پژوهشنامه حمل و نقل

پژوهشنامه حمل و نقل

بررسی تأثیر عوامل هندسی بر شدت تصادفات در راه‌های برون‌شهری با استفاده از الگوریتم‌های هوش مصنوعی و یادگیری عمیق

نوع مقاله : مقاله پژوهشی

نویسندگان
سارا غفاری 1
فرهاد حسینعلی 2
1 دانش آموخته کارشناسی ارشد، گروه مهندسی نقشه برداری، دانشکده مهندسی عمران، دانشگاه تربیت دبیر شهید رجایی، تهران
2 دانشیار، گروه مهندسی نقشه برداری، دانشکده مهندسی عمران، دانشگاه تربیت دبیر شهید رجایی، تهران
10.22034/tri.2024.453155.3240
چکیده
جاده ها به عنوان جزء اساسی حمل و نقل زمینی، با معضلی به نام تصادفات جاده ای رو به رو هستند. تصادفات جاده ای تأثیرات مالی و جانی جبران ناپذیری بر زندگی مردم و جامعه دارند. به همین دلیل اهمیت بررسی ایمنی جاده ها و تصادفات آنها بدیهی به نظر می رسد. در این تحقیق دو محور اردکان-نائین و نائین-اردکان بر اساس شش پارامتر هندسی و محیطی شامل: قوس افقی، فاصله دید، فاصله از تقاطع، فاصله از پل، شانه راه و کاربری محل مورد بررسی قرار گرفتند. هر دو محور بر اساس این شش پارامتر به صورت جداگانه قطعه بندی همگن و بر اساس شدت عوامل هندسی و محیطی حادثه خیز طبقه بندی شده و به نمایش درآمدند. پس از آن با اضافه کردن نقاط تصادف به هر دو محور، با استفاده از شاخص EPDO شدت تصادفات برای هر قطعه محاسبه و در پنج سطح طبقه بندی گردید.سپس با به کارگیری سه روش شبکه عصبی مصنوعی عمیق به نامهای: RNN،CNN و MLFNN شدت تصادفات هر قطعه بر اساس پارامترهای هندسی و محیطی پیش بینی شد. دو روش RNN وCNN در بهترین حالت به صحت کلی حدود 20 درصد در هر دو محور دست یافتند درحالیکه روش MLFNN با صحت کلی حدود 90 درصد در هر دو محور نتایج بسیار بهتری ارائه نمود. سپس امکان پیش بینی شدت تصادفات یک محور با آموزش مدل بر اساس داده های محور دیگر، بررسی شد. این فرآیند صحت کلی 88 درصد را در حالت آموزش با محور اردکان-نائین و صحت کلی 78 درصد را در حالت معکوس به دست آورد. بخش آخر این پژوهش، تحلیل میزان تأثیر بهبود عوامل هندسی بر کاهش شدت تصادفات است. نتایج این تحلیل نشان داد که در محور اردکان- نائین، عامل شانه راه و در محور نائین- اردکان، کاربری محل بیشترین تأثیر را در کاهش شدت تصادفات دارند.
کلیدواژه‌ها
تصادفات جاده‌ای
شبکه عصبی مصنوعی عمیق
قطعه‌بندی
مناطق حادثه‌خیز
عوامل هندسی

موضوعات

عنوان مقاله English

Investigating the Effect of Geometrical Factors on the Severity of Car Crashes on Suburban Roads Utilizing Artificial Intelligence and Deep Learning Algorithms

نویسندگان English

Sarah Ghaffari 1
Farhad Hosseinali 2
1 M.Sc., Grad., Department of Surveying Engineering, Faculty of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran.
2 Associate Professor, Department of Surveying Engineering, Faculty of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran.
چکیده English

Roads, as a fundamental component of land transportation, face the challenge of road accidents. These accidents have irreparable financial and human impacts on individuals and society. Consequently, the importance of examining road safety and the factors contributing to accidents is evident. This study focused on the two routes of Ardakan-Naeen and Naeen-Ardakan, analyzing them based on six geometric and environmental parameters: horizontal curvature, sight distance, distance from intersections, distance from bridges, shoulder width, and land-use. Each route was segmented into homogeneous segments according to these six parameters and was classified based on the severity of geometric and environmental factors contributing to accidents. Subsequently, by incorporating accident points into both routes, the severity of accidents for each segment was calculated using the EPDO index and categorized into five levels. Three deep Neural Network methods, namely RNN, CNN, and MLFNN, were then employed to predict the severity of accidents for each segment based on the geometric and environmental parameters. The RNN and CNN methods achieved an overall accuracy of approximately 20 percent for both routes, while the MLFNN method demonstrated significantly better results with an overall accuracy of around 90 percent for both routes. Finally, the potential for predicting accident severity on one route by training the model with data from the other route was examined, resulting in an overall accuracy of 88 percent when trained with the Ardakan-Naeen route data and overall accuracy of 78 percent in a reversed state. The last section of this research focuses on analyzing the extent to which improvements in geometric factors contribute to the reduction of accident severity. The results of this analysis indicated that on the Ardakan-Naeen road, the shoulder of the road had the most significant impact, while on the Naeen-Ardakan road, land-use was the primary factor in reducing accident severity.

کلیدواژه‌ها English

Road Accidents
Deep Artificial Neural Network
Segmentation
Accident-Prone Areas
Geometrics Parameters
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