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A new model for determining the traffic accident black spots using GIS-aided spatial statistical methods

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  • Dereli, Mehmet Ali
  • Erdogan, Saffet

Abstract

Traffic accidents are one of the important problems in our country as it in the world. The World Health Organization case reports published in 2015 stated that approximately 1.25 million people died each year and more than 50 million people injured as a result of traffic accidents in the world. Considering this situation, it is seen that traffic accidents are mostly human originated and one of the major problems that is negatively affecting life. In this context, many investments and many studies have been performed on the determination of traffic accident black spots to reduce traffic accidents.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:transa:v:103:y:2017:i:c:p:106-117
    DOI: 10.1016/j.tra.2017.05.031
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    References listed on IDEAS

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    1. Amarasingha, Niranga & Dissanayake, Sunanda, 2013. "Modeling Frequency of Truck Crashes on Limited-Access Highways," Journal of the Transportation Research Forum, Transportation Research Forum, vol. 52(3).
    2. Jones, Andrew P. & Langford, Ian H. & Bentham, Graham, 1996. "The application of K-function analysis to the geographical distribution of road traffic accident outcomes in Norfolk, England," Social Science & Medicine, Elsevier, vol. 42(6), pages 879-885, March.
    3. Lord, Dominique & Mannering, Fred, 2010. "The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(5), pages 291-305, June.
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    2. Artur I. Petrov & Victor I. Kolesov & Daria A. Petrova, 2021. "Theory and Practice of Quantitative Assessment of System Harmonicity: Case of Road Safety in Russia before and during the COVID-19 Epidemic," Mathematics, MDPI, vol. 9(21), pages 1-33, November.
    3. Yingliu Yang & Lianghai Jin, 2022. "Visualizing Temporal and Spatial Distribution Characteristic of Traffic Accidents in China," Sustainability, MDPI, vol. 14(21), pages 1-16, October.
    4. 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.
    5. Tianzheng Xiao & Huapu Lu & Jianyu Wang & Katrina Wang, 2021. "Predicting and Interpreting Spatial Accidents through MDLSTM," IJERPH, MDPI, vol. 18(4), pages 1-18, February.
    6. Bakhtiar Feizizadeh & Davoud Omarzadeh & Ayyoob Sharifi & Abolfazl Rahmani & Tobia Lakes & Thomas Blaschke, 2022. "A GIS-Based Spatiotemporal Modelling of Urban Traffic Accidents in Tabriz City during the COVID-19 Pandemic," Sustainability, MDPI, vol. 14(12), pages 1-20, June.

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