[논문]Analysis of Traffic Accident using Association Rule Model

Ihm, Sun-Young; Park, Young-Ho

doi:10.9717/jmis.2018.5.2.111

Analysis of Traffic Accident using Association Rule Model 원문보기

The journal of multimedia information system, v.5 no.2, 2018년, pp.111 - 114

Ihm, Sun-Young (Big Data Using Research Center, Sookmyuung Women's University) , Park, Young-Ho (IT Engineering, Sookmyung Women's University)

Abstract ▼ AI-Helper

Traffic accident analysis is important to reduce the occurrence of the accidents. In this paper, we analyze the traffic accident with Apriori algorithm to find out an association rule of traffic accident in Korea. We first design the traffic accident analysis model, and then collect the traffic accidents data. We preprocessed the collected data and derived some new variables and attributes for analyzing. Next, we analyze based on statistical method and Apriori algorithm. The result shows that many large-scale accident has occurred by vans in daytime. Medium-scale accident has occurred more in day than nighttime, and by cars more than vans. Small-scale accident has occurred more in night time than day time, however, the numbers were similar. Also, car-human accident is more occurred than car-car accident in small-scale accident.

주제어

AI 본문요약
AI-Helper

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문제 정의

In this paper, we analyze the factors and association rules of traffic accident from 2015 to 2016 in Korea. The analysis shows that most of the accidents are caused by the driver’s failure to safely drive.
The aim of this study is to analyze the factors of traffic accidents in Korea. We use the traffic accident data provided by the government for the analysis, which are occurred in 2015 and 2016.

제안 방법

In this section, we analyze the data and describe the result. First, we performed statistical analysis based on the attributes of the preprocessed data. Figure 2 shows the result of analysis of traffic accident with type of accident, and the most accident occurred during the crossing.
In this paper, we find out the association rules based on various factors to analyze the causes of traffic accidents. We first collect the traffic accident data generated in Korea and preform the preprocessing phase for analysis.
[5] applied data mining algorithms such as Apriori algorithm, Naïve Bayses classifier and k-means clustering algorithm on the roadway traffic data. The authors performed the analysis, including weather, light conditions or surface condition. However, an analysis between more closely related attributes to the accident, such as the accident type or the road type is needed.
We use the traffic accident data provided by the government for the analysis, which are occurred in 2015 and 2016. The collected data includes 23 attributes, such as the date and time of the accident, type of accident, the number of deaths and injured people, etc., and the data is provided in csv format. Table 1 summarizes the main attributes of the collected data.

이론/모형

And then, we analyze the data to derive its meaning and discuss the results. Here, the analysis performs a basic statistical analysis considering various properties and we use the Apriori algorithm for the association analysis.
Next, we analyze the traffic accident by scale of accidents. In this paper, we used Apriori algorithm, which is an algorithm to find out association rules to analysis.

참고문헌 (10)

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상세보기
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상세보기
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상세보기
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상세보기
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