2019Unpublished venueRequires access

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

Kyi Pyar Hlaing, Nyein Thwet Thwet Aung, Swe Zin Hlaing, Koichiro Ochimizu

Open publisher page 5 citations

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.

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What this paper is about

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.

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OpenAlex reports 5 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available 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.

Key concepts: Accident (philosophy), Road accident, Accident analysis, Bayes' theorem, Computer science, Independence (probability theory), Traffic accident, Accident investigation

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