Traffic accidents, which continue to increase from year to year in Turkey and in the world, have become a huge problem that can result in serious traumas, injuries, and deaths, as well as their material and moral consequences. Many studies have been carried out in the world and in Turkey to reduce the number of traffic accidents, but these studies have not been very effective in reducing accidents. In this study, 3105 fatal or injured traffic accidents between 2010–2017 in Sarıyer district of Istanbul, Turkey’s largest city in terms of population, were discussed. We analyzed the statistical information on the subject in detail within the framework of geographic information systems. It has been tried to determine the sections where traffic accidents are concentrated in this region with studies based on spatial methods. Thematic accident map was created according to the accident types. In this context, the advantages and disadvantages of these methods were compared using Point Density, Kernel Density, Getis Ord Gi*, and Anselin Local Moran’s I (LISA) Spatial Autocorrelation. In addition, in order to observe the change in accidents, thematic accident and Kernel Density maps were created separately according to accident occurrence types in the beginning and last year. From this point of view, the changes that occurred in the accidents were interpreted. The current study determined that the most accidents were on some streets and these streets divided into regions in a plan. The cases were examined with statistical analyses according to accident types and using the Kernel Density method. In addition, it has been observed that Kernel Density method gives better visual results than other spatial methods. In this study, spatial analysis and statistical analysis methods were used to evaluate traffic accidents more realistically. The day of the week effect and month of the year effect on traffic accidents was investigated for the first time. In addition, it is proposed to bring a new approach to the prevention of traffic accidents by using hotspot, accident type, and day of the week effect.

Studies to reduce traffic accidents are of great importance, especially for metropolitan cities. One of these metropolitan cities is undoubtedly Istanbul. In this study, a perspective on reducing traffic accidents was trying to be revealed by analyzing 3833 fatal and injury traffic accidents that occurred in the Şişli district of Istanbul between 2010-2017, with Data Mining (DM), Machine Learning (ML) and Geographic Information Systems methods (GIS), as well as traditional methods. It is aimed to visually determine the streets where traffic accidents are concentrated, to examine whether the accidents show anomalies according to the effect of the days of the week, to examine the differences according to the accidents that occur in the regions and to develop a model. For this purpose Kernel Density, decision trees, artificial neural networks, logistic regression and Naive Bayes methods were used. From the results obtained, it has been seen that some days are different from other days in terms of traffic accidents, according to the accident intensities and the performances of the modelling techniques used vary according to the regions. This study revealed that the ‘day of the week effect’ can also be applied to traffic accidents

Traffic accidents occurring are one of the most important issues that cause loss of life and property of the people. With the increasing population, the number of vehicles increasing in use creates traffic density. For this reason, studies aimed at reducing traffic accidents are of vital importance. In this study, a total of 3833 fatal and injured traffic accidents that occurred between 2010-2017 in Şişli district were analyzed with the help of geographical information systems and Kernel density method. In this study, various maps were created according to the accident type, time zone and the type of vehicles that had the most accident, and the locations of the accidents were examined. It is aimed to help reduce the number of possible accidents by taking necessary precautions in locations that are determined to be risky according to the accident intensities obtained. It has been observed that the accidents intensify differently according to the changing time zones, especially on the streets. In the study, it is also aimed to help the units that make traffic planning by making separator maps of the types of vehicles that have the most accidents on these streets, according to the accident types, days of the week and time zones.

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