک استان‌های کشور ساخته شده‌اند. میانگین درصد خطای مطلق به‌عنوان شاخص ارزیابی مدل‌ها مورداستفاده قرار گرفت. متوسط این شاخص برای تمام مدل‌ها 28/9 درصد و به‌عنوان نمونه برای استان مازندران، 89/8 درصد بود. نتایج این پژوهش به سیاست‌گذاران کمک می‌کند تا در شرایط مختلف ایام سال، حجم تردد در محورهای مواصلاتی کشور را در سطح استان‌-روز پیش‌بینی کرده و از قبل تدابیر لازم را برای مواجه شدن با تراکم‌های ترافیکی در ایام خاص و تعطیلات اتخاذ نمایند. همچنین می‌توان با به‌کارگیری مدل توسعه داده شده، تأثیر سناریوهای مختلف تعطیلات را بر حجم تردد محورهای کشور بررسی نمود.
کلیدواژه‌ها

موضوعات


عنوان مقاله English

Holidays Traffic Volume Prediction Using Neural Network (Case Study: Iran Rural Roads)

نویسندگان English

Sina Sahebi 1
Mana Meskar 2
Mohammad Bafandeh 3
1 Assistant Professor, Civil, Water and Environmental Engineering, Shahid Beheshti University‌, Tehran, Iran.
2 Ph.D., Student, Industrial Engineering, Sharif University of Technology, Tehran, Iran.
3 M.Sc., Student, Civil, Water and Environmental Engineering, Shahid Beheshti University‌, Tehran, Iran.
چکیده English

Traffic volume represents one of the most critical parameters in transportation analyses, influencing various aspects such as design, planning, policy-making, and model development. During holidays, characterized by a peak in out-of-town trips, significant changes occur in the traffic volume pattern across the country's rural road network. In Iran, holidays follow different patterns based on lunar and solar occasions, resulting in notable fluctuations in traffic volume on suburban roads. This study aims to investigate the pattern of changes in traffic volume during holidays and to provide a model for predicting traffic volume during such periods. For this purpose, an artificial neural network model was developed to forecast traffic volume on the suburban roads of the country's provinces, with sensitivity to holidays. The models are designed on a daily time scale and are developed separately for each province. The evaluation metric used for the models is the mean absolute percentage error. The average of this metric across all models was 9.28%, with an example from Mazandaran province showing 8.89%. The results of this research aid policymakers in predicting traffic volume in the transportation networks of the country at the province-day level during different times of the year and enable them to take proactive measures to address traffic congestion during special occasions and holidays. Moreover, the developed model allows for the examination of the effect of different holiday scenarios on the country's traffic volume.

کلیدواژه‌ها English

Traffic Volume Prediction
Holidays
Artificial Neural Network
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