توسعۀ یک مدل تصمیم‌گیری مبتنی بر محاسبات نرم جهت پیش‌بینی شدت تصادفات در راه‌های برون‌شهری

نوع مقاله : علمی - پژوهشی

نویسنده

استادیار، گروه مهندسی عمران، دانشکدۀ فنی دانشگاه گیلان، رشت

چکیده

توسعۀ روش‌های جدید و هوشمند برای جلوگیری از وقوع تصادف یا کاهش شدت تصادفات در راه‌های برون‌شهری یکی از اهداف اصلی مطالعات ایمنی راه است. هدف این تحقیق تلفیق قابلیت‌های سیستم‌های اطلاعات مکانی (GIS) با روش‌های مبتنی بر محاسبات نرم، جهت برآورد شدت تصادفات و تعیین فاکتورهای مؤثر بر آن در راه‌های دو‌خطۀ برون‌شهری است. روش پیشنهادی با ارائۀ مدل درخت دسته‌بندی و رگرسیون فازی (FCART) و ایجاد پایگاه دادۀ مکانمند متشکل از داده‌های تصادفات و اطلاعات راه و محیط مجاور آن در محور قزوین- رشت (ایران) بررسی می‌شود. نتایج با استفاده روش اعتبارسنجی ده‌قسمتی بر رویدادهایی که شدت تصادفات آنها معلوم است، ارزیابی و با مدل درخت دسته‌بندی و رگرسیون (CART) مقایسه می‌شود. نتایج نشان می‌دهد که مدل درخت دسته‌بندی و رگرسیون فازی در مقایسه با درخت تصمیم CART فرایند استنتاج قوی‌تر‌ی دارد و شدت تصادفات را با صحت بیشتری پیش‌بینی می‌کند. تحلیل حساسیت روش پیشنهادی ضمن کشف تأثیرات مکانی طرح هندسی و عوارض و کاربری‌های مجاور راه بر شدت تصادفات، نقص فنی خودرو، کمربند ایمنی و شرایط آب‌وهوایی را نیز مهم‌ترین فاکتورهای تأثیر‌گذار در شدت تصادف می‌شمارد. این مطالعه به متخصصان ایمنی راه کمک ‌می‌کند تا عوامل مکانی تأثیرگذار در سطوح متفاوت شدت تصادفات را شناسایی کنند و اقدامات پیشگیرانۀ لازم را برای کاهش شدت یا جلوگیری از وقوع تصادفات انجام دهند.

کلیدواژه‌ها

عنوان مقاله [English]

Developing a Soft Computing based Decision Tree Approach for Predicting Crashes Severity on Regional Highway Corrid

نویسنده [English]

  • M Effati
Assistant Professor, Department of Civil Engineering, Faculty of Engineering, The University of Guilan, Rasht
چکیده [English]

Developing intelligent and novel methods for crash prevention or reducing crash severity in regional highway corridor is one of the major goals of road safety research. This study presents a comprehensive approach using geospatial information systems and data mining to analyze the severity of highway corridors crashes and identify the most spatial contributing factors. The approach implements Fuzzy Classification and Regression Tree (FCART) on a database of spatial data and four year period accident records in the study corridor (Qazvin-Rasht). The proposed method is tested on the crash data using a 10-fold cross validation process and the results are compared with Classification and Regression Tree (CART) model. The results show that FCART model inducts crash severity better than CART model and its overall accuracy is higher than CART model. Moreover, the sensitivity analysis of FCART model indicates that beside vehicle failure, using seatbelt and weather condition factors, curve and the spatial distribution and prevalence of activities and land uses in the proximity of highway corridors are among the most important factors affecting the severity of injuries and increase opportunities for crash occurrences.

کلیدواژه‌ها [English]

  • Spatial analysis
  • Soft Computing
  • Crash Severity prediction
  • Regional Highway Corridor

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