اولویت‌بندی عوامل زیرساختی مؤثر بر ایمنی راه‌های دوخطه با رویکردهای پیشگیرانه و واکنش‌گرا (مطالعه موردی: محور اهر-تبریز)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار،گروه مهندسی عمران، دانشگاه آیت الله العظمی بروجردی، بروجرد، ایران

2 گروه مهندسی عمران، دانشگاه صنعتی شاهرود، شاهرود، ایران

چکیده

شناسایی عوامل زیرساختی مؤثر در تصادفات راه‌های دوخطه برون­ شهری مبتنی بر دو رویکرد پیشگیرانه و واکنش­ گرا  از روش ­های ارتقاء ایمنی جاده­ای است. هدف پژوهش حاضر، شناسایی و اولویت ­بندی عوامل مؤثر بر ایمنی راه‌ برون­شهری دوخطه محور اهر- تبریز توسط سه مدل شبکه عصبی، تاپسیس و رگرسیون لجستیک است. از روش تحلیل سلسله مراتبی نیز برای تعیین اوزان معیارها و زیرمعیارهای مرتبط در روش تاپسیس استفاده می­ شود. سپس، این مطالعه به مقایسه نتایج اولویت­ بندی سه روش­ پیشنهادی بر اساس دو رویکرد مورد نظر می­ پردازد. نتایج حاصل از مدل شبکه عصبی نشان داد که این مدل با درستی پیش ­بینی 86 درصد متغیر­های زیرساختی به ­ترتیب وجود قوس­ های افقی و قائم، درصد خودرو­های سنگین، وضعیت روسازی، وضعیت زهکشی راه، حجم ترافیک عبوری و وضعیت دوربین­ کنترل سرعت را اولویت­ بندی می ­کند. در حالی ­که بر اساس روش تاپسیس، اولویت متغیر­های زیرساختی مؤثر بر ایمنی مسیر به ترتیب شامل وضعیت زهکشی، وضعیت روسازی، وضعیت دوربین­ ­کنترل سرعت، وجود قوس ­های افقی و قائم، وضعیت علائم مورد استفاده در راه، درصد خودروهای سنگین، وضعیت روشنایی راه، حجم ترافیک عبوری و وضعیت آرام‌سازی ترافیک می­ باشد. همچنین، نتایج تحلیل رگرسیون لجستیک نشان داد که این مدل با درستی 74/82 درصد توانایی اولویت ­بندی عوامل زیرساختی شامل متغیرهای وضعیت روسازی، وضعیت دوربین­ کنترل سرعت، وضعیت روشنایی راه، حجم ترافیک عبوری، و وضعیت علائم مورد استفاده در راه دارد که این متغیرها به­­ ترتیب اثرگذاری بیشتری بر اساس شانس بیشتر در مدل نسبت به دیگر متغیرهای زیرساختی در احتمال وقوع شدت تصادفات دارند. مقایسه عملکردی رویکردهای واکنش­گرا و پیش گیرانه با استفاده از آزمون­ های اسپیرمن و تی-تست نیز نشان داد که بین دو مدل شبکه عصبی و رگرسیون لجستیک همبستگی در عملکرد وجود دارد و دو مدل در رویکرد واگنش­ گرا تفاوت معناداری وجود ندارد. در حالی­ که بین این مدل ­ها و مدل تاپسیس تفاوت معناداری در عملکرد رویکرد وجود دارد. در نتیجه دو مدل عصبی و رگرسیون لجستیک در رویکرد واکنش ­گرا و مدل تاپسیس در رویکرد پیش گیرانه طبقه­ بندی می ­شوند و مدل شبکه عصبی به ­دلیل مقدار درستی پیش ­بینی قابل توجه در پیش ­بینی شدت تصادفات از قابلیت بهتری نسبت به سایر مدل­ ها در شناسایی و اولویت­ بندی عوامل زیرساختی کاربرد دارد.
 

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Prioritization of infrastructure factors affecting on the safety of two-lane roads using proactive and reactive methods (Case study: Ahar-Tabriz road)

نویسندگان [English]

  • Shahab Hassanpour 1
  • Farhad hadadi 2
1 Assistant Professor, Faculty of Civil Engineering, Ayatollah Boroujerdi University, Boroujerd, Iran
2 Faculty of Civil Engineering, Shahrood University of Technology, Shahrood, Iran
چکیده [English]

Identifying effective infrastructure factors that contributed to accidents of two-lane roads based on proactive and reactive methods is one of the ways to increase road safety. Therefore, the present study is firstly aimed to identify, and prioritize factors affecting accidents on rural road of Ahar-Tabriz using artificial neural network (ANN), TOPSIS and multinomial logistic regression (MNL) models. AHP was used for the weighting of criteria and subcriteria in the TOPSIS model. Secondly, this study compares the results of priorities in three models based on proactive and reactive methods. The results of the ANN model indicated that this model predicts accident severity via 86%, in which prioritized factors such as horizontal and vertical curves, percentage of heavy vehicles, pavement condition, road drainage condition, the volume of passing cars and use of speed control cameras, respectively. However, the TOPSIS model has shown that the priority of the infrastructure factors affecting road safety includes drainage condition, pavement condition, use of speed control cameras, horizontal and vertical curves, road signs, percent of heavy cars, road lighting condition, the volume of passing cars, and traffic calming, respectively. In addition, the MNL model indicated that this model is capable of prediction in accident severity via 74.82% in which pavement condition, use of speed control cameras, road lighting condition, the vehicle of passing cars, and road signs are ranked as the most effective factors involved with accidents on rural roads, respectively. Therefore, by making a performance comparison based on Spearman's rank correlation coefficient, T-test analysis, it is found that there is no difference between ANN and MNL models; however, there is a significant difference between TOPSIS and other models. Thus, ANN and MNL models are reactive methods, and the TOPSIS model is a proactive method in which the ANN model, due to higher accident severity prediction, is selected as the best predictive model. 

کلیدواژه‌ها [English]

  • Two-lane roads
  • Road safety
  • Proactive method
  • Reactive method
  • Prioritization models

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نشریه مهندسی عمران امیرکبیر
دوره 54، شماره 4
تیر 1401
صفحه 1419-1440

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