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The motor vehicle buying population of our country is increasing every day, and this increase in the number of vehicles leads to more accidents. These accidents are due to human error, faulty vehicles, and errors in the geometric design of roads and unsuitable weather conditions. Road accidents cannot be prevented, but by using suitable traffic engineering safety plans and management methods, these accident rates and severity of injury can be reduced to a greater extent. The locations on the stretch where the frequency of road accidents is higher and these crashes may lead to serious injuries or deaths are defined as black spot. The identification of these black spot locations is the first step in road safety measures. The present study identifies the black spots along the selected road stretch which is from Kalamboli Circle to Juinagar Station counting to 13 km stretch in Navi Mumbai city. The Google Earth is used to locate these black spots. For further study, Accidental Severity Index method and Statistical Package for Social Sciences software are used. For this study, the road accident data for the past four years that are 2020–2023 of selected road stretches are collected from CIDCO Office Belapur, Navi Mumbai. Road safety analysis is carried out in the identified black spots such as MGM hospital, Kharghar Hiranandani, CBD Belapur, Nerul, and Juinagar Railway Station. The results of the analysis indicate identified black spots in the selected study stretch are in severe condition and some remedial measures are made to reduce future accidents. Over the period from 2020 to 2023, there were 73 fatal accidents, 234 non-fatal accidents and 307 total accidents. In the year 2020, there were five deaths, and year 2023 showed a rise to 27 deaths. The age group 18–30 years consistently appears to be the most prevalent causing accidents followed by age group of 31–45 years. The data show that four-wheelers constitute the highest proportion of accidents at 28.01% followed by rickshaws at 26.05% and two-wheelers at 22.14%, respectively.

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Identification and Analysis of Black Spots Along the Selected Road Stretches of Navi Mumbai City

  • 会议论文

  • 发表日期：

    2025年

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  • Sagar K. Sonawane,
  • Sarvesh Thakur,
  • Rajshekhar G. Rathod

摘要

The motor vehicle buying population of our country is increasing every day, and this increase in the number of vehicles leads to more accidents. These accidents are due to human error, faulty vehicles, and errors in the geometric design of roads and unsuitable weather conditions. Road accidents cannot be prevented, but by using suitable traffic engineering safety plans and management methods, these accident rates and severity of injury can be reduced to a greater extent. The locations on the stretch where the frequency of road accidents is higher and these crashes may lead to serious injuries or deaths are defined as black spot. The identification of these black spot locations is the first step in road safety measures. The present study identifies the black spots along the selected road stretch which is from Kalamboli Circle to Juinagar Station counting to 13 km stretch in Navi Mumbai city. The Google Earth is used to locate these black spots. For further study, Accidental Severity Index method and Statistical Package for Social Sciences software are used. For this study, the road accident data for the past four years that are 2020–2023 of selected road stretches are collected from CIDCO Office Belapur, Navi Mumbai. Road safety analysis is carried out in the identified black spots such as MGM hospital, Kharghar Hiranandani, CBD Belapur, Nerul, and Juinagar Railway Station. The results of the analysis indicate identified black spots in the selected study stretch are in severe condition and some remedial measures are made to reduce future accidents. Over the period from 2020 to 2023, there were 73 fatal accidents, 234 non-fatal accidents and 307 total accidents. In the year 2020, there were five deaths, and year 2023 showed a rise to 27 deaths. The age group 18–30 years consistently appears to be the most prevalent causing accidents followed by age group of 31–45 years. The data show that four-wheelers constitute the highest proportion of accidents at 28.01% followed by rickshaws at 26.05% and two-wheelers at 22.14%, respectively.