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Deep Learning-Based Black Spot Identification on Greek Road Networks

Author

Listed:
  • Ioannis Karamanlis

    (Department of Civil Engineering, University Campus at Kimmeria, Democritus University of Thrace, GR-67100 Xanthi, Greece)

  • Alexandros Kokkalis

    (Department of Civil Engineering, University Campus at Kimmeria, Democritus University of Thrace, GR-67100 Xanthi, Greece)

  • Vassilios Profillidis

    (Department of Civil Engineering, University Campus at Kimmeria, Democritus University of Thrace, GR-67100 Xanthi, Greece)

  • George Botzoris

    (Department of Civil Engineering, University Campus at Kimmeria, Democritus University of Thrace, GR-67100 Xanthi, Greece)

  • Chairi Kiourt

    (Athena Research Center, University Campus at Kimmeria, GR-67100 Xanthi, Greece)

  • Vasileios Sevetlidis

    (Athena Research Center, University Campus at Kimmeria, GR-67100 Xanthi, Greece)

  • George Pavlidis

    (Athena Research Center, University Campus at Kimmeria, GR-67100 Xanthi, Greece)

Abstract

Black spot identification, a spatiotemporal phenomenon, involves analysing the geographical location and time-based occurrence of road accidents. Typically, this analysis examines specific locations on road networks during set time periods to pinpoint areas with a higher concentration of accidents, known as black spots. By evaluating these problem areas, researchers can uncover the underlying causes and reasons for increased collision rates, such as road design, traffic volume, driver behaviour, weather, and infrastructure. However, challenges in identifying black spots include limited data availability, data quality, and assessing contributing factors. Additionally, evolving road design, infrastructure, and vehicle safety technology can affect black spot analysis and determination. This study focused on traffic accidents in Greek road networks to recognize black spots, utilizing data from police and government-issued car crash reports. The study produced a publicly available dataset called Black Spots of North Greece (BSNG) and a highly accurate identification method.

Suggested Citation

  • Ioannis Karamanlis & Alexandros Kokkalis & Vassilios Profillidis & George Botzoris & Chairi Kiourt & Vasileios Sevetlidis & George Pavlidis, 2023. "Deep Learning-Based Black Spot Identification on Greek Road Networks," Data, MDPI, vol. 8(6), pages 1-27, June.
    • Handle: RePEc:gam:jdataj:v:8:y:2023:i:6:p:110-:d:1172643
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    References listed on IDEAS

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    1. 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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    Keywords

    AI; black spot; road safety; dataset;
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