Clustering of Districts in Erzurum by Number of Injury

Home , Aziziye, Erzurum, Narman, Oltu, Olur, Pazaryolu

Journal of Traffic and Logistics Engineering Vol. 3, No. 2, December 2015

Hümeyra Bolakar Department of Civil Engineering, Engineering Faculty, Aksaray University, Aksaray, Turkey E-mail: [email protected]

Ahmet Tortum Department of Civil Engineering, Engineering Faculty, Ataturk University, Erzurum, Turkey E-mail: [email protected]

Ahmet Atalay Narman Vocational High School, Ataturk University, Erzurum, Turkey E-mail: [email protected]

Abstract—In this study, the number of injuries from road Clustering analysis is performed to determine the black traffic accidents for each district was identified in Erzurum, spots in traffic accident analysis in some studies [3], [6], Turkey during the years of 2012 and 2013. Clustering [8]. Moreover, clustering analysis is used to determine analysis was made according to these rates by using both similar districts or provinces in the literature [4]-[11]. classical k-means and fuzzy c-means technique. Districts In this study, clustering analysis was performed were divided into five clusters by analysis conducted with these two techniques. Districts with the highest injury risk according to the number of injuries from road traffic were determined, and the results obtained were compared. accidents (RTAs) occurred in Erzurum province for 2012 In this study, it was observed that the result of fuzzy c- and 2013. Clustering analysis was realized in two means technique is equal to the result of k-means technique. different forms. Firstly, traditional k-means method, and Moreover, it was determined that geographical information secondly fuzzy c-means method were applied. systems are advantageous to show and understand the Geographical Information System (GIS) software was  results of the thematic maps. used to demonstrate the results of the clustering analysis. Thematic maps of the districts were drawn by using GIS Index Terms—clustering, k-means, fuzzy c-means, road software. Erzurum province has eighteen districts. traffic accident, injury The aim of this study is to group similar districts of Erzurum according to the number of injury in road traffic accidents. It is to compare the results of traditional I. INTRODUCTION clustering and fuzzy clustering methods. Traffic accidents and deaths, injuries and material damage caused by these accidents still occupy a large II. METHOD place as one of the most important problems in the world. In Turkey every year more than 500.000 traffic accidents A. Cluster Analysis happen and of these accidents 5.000 end up in death and Cluster Analysis is the group of methods that help to 160.000 results in injuries. In 2012 and 2013, 9861 traffic divide the units, variables, or units and variables that take accidents happened in Erzurum and 87 people died in place in the X data matrix and of which natural groupings these accidents while 5755 people were injured [1], [2]. are not certainly known in terms of sub-clusters similar to In recent years, clustering analysis was carried out by each other. researchers on traffic accidents [3]-[11]. The purpose of In clustering analysis, we used both traditional k- such cluster analyses was to determine the districts means and fuzzy c-means methods. showing similarities with each other in the light of data on traffic accidents. Upon determining similar districts, B. K-Means Clustering Method each group may be analysed separately and the measures K-means technique, the most commonly used of the to be taken for traffic accidents may be easily determined. non-hierarchy methods, was found by MacQueen and it Diminish in death and material loss will be achieved by aims to collect elements with values closest to each other means of special measures taken in each district group in in the same cluster in cases when the number of the addition to general precautions taken to prevent traffic clusters is known [12], [13]. accidents. In this method, individuals are divided into k clusters to make the sum of squares within the groups the smallest.

