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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Improving Pedestrian Safety at Intersections Using Probabilistic Models and Monte