Identifying Black Spot: A GIS Approach to Road Safety and Accident Severity Patterns

  • Unique Paper ID: 182355
  • Volume: 12
  • Issue: 2
  • PageNo: 1855-1858
  • Abstract:
  • Road safety is a dynamic element of sustainable urban growth especially in rapidly expanding cities like Pune. This research aims to identify and examine accident-prone zones which are known as "black spots" along the Yerwada to Alandi road, which faces increasing traffic congestion and frequent accidents. Utilizing five years of detailed accident records from 2020 to 2024, the study employs Geographic Information System (GIS) technology for spatial analysis supplemented by Severity Index (SI) and Accident Severity Index (ASI) methods to identify and prioritize dangerous segments. The approach combines accident reports, traffic volume data and field case studies to design the spatial distribution of minor, major and fatal crashes. Key findings of the study specify that locations like Yerwada and Vishrantwadi consistently record high accident rates and severity which are mainly due to poor road design, missing pedestrian facilities and weak traffic control. Rear-end and head-on collisions are the most frequent types of accidents. Further, the study highlights the crucial role of merging GIS models with traffic engineering insights to effectively detect, visualize and address black spots. Also, it provides recommendations for targeted safety measures such as dedicated lanes, enhanced signage and stricter enforcement to reduce risks and improve road safety. This methodology offers a replicable framework for urban traffic safety planning in other large cities.

Copyright & License

Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@article{182355,
        author = {Ajay Sambhaji Nikam and Kailas Patil},
        title = {Identifying Black Spot: A GIS Approach to Road Safety and Accident Severity Patterns},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {2},
        pages = {1855-1858},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=182355},
        abstract = {Road safety is a dynamic element of sustainable urban growth especially in rapidly expanding cities like Pune. This research aims to identify and examine accident-prone zones which are known as "black spots" along the Yerwada to Alandi road, which faces increasing traffic congestion and frequent accidents. Utilizing five years of detailed accident records from 2020 to 2024, the study employs Geographic Information System (GIS) technology for spatial analysis supplemented by Severity Index (SI) and Accident Severity Index (ASI) methods to identify and prioritize dangerous segments.
The approach combines accident reports, traffic volume data and field case studies to design the spatial distribution of minor, major and fatal crashes. Key findings of the study specify that locations like Yerwada and Vishrantwadi consistently record high accident rates and severity which are mainly due to poor road design, missing pedestrian facilities and weak traffic control. Rear-end and head-on collisions are the most frequent types of accidents. Further, the study highlights the crucial role of merging GIS models with traffic engineering insights to effectively detect, visualize and address black spots. Also, it provides recommendations for targeted safety measures such as dedicated lanes, enhanced signage and stricter enforcement to reduce risks and improve road safety. This methodology offers a replicable framework for urban traffic safety planning in other large cities.},
        keywords = {Geographic Information Systems, Road Safety, Spatial Analysis, Accident Severity Index, Black Spot},
        month = {July},
        }

Cite This Article

  • ISSN: 2349-6002
  • Volume: 12
  • Issue: 2
  • PageNo: 1855-1858

Identifying Black Spot: A GIS Approach to Road Safety and Accident Severity Patterns

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