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<p>The distribution of accidental road traffic crashes (RTCs) in India varies geographically, but there is limited district-level evidence on the relationship between crash exposure and fatality severity. This study constructs a dual-indicator spatiotemporal framework to examine RTC dynamics at the district level in Haryana over a decade (2013–14 to 2022–23). Data on crashes, fatalities, and injuries reported by police were obtained from the National and State Crime Records Bureaus. The Average Annual Growth Rate method was employed to estimate district populations, enabling population-normalized risk assessment when traffic exposure data were unavailable. Administrative boundary harmonization ensured consistency over time after the establishment of Charkhi Dadri district in 2016. Accident Risk crashes per 100,000 population, and Severity Index fatalities per 100 crashes were calculated annually as complementary measures of exposure and lethality. Standardized maps of districts were analyzed using Geographic Information System technology to identify persistent spatial regimes, inter-annual variability, and extreme severity behavior, which was further examined through violin plot distributions. The analysis revealed a consistent east–west gap in road safety performance. Crash exposure was higher in eastern districts along large urban industrial belts, while extremely high lethality was observed in western and central regions. Additionally, SI continued to rise, reaching peak values over 60 and exceeding 100 in individual years. The mean AR dropped significantly (22) during the COVID-19 period, while SI increased in various districts, indicating a pattern of high-speed, low-volume fatalities. This decade-long, district-level GIS-based study of RTCs in Haryana integrates AR-SI coupling, administrative reorganization, and distributional analysis. The framework identifies long-term, high-risk and high-severity spatial regimes that are often hidden in aggregate studies. It provides a transferable methodology template for district-specific road safety interventions under data-limited conditions.</p> Graphical abstract <p></p>

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District-scale road safety assessment under data constraints: a GIS-based AR/SI framework from Haryana, India

  • 期刊论文

  • 发表日期：

    2026年4月30日

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  • Ajay Dheekwal,
  • Kanwarpreet Singh,
  • Abhishek Sharma,
  • Sahil Sharma

摘要

The distribution of accidental road traffic crashes (RTCs) in India varies geographically, but there is limited district-level evidence on the relationship between crash exposure and fatality severity. This study constructs a dual-indicator spatiotemporal framework to examine RTC dynamics at the district level in Haryana over a decade (2013–14 to 2022–23). Data on crashes, fatalities, and injuries reported by police were obtained from the National and State Crime Records Bureaus. The Average Annual Growth Rate method was employed to estimate district populations, enabling population-normalized risk assessment when traffic exposure data were unavailable. Administrative boundary harmonization ensured consistency over time after the establishment of Charkhi Dadri district in 2016. Accident Risk crashes per 100,000 population, and Severity Index fatalities per 100 crashes were calculated annually as complementary measures of exposure and lethality. Standardized maps of districts were analyzed using Geographic Information System technology to identify persistent spatial regimes, inter-annual variability, and extreme severity behavior, which was further examined through violin plot distributions. The analysis revealed a consistent east–west gap in road safety performance. Crash exposure was higher in eastern districts along large urban industrial belts, while extremely high lethality was observed in western and central regions. Additionally, SI continued to rise, reaching peak values over 60 and exceeding 100 in individual years. The mean AR dropped significantly (22) during the COVID-19 period, while SI increased in various districts, indicating a pattern of high-speed, low-volume fatalities. This decade-long, district-level GIS-based study of RTCs in Haryana integrates AR-SI coupling, administrative reorganization, and distributional analysis. The framework identifies long-term, high-risk and high-severity spatial regimes that are often hidden in aggregate studies. It provides a transferable methodology template for district-specific road safety interventions under data-limited conditions.

Graphical abstract