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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.21930 |
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| _version_ | 1866915361375387648 |
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| author | Liashkov, Stanislav |
| author_facet | Liashkov, Stanislav |
| contents | Despite a global decline in motor vehicle crash fatalities due to improved research and road safety policies, road traffic injuries remain a significant public health concern. The World Health Organization 2023 report highlights that road traffic injuries are the leading cause of death among individuals aged 5-29, with over half of fatalities involving pedestrians, cyclists, and motorcyclists. This study addresses this critical issue by identifying high-risk areas in Montgomery County, Maryland, contributing to the global goal of halving road traffic deaths and injuries by 2030. Using Kernel Density Estimation (KDE) and spatial autocorrelation analysis, we estimate collision densities and identify hotspots for targeted interventions. Our findings reveal significant spatial clustering of traffic collisions, with distinct patterns in densely populated urban areas and rural regions, offering valuable insights for policymakers to enhance road safety. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_21930 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Identifying High-Risk Areas for Traffic Collisions in Montgomery, Maryland Using KDE and Spatial Autocorrelation Analysis Liashkov, Stanislav Applications Despite a global decline in motor vehicle crash fatalities due to improved research and road safety policies, road traffic injuries remain a significant public health concern. The World Health Organization 2023 report highlights that road traffic injuries are the leading cause of death among individuals aged 5-29, with over half of fatalities involving pedestrians, cyclists, and motorcyclists. This study addresses this critical issue by identifying high-risk areas in Montgomery County, Maryland, contributing to the global goal of halving road traffic deaths and injuries by 2030. Using Kernel Density Estimation (KDE) and spatial autocorrelation analysis, we estimate collision densities and identify hotspots for targeted interventions. Our findings reveal significant spatial clustering of traffic collisions, with distinct patterns in densely populated urban areas and rural regions, offering valuable insights for policymakers to enhance road safety. |
| title | Identifying High-Risk Areas for Traffic Collisions in Montgomery, Maryland Using KDE and Spatial Autocorrelation Analysis |
| topic | Applications |
| url | https://arxiv.org/abs/2506.21930 |