پیش‌بینی زمان‌ ورود اتوبوس به ایستگاه با استفاده از سیستم‌ استنتاج فازی-عصبی تطبیقی

نوع مقاله : علمی - پژوهشی

نویسندگان
1 دانشجوی دکتری، پردیس دانشکده‌های فنی، دانشگاه تهران، تهران، ایران
2 استادیار، دانشکده مهندسی عمران، دانشگاه یزد، یزد، ایران
چکیده
پیش‌بینی دقیق زمان‌ ورود اتوبوس به ایستگاه‌ و اطلاع‌رسانی آن به مسافران در بهبود کیفیت خدمات حمل‌ونقل همگانی موثر است. افزایش کیفیت خدمات در یک سیستم اتوبوس‌رانی موجب افزایش مطلوبیت این شیوه سفر و به تبع آن ترغیب مسافران به استفاده‌ از این شیوه حمل‌ و نقلی می‌شود. در این راستا در این پژوهش، مدلی مبتنی بر «سیستم استنتاج عصبی- فازی انطباقی» برای پیش‌بینی زمان‌رسیدن اتوبوس به ایستگاه ارائه می‌شود. برای پرداخت و اعتبارسنجی مدل پیشنهادی از داده‌های تاریخچه‌زمانی زمان‌ سفر حاصل از سامانه موقعیت‌یاب اتوبوس‌های خط آزادی-ونک شرکت اتوبوس‌رانی تهران استفاده شده است. نتایج این مطالعه نشان می‌دهد که مدل مبتنی بر «سیستم استنتاج عصبی- فازی انطباقی» قادر است در بیش‌تر از 86 درصد موارد با خطای حداکثر برابر با 20 درصد، زمان‌ورود اتوبوس به ایستگاه را پیش‌بینی ‌کند. در مطالعه حاضر، به منظور مقایسه نتایج، مدل پیش‌بینی دیگری مبتنی بر رگرسیون خطی ایجاد شد. نتایج، مقایسه دو مدل، بیانگر برتری اندک مدل مبتنی بر «سیستم استنتاج فازی–عصبی تطبیقی» نسبت به مدل رگرسیون خطی است.

کلیدواژه‌ها

موضوعات


عنوان مقاله English

Bus Arrival Time Prediction Using Adaptive Neuro-fuzzy Inference System

نویسندگان English

Alireza Ganjkhanloo 1
Mojtaba Rajabi-Bahaabadi 2
1 Ph.D. candidate, School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran
2 Assistant Professor, Department of Civil Engineering, Yazd University, Yazd, Iran
چکیده English

The provision of accurate bus arrival travel time information is vital since it increases the quality of public transport services. Improving the quality of service increases the satisfaction of bus users, which in turn attracts additional ridership. In this paper, a model based on the adaptive neural-fuzzy inference system is developed to predict bus arrival time at bus stops. Real travel time data from a bus route located in Tehran, Iran, collected over three months is used for calibration and validation of the model. A regression-based model is also developed to predict bus arrival time. The results of this study show that the neuro-fuzzy model can predict arrival time in more than 86% of cases with a maximum error of 20%. The root-mean-squared error is employed as an index to compare the proposed model with the regression-based model. It is found that the neuro-fuzzy model outperforms the regression-based model in terms of prediction accuracy.

کلیدواژه‌ها English

Arrival time prediction
Adaptive neuro-fuzzy inference system (ANFIS)
Travel time reliability
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  • تاریخ دریافت 24 بهمن 1401
  • تاریخ بازنگری 03 خرداد 1402
  • تاریخ پذیرش 20 خرداد 1402