تعیین تابع آموزش مناسب مدل شبکه عصبی به منظور ارتقاء ایمنی تردد جاده‌ای

نویسندگان

1 دانش آموخته کارشناسی ارشد، دانشکده مهندسی عمران، دانشگاه یزد، یزد، ایران

2 استادیار، دانشکده مهندسی عمران، دانشگاه یزد، یزد، ایران

3 دانش آموخته کارشناسی ارشد، دانشکده مهندسی عمران، دانشگاه پیام‌نور رضوانشهر، یزد، ایران

4 دانش آموخته کارشناسی ارشد، دانشکده مهندسی عمران، دانشگاه آزاد اسلامی واحد بافق، یزد، ایران

چکیده

­سالانه تعداد زیادی از مردم دنیا در اثر تصادفات جاده­ای جان و مال خود را از دست می­دهند. یکی از روش­های مناسب به منظور کاهش تصادفات، پیش­بینی وقوع تصادفات قبل از رخداد آن­ها می­باشد. در این مقاله به‌طور موردی تصادفات محور نائین-اردکان استان یزد با بهره­گیری از مدل شبکه عصبی مورد ارزیابی قرار گرفت. تاکنون در هیچ مطالعه‌ای به بررسی تاثیر توابع مختلف آموزش مدل شبکه عصبی در دقت نتایج پیش­بینی پرداخته نشده است. هدف این مقاله تعیین تابع آموزش دقیق­تر شبکه عصبی به منظور پیش­بینی تعداد تصادفات محور موردبررسی بود. در این راستا تعداد 4 تابع مختلف ارزیابی گردید. بررسی­های این مقاله حاکی از برتری نسبی مدل شبکه عصبی با تابع آموزش از نوع  trainlmبود. همچنین نتایج نشان داد که عوامل میزان تردد در هر خط و عدم رعایت فاصله ایمن به ترتیب بیشترین تأثیر را در وقوع تصادفات محور موردمطالعه داشتند. کاربرد نتایج تحقیق در بیان دقیق­تر اثر متغیرهای مستقل در وقوع تصادفات است. به بیان دقیق­تر تاثیرگذاری متغیرهای مستقل می­تواند به کارشناسان ایمنی جهت اعمال بهینه­تر سناریو­های کاهش تصادفات کمک کند.

کلیدواژه‌ها

عنوان مقاله [English]

Determining the Proper Training Algorithm of Artificial Neural Network Prediction Model as a Tool for Road Safety Promotion

نویسندگان [English]

  • Abolfazl Khishdari 1
  • Hamed Khani Sanij 2
  • Javad Zaker Harofteh 3
  • Mohsen Dehghan Banadaki 4
1 M.Sc., Grad., Department of Civil Engineering, Yazd University, Yazd, Iran.
2 Assistant Professor, Department of Civil Engineering, Yazd University, Yazd, Iran.
3 M. Sc., Grad., Department of Civil Engineering, Payam Noor Rezvanshahr University, Yazd, Iran.
4 M. Sc., Grad., Department of Civil Engineering, Bafgh Islamic Azad University, Yazd, Iran.
چکیده [English]

Numerous people have died and economically damaged due to the road accidents. One of the efficient ways of reducing crashes is to predict them before happening. This paper investigated the power of artificial-neural network (ANN) model to predict crash frequencies of Naein-Ardakan road, located in Yazd, Iran. To date, there seems no research done to compare the effects of ANN training functions on prediction performance. This research aimed to determine the proper ANN training algorithm for crash frequency prediction. In this regard, four different training algorithms were investigated. The results demonstrated the outperformance of ‘trainlm’ algorithm. Additionally, it was found that the average daily traffic per lane and gap lengths is the most influential factors in crash occurrences, respectively. The present study can be applied to more precisely explain the effects of independent variables on crash outcomes. An in-depth explanation of the effectiveness of independent variables can assist road safety experts in making better decisions for reducing accidents.

کلیدواژه‌ها [English]

  • Neural network
  • Training Function
  • Prediction
  • Crash Frequencies

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