Analysis of accident severity factor in Road Accident of Yangon using FRAM and Classification Technique

2019 International Conference on Advanced Information Technologies (ICAIT) Pub Date : 2019-11-01 DOI:10.1109/AITC.2019.8921119

Kyi Pyar Hlaing, Nyein Thwet Thwet Aung, Swe Zin Hlaing, K. Ochimizu

{"title":"Analysis of accident severity factor in Road Accident of Yangon using FRAM and Classification Technique","authors":"Kyi Pyar Hlaing, Nyein Thwet Thwet Aung, Swe Zin Hlaing, K. Ochimizu","doi":"10.1109/AITC.2019.8921119","DOIUrl":null,"url":null,"abstract":"Road accidents are unpredictable and undetermined occurrence. Analysis of road accidents needs to understand the factor causing road accident severity. Careful analysis of road accident record is important to find out leading indicator factor for road accident. This paper introduces the analysis of severity factor using Functional Resonance Analysis Method (FRAM) that can be used an accident analysis method providing a new concept for people to analyze accidents. It also applies Naïve Bayes (NB) Algorithm is one of the classification techniques and based on probability models that incorporate strong independence assumptions. In this paper, firstly, FRAM shows the model of analysis of road accident. Secondly NB algorithm applies to calculate the probability of severity level attribute. Finally, this paper shows some experiment of the real dataset of road accident in Yangon by applying the actual scenario. The result shows that the performance variability from the function of the model such as accident time, causes of accident reason and type of vehicle are important factor to lead the level of road accident severity.","PeriodicalId":388642,"journal":{"name":"2019 International Conference on Advanced Information Technologies (ICAIT)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 International Conference on Advanced Information Technologies (ICAIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AITC.2019.8921119","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 2

Abstract

Road accidents are unpredictable and undetermined occurrence. Analysis of road accidents needs to understand the factor causing road accident severity. Careful analysis of road accident record is important to find out leading indicator factor for road accident. This paper introduces the analysis of severity factor using Functional Resonance Analysis Method (FRAM) that can be used an accident analysis method providing a new concept for people to analyze accidents. It also applies Naïve Bayes (NB) Algorithm is one of the classification techniques and based on probability models that incorporate strong independence assumptions. In this paper, firstly, FRAM shows the model of analysis of road accident. Secondly NB algorithm applies to calculate the probability of severity level attribute. Finally, this paper shows some experiment of the real dataset of road accident in Yangon by applying the actual scenario. The result shows that the performance variability from the function of the model such as accident time, causes of accident reason and type of vehicle are important factor to lead the level of road accident severity.

查看原文本刊更多论文

基于FRAM和分类技术的仰光道路交通事故严重程度因素分析

道路交通事故是不可预测和不确定的事件。分析道路交通事故需要了解造成道路交通事故严重程度的因素。仔细分析道路交通事故记录，对于找出道路交通事故的主导指标因素至关重要。本文介绍了用功能共振分析法(FRAM)分析严重程度因子的方法，该方法可作为事故分析方法，为人们分析事故提供了一种新的思路。Naïve贝叶斯(NB)算法是一种分类技术，基于包含强独立性假设的概率模型。本文首先采用FRAM模型对道路交通事故进行分析。其次，应用NB算法计算严重性等级属性的概率。最后，本文通过实际场景对仰光市道路交通事故真实数据集进行了一些实验。结果表明，事故时间、事故原因和车辆类型等模型函数的性能变异性是影响道路事故严重程度高低的重要因素。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

2019 International Conference on Advanced Information Technologies (ICAIT)

自引率

0.00%

发文量