کاربرد مدل‌های فضایی- مکانی در پیش‌بینی فراوانی تصادفات جاده‌ای (مطالعه موردی: شبکه راه‌های اصلی استان همدان)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار، دانشکده مهندسی عمران، دانشگاه آزاد تهران شمال، تهران، ایران

2 دانشجوی دکتری، دانشکده مهندسی عمران، دانشگاه بین‌المللی امام خمینی قزوین، تهران، ایران

3 مربی،دانشکده عمران و معماری،دانشگاه ملایر،ملایر،ایران

10.22034/tri.2021.250276.2815

چکیده

شناسایی بخش‌های جاده‌ای با خطر تصادف رویکرد ویژه‌ای برای درک بهتر الگوهای تصادفات و افزایش مدیریت ایمنی جاده‌ای به متخصصان ایمنی ارائه می‌دهد. روش‌های معمول شناسایی مقاطع حادثه‌خیز و الگوهای تصادف آن‌قدر قوی نیستند که خصوصیات مکانی داده‌های تصادف را در مدل لحاظ کنند. حوادث رانندگی دارای خاصیت فضایی تمایل به وابستگی مکانی دارند، پدیده‌ای که به‌عنوان همبستگی مکانی شناخته می‌شود. مدل‌های فضایی مقدار پیش‌بینی‌شده الگوی تصادف را در فضا را توصیف می‌کند که می‌تواند ناشی از تغییر در خصوصیات قابل‌توجه محیط محلی باشد با توجه به ماهیت گسسته داده‌های تصادف و دسترسی محدود به مکان‌های دقیق تصادف، یک سطح مداوم که از نقاط گسسته کشیده شده باشد، تراکم تصادف را بهتر منعکس می‌کند و تصویر واقع‌بینانه‌تری از توزیع تصادف ارائه می‌دهد. در این مطالعه کل محورهای اصلی برون‌شهری استان همدان بر اساس داده‌های تصادف مکانمند از سال 1396 تا 1398 با استفاده از روش‌های توزیع چگالی کرنل، رگرسیون وزن‌دار جغرافیایی، رگرسیون پوآسون وزن‌دار جغرافیایی مورد بررسی قرار گرفته است. نتایج مدل‌ها نشان می‌دهد که مدل رگرسیون پوآسن وزن‌دار جغرافیایی نتایج بهتری برای پیش‌بینی مکان‌های تصادف نسبت به سایر مدل‌ها دارد

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Application of Geographical-Spatial Models in Predicting the Frequency of Road Crash (Case Study: Main Road Network of Hamadan Province)

نویسندگان [English]

  • Hasan Khaksar 1
  • Seyed Ahmad Almasi 2
  • Ali Asghar Goharpoor 3
1 Assistant Professor, Civil Engineering, Faculty of Civil Engineering, Azad University, North Tehran Branch, Tehran, Iran.
2 Ph.D., Student, Roads and Transportation Planning, Faculty of Civil Engineering, Faculty of Engineering, Qazvin Imam Khomeini International University, Qazvin, Iran.
3 Department of Engineering, Malayer University, Malayer, Iran
چکیده [English]

Identifying road segment at risk of accidents offers a special approach to safety professionals to better understand crash patterns and enhance road safety management. Conventional methods for identifying accident hotspots and crash patterns are not strong enough to take into account the spatial properties of crash data in the model. Traffic accidents with a spatial nature tend to be spatially dependent, Spatial models describe the predicted value of the crash pattern in space, which can be due to changes in the remarkable properties of the local environment Reflects crash densities better and provides a more realistic picture of crash distribution. In this study, all the main suburban axes of Hamedan province based on spatial accident data from 2017 to 2019 using kernel density distribution methods, geographical weighted regression, (GWR) geographical weighted Poisson regression(GWPR) have been studied. The results of the models show that the geographically weighted Poisson regression(GWPR) model has better results for predicting crash locations than other models.

کلیدواژه‌ها [English]

  • Crash
  • safety
  • spatial distribution

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