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Analysis of Fatal and Injury Traffic Accidents in Istanbul Sarıyer District with Spatial Statistics Methods

Author

Listed:
  • Mert Ersen

    (Graduate School of Science and Engineering, Yıldız Technical University, Davutpaşa Campus, 34220 Istanbul, Turkey)

  • Ali Hakan Büyüklü

    (Department of Statistics, Faculty of Arts & Science, Yıldız Technical University, Davutpaşa Campus, 34220 Istanbul, Turkey)

  • Semra Taşabat Erpolat

    (Department of Statistics, Faculty of Arts & Science, Mimar Sinan Fine Arts University, Bomonti Campus, 34360 Istanbul, Turkey)

Abstract

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.

Suggested Citation

  • Mert Ersen & Ali Hakan Büyüklü & Semra Taşabat Erpolat, 2021. "Analysis of Fatal and Injury Traffic Accidents in Istanbul Sarıyer District with Spatial Statistics Methods," Sustainability, MDPI, vol. 13(19), pages 1-39, October.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:19:p:11039-:d:650410
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    References listed on IDEAS

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    1. Baur, Dirk G. & Cahill, Daniel & Godfrey, Keith & (Frank) Liu, Zhangxin, 2019. "Bitcoin time-of-day, day-of-week and month-of-year effects in returns and trading volume," Finance Research Letters, Elsevier, vol. 31(C), pages 78-92.
    2. Aharon, David Yechiam & Qadan, Mahmoud, 2019. "Bitcoin and the day-of-the-week effect," Finance Research Letters, Elsevier, vol. 31(C).
    3. Dereli, Mehmet Ali & Erdogan, Saffet, 2017. "A new model for determining the traffic accident black spots using GIS-aided spatial statistical methods," Transportation Research Part A: Policy and Practice, Elsevier, vol. 103(C), pages 106-117.
    4. Xie, Zhixiao & Yan, Jun, 2013. "Detecting traffic accident clusters with network kernel density estimation and local spatial statistics: an integrated approach," Journal of Transport Geography, Elsevier, vol. 31(C), pages 64-71.
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