Data Mining as a Method for Comparison of Traffic Accidents in Şişli District of Istanbul
DOI:
https://doi.org/10.25034/ijcua.2022.v6n2-2Keywords:
Geographic Information Systems, Kernel Density Method, Traffic Accidents, Decision Trees, Artificial Neural Networks, Logistic Regression, Naive BayesAbstract
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
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Copyright (c) 2022 Ph.D. Candidate Mert Ersen, Professor Dr. Ali Hakan Büyüklü, Professor Dr. Semra Erpolat Taşabat
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