ارایه روشی جهت تحلیل شدت تصادفات راه های برون شهری مبتنی بر توابع خوشه‌بندی مکانی و داده‌کاوی به روش درخت تصمیم، محور مورد مطالعه: آزادراه قزوین-لوشان

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشکده مهندسی عمران، دانشگاه علم‌و‌صتعت ایران، تهران، ایران

2 استادیار، گروه مهندسی عمران (راه و ترابری) دانشکده فنی، دانشگاه گیلان

3 دانشکده مهندسی عمران، دانشگاه علم‌و‌صنعت ایران، تهران، ایران

چکیده

تحلیل مکانی تصادفات رخ داده در راه های برون شهری با هدف شناسایی پارامترهای مؤثر بر افزایش شدت تصادفات، می تواند در تصمیم‌گیری متخصصین و دستاندرکاران اصلاح و بهبود ایمنی راه ها به منظور کاهش شدت تصادفات جاده ای موثر باشد. هدف این تحقیق ارایه روشی جهت تحلیل شدت تصادفات و تعیین عوامل موثر بر آن در آزادراه‌های برون شهری مبتنی بر توابع خوشه‌بندی مکانی و مدل داده کاوی درخت طبقه بندی و رگرسیون است. روش پیشنهادی در آزادراه قزوین-لوشان مورد ارزیابی و آزمون قرار می‌گیرد. در این راستا به منظور بررسی توزیع مکانی تصادفات در محور مورد مطالعه طی دوره 6 ساله 1390 تا 1395 شمسی، از توابع خودهمبستگی مکانی گتیس-ارد جی استار و تراکم کرنل استفاده شده است. خروجی تحلیل‌های مکانی نشان داد، که تمرکز تصادفات در بخش اعظمی از قوس‌های افقی محور مورد مطالعه بیشتر می‌باشد. باتوجه به این دستاورد در فاز بعدی تحقیق به منظور بررسی عوامل موثر بر شدت تصادفات، از مدل داده کاوی درخت طبقه بندی و رگرسیون بر روی تصادفات رخ داده در کل محور و به طور خاص تصادفات رخ داده در قوس‌های افقی استفاده گردید. نتایج حاکی از آن بود که مهم‌ترین عوامل موثر بر افزایش شدت تصادفات در محور مورد مطالعه، دو متغیر نوع تصادفات و عامل انسانی با ضرایب اهمیت نسبی متغیرهای مستقل به ترتیب 100 و 7/39 درصد برای کل محور و 100و 9/65 درصد برای قوس‌های افقی هستند. بررسی اهمیت نسبی سایر متغیرهای مدل پیشنهادی نیز نشان داد که طرح هندسی، نحوه برخورد و روز وقوع تصادف از جمله عوامل موثر در افزایش تصادفات با شدت خسارتی در آزادراه قزوین-لوشان می‌باشد. این تحقیق نشان داد که تلفیق توابع مکانمند GIS با تحلیل‌های ناپارامتریک داده‌کاوی مبتنی بر درخت تصمیم که قابلیت مدل‌سازی توامان داده‌های کمی و کیفی را همزمان دارا می‌باشد، در تعیین عوامل موثر بر شدت تصادفات و تحلیل مکانمند الگوهای رایج تصادفات در محورهای برونشهری کارا و موثر است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Providing a Method for Accident Severity Analysis Using Geospatial Clustering Functions and Decision Tree, Case Study: Qazvin-Loshan Freeway

نویسندگان [English]

  • hamid behbahani 1
  • Meysam Effati 2
  • Samane Mortezaei 3
1 Department of Road and Transportion, Faculty of Civil Engineering, The University Of Elmo-Sanat,Tehran, Iran
2 Assistant Professor, Department of Civil Engineering, Faculty of Engineering The University of Guilan
3 Department of Road and Transportion, Faculty of Civil Engineering, The University Of Elmo-Sanat,Tehran, Iran
چکیده [English]

Spatial analysis of accidents occurred in freeways and identifying effective parameters can help researchers and authorities to improve road safety by reducing the severity of accidents. The purpose of this study is to provide a method to analyze the accident severity and determine related effective parameters in freeways based on spatial clustering functions and regression and classification tree data mining method. Proposed method was assessed in Qazvin-Loshan freeway. In this study, to study the spatial distribution of the accidents in the aforementioned axis during the period from 2011 to 2016, the spatial functions such as Getis-Ord G* autocorrelation and Kernel Density Functions were Used. The results of spatial analysis showed that the spatial gathering of accidents in most of horizontal curves was greater. According to this achievement, in the next phase of the study, in order to study the factors affecting the severity of accidents, the Regression and Classification Tree was used on accidents that occurred in the whole axis and specifically the crashes which occurred in the horizontal curves. Results of this part of the study showed that the type of accidents (overturning and falling, exit from the road, multi-vehicle collisions, etc.) and human factors are the most important factors in the severity of accidents in this axis. Relative importance coefficients for these two independent variables are 100 and 39.7 percent for the whole axis and 100 and 65.9 percent for horizontal curves. The study of the relative importance of other variables used in the proposed model showed that the geometric design, type and date of crashes are among the most effective factors in increasing the property damage only crashes in Qazvin-Loshan Freeway. This study showed that the integration of GIS functions with non-parametric data mining algorithms such as decision tree, which is capable of simultaneous modeling of quantitative and qualitative data, is an effective approach to determine the factors affecting the severity of accidents and to analyze the spatial patterns of accidents in freeways.

کلیدواژه‌ها [English]

  • Road Safety
  • Accidents Severity
  • Getis-Ord G* Autocorrelation
  • kernel Density Functions
  • Classification and Regression Tree

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نشریه مهندسی عمران امیرکبیر
دوره 52، شماره 6
شهریور 1399
صفحه 1419-1438

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