TRAFİK KAZA DESENLERİNİN TANIMLANMASINDA K-MEANS KÜMELEME ALGORİTMASININ KULLANILMASI: SAKARYA İLİ UYGULAMASI
THE USE OF K-MEANS CLUSTERING ALGORITHM FOR IDENTIFYING THE TRAFFIC ACCIDENT PATTERNS: CASE OF THE SAKARYA CITY

Summary/Abstract: The most common definition of traffic accidents is the fatality, injury or damage of one or more vehicles on the roads (Anderson, 2009). Traffic accidents cause loss of life and property both in the world and in our country.A total of 4577 fatal and injured traffic accidents occurred in the city center between 2006 and 2012 in Sakarya. A total of 6647 people were injured in these accidents and 62 people lost their lives (Sakarya Province Traffic Inspection Branch Directorate). However, it should be noted that this figure only covers citizens who have died at the site of the accident. This number is expected to increase when the survivors died in the hospital after the accident. In this study, which examines the traffic accidents occurred in the city of Sakarya between 2006 and 2012, the similarities between the traffic accidents will be investigated, and the main accident characteristics will be determined using these similarities. Therefore, it is aimed to identify similar accident groups and propose specific solutions to them.The main objective of the study is to investigate the similarity relations between traffic accidents occurring in the city and to provide a road map to the decision makers in the investments to be made to the developing city infrastructure based on this similarity relationship. In this study, it is planned to answer the following research questions:• How many different clusters can be collected by using the similarity of Euclidean distance between traffic accidents?• What is the weighted distribution of accidents under these clusters?• How can the clusters be classified on the basis of districts?In this study, K-means clustering method was used in the analysis of traffic accidents. Clustering algorithm is a statistical method commonly used in accident analysis. This is because accidents show many similarities and differences in terms of environmental characteristics, vehicle characteristics, types of accidents and driver characteristics. The grouping of these similarities is important to determine the accident characteristics and to prevent the recurrence of these accidents. As a matter of fact, it is seen that clustering method is used widely in the accident analyzes conducted both in the world and in our country. International studies such as Kim and Yamashita (2007), Anderson (2009), Bocarejo and Diaz (2011), Figuera et al. (2011) and Mauro et al. (2013) can be given as example. The studies of Karpat and Yılmaz (2002), Yılmaz and Erişoğlu (2003), Murat and Şekerler (2009), Atalay and Tortum (2010), Tortum vd. (2011) and Güner vd. (2014) are the examples of national studies.K-means clustering method brings the units together according to similar features, but also removes them according to their different characteristics. Clustering analysis is a statistical method for separating units into heterogeneous groups among themselves by using distance matrix or similarity matrix (Mauro et al. 2013). In this method, while the distance between the units in the same cluster is minimized, the distance between the clusters is maximized. Thus, the units included in the analysis are clustered according to their similar characteristics, while at the same time they are separated from the other units due to their different properties. Clustering algorithm is a statistical method which is commonly used in accident analysis.The research covers accidents that occurred between 2006 and 2012. The data set was obtained from Sakarya Police Department. Since Sakarya Police Department records only fatality or injured accidents, only property damaged accidents were excluded from the analysis. In addition, pedestrian accidents were excluded from the analysis and only vehicle accidents were taken into consideration. Thus, the data set includes 3333 fatality or injured accidents. When the dataset is examined, it was observed that the most of accidents occurred as side-by-side collision. Pedestrian accidents also occupy an important place in all accidents. Vehicle accidents are mostly included the car and heavy car accident. Number of accidents is similar in summer, spring and autumn, however it decreases in winter. Majority of these accidents took place under normal circumstances. According to this result, accidents are occurred in outdoors, during the daytime and in dry road surface. The majority of accidents occur between 12:00 and 20:00. Due to the high density of work and the end of working hours, the number of personnel services, automobiles and public transport vehicles that are on the road cause an increase in the number of accidents. In addition, municipal crews (such as cleaning work, road maintenance, water / sewerage works), which work with heavy maneuvering vehicles during this time of the day, cause traffic density and accidents.As a result of the clustering analysis, four different types of accidents were identified for Sakarya in the context of environmental characteristics, and these types of accidents are described in detail.· Cluster 1. Collisions involving two vehicles. In this cluster, accidents took place in autumn, on weekdays and before noon, on dry road surface and outdoors. Most of accidents in Sakarya is high relevance with Cluster 1.· Cluster 2. Single-vehicle accidents without junction. These represent the accident that is on wet road surface, a cloudy day in autumn, on weekdays and midnight. The cluster is unique for mapping of single-vehicle accident.· Cluster 3. Two-vehicle accidents with junctiona. The features of accident in Cluster 3 are on dry road surface, outdoors, in summer, at weekends and afternoon. The traffic density in Cluster 3 is more than other type of Clusters.· Cluster 4. Two-vehicle accidents with junctionb. In Cluster 4, the accidents are occurred dry road surface, outdoors, in spring, on weekdays and afternoon.The percentages of Clusters are 46%, 10%, 27% and %17, respectively. The accident density of Cluster 1 is high when compared with the others. The most attractive features of Cluster 1 are on weekdays and before noon and Cluster 3 that has second density of accident of all is on junction, in summer, at weekend and afternoon.Hence, the results of the research revealed the basic characteristics of the traffic accidents that took place in Sakarya. The accidents in Adapazarı, Erenler, Serdivan ve Arifiye that is sub-province match up with the clusters to determine the characteristics of accident. Sub-provinces correspond with Clusters such as Adapazarı-Cluster 1, Serdivan-Cluster 3, Erenler-Cluster 4 and Arifiye- Cluster 2. Thus, it is expected that this research will help decision makers to prevent and reduce traffic accidents.

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