According to the below stated formula individuals are Manuscript received February 1, 2015; revised April 12, 2015.

classified into the cluster giving the smallest distance (the However the cluster centres should change at the same closest) when a1n, a2n, .... , akn every group is selected as time according to the following weighted average cluster centre for individuals in the same space while formula in (6): each observation vector of x1, x2, x3, ...., xn variables with n p variables expresses a point in the multi-dimensional x- m (üik ) xk space in (1) [9], [13]-[15].  v  k1 (1 i  c) (6) n i n 1 2 W  min x  a (1) (üik ) N n  i in  i1 k1 For the data to be partitioned to clusters by this method, C. Fuzzy c-Means Clustering Method following procedures must be completed step by step. Fuzzy c-means algorithm is the best-known and widely Step 1: Dates are a date series or pattern series X= {x1, used method of fuzzy partitioning clustering techniques. x2, x3,…, xn},in general, c is identified, (2

means clustering, thematic map of districts was identified TABLE II. CENTRES OF CLUSTERS BY FUZZY C-MEANS in Fig. 1. CLUSTER NUMBER

1 2 3 4 5

Injuries 103,9394 1002 6,835842 16,4014 51,82501 during 2012 Injuries 116,9276 991 4,373043 26,87263 42,67219 during 2013

According to cluster centres in fuzzy c-means method, clusters were entitled in Fig. 2. According to the result of fuzzy c-means clustering, thematic map of districts was identified in Fig. 2. ArcGis program, the geographic information system software, was prepared and used to show the results in a visual way on the map of Erzurum (Fig. 1, Fig. 2). The demonstration of the results made it easier to understand the results. Maps were coloured according to cluster numbers considering the cluster naming. In this study, same results were obtained by both k- means and fuzzy c-means methods in Table III. This result is supported by the results of previous studies in the literature [4], [6], [9].

TABLE III. RESULTS OF CLUSTER ANALYSIS

Cluster Name Districts The highest The Centre of Erzurum/ Aziziye Figure 1. Clusters of districts by k-means method. More than medium Pasinler Medium Horasan, Oltu Less than medium Aşkale, Hınıs, İspir, Karaçoban The least Çat, Karayazı, Narman, Olur

In this study the highest injury was found in the Centre of Erzurum/Aziziye, Pasinler, Horasan and Oltu. These districts have high population and high traffic mobility in Erzurum province. On the other hand, the lowest injury was found in Çat, Karayazı, Narman, Olur, Pazaryolu, Şenkaya and Uzundere. These districts have low population and low traffic mobility. In the previous studies in the literature, it is pointed out that differences are seen in the in clusters obtained according to the two methods. This difference is due to the fact that fuzzy c-means technique was affected less by the initial values as compared to k-means technique. It was observed that fuzzy c-means technique usually produced more stable results. In addition, fuzzy c-means technique was observed to have been affected very much by exceptional data whereas k-means technique was influenced very little [9], [18].

IV. CONCLUSION The number of injuries is the highest in developed Figure 2. Clusters of districts by fuzzy c-means method. districts. These districts have high population, high car ownership and high traffic mobility. On the other hand, Matlab program was used for the fuzzy clustering undeveloped districts have the least injury numbers. analysis. Cluster centres obtained according to fuzzy c- These districts have low population, low car ownership means clustering method were obtained as shown in and low traffic mobility. Table II. The values of cluster centres in both methods The findings of this research can be used for the were approximately the same in Table III. investigation of different effects on traffic accident.

According to injury numbers, safety levels of districts can [15] E. Özgür, “Multivariate statistical analysis methods and an be determined. On the other hand, risk analysis can be application,” Ph.D. dissertation, Social Sciences Institue, Gazi University, Ankara, Turkey, 2003. made in detail in these districts and the priorities of [16] F. Höppner, F. Klawonn, R. Kruse, and T. Runkler, Fuzzy Cluster investment planning can be defined considering the level Analysis, Chichester, England: John Wiley&Sons Ltd., 2000. of safety risk. [17] Z. Şen, Fuzzy Logic with Engineering Principles of Modeling, Clustering analysis can be used in road safety studies. Istanbul, Turkey: Water Foundation Publications, 2004. [18] M. Işık and A. Y. Çamurcu, “K-means, K-meoids and Fuzzy c- Fuzzy clustering analysis approximately gives similar means algorithms verifying performance in practice,” Istanbul results with traditional clustering. Trade University Journal of Science, vol.11, no.1, pp. 31-45, 2007.

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