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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.07805 |
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| _version_ | 1866916648582119424 |
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| author | Bagabaldo, Alben Rome Hackl, Jürgen |
| author_facet | Bagabaldo, Alben Rome Hackl, Jürgen |
| contents | National Highway Traffic Safety Administration reported 7,345 pedestrian fatalities in the United States in 2022, making pedestrian safety a pressing issue in urban mobility. This study presents a novel probabilistic simulation framework integrating dynamic pedestrian crossing models and Monte Carlo simulations to evaluate safety under varying traffic conditions. The framework captures key influences on pedestrian decisions, such as traffic light states, vehicle proximity, and waiting times, while employing the Intelligent Driver Model (IDM) to simulate realistic vehicle dynamics. Results from 500 trials show that pedestrians avoid crossing during green lights, reducing collision risks, while shorter waiting times during red lights encourage safer crossings. The risk is heightened during yellow lights, especially with nearby vehicles. This research emphasizes the importance of adaptive traffic control measures, such as pedestrian-triggered signals and enhanced traffic light timing, to mitigate risks and prioritize pedestrian safety. By modeling realistic interactions between pedestrians and vehicles, the study offers insights for designing safer and more sustainable urban intersections. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_07805 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Improving Pedestrian Safety at Intersections Using Probabilistic Models and Monte Carlo Simulations Bagabaldo, Alben Rome Hackl, Jürgen Applications National Highway Traffic Safety Administration reported 7,345 pedestrian fatalities in the United States in 2022, making pedestrian safety a pressing issue in urban mobility. This study presents a novel probabilistic simulation framework integrating dynamic pedestrian crossing models and Monte Carlo simulations to evaluate safety under varying traffic conditions. The framework captures key influences on pedestrian decisions, such as traffic light states, vehicle proximity, and waiting times, while employing the Intelligent Driver Model (IDM) to simulate realistic vehicle dynamics. Results from 500 trials show that pedestrians avoid crossing during green lights, reducing collision risks, while shorter waiting times during red lights encourage safer crossings. The risk is heightened during yellow lights, especially with nearby vehicles. This research emphasizes the importance of adaptive traffic control measures, such as pedestrian-triggered signals and enhanced traffic light timing, to mitigate risks and prioritize pedestrian safety. By modeling realistic interactions between pedestrians and vehicles, the study offers insights for designing safer and more sustainable urban intersections. |
| title | Improving Pedestrian Safety at Intersections Using Probabilistic Models and Monte Carlo Simulations |
| topic | Applications |
| url | https://arxiv.org/abs/2503.07805 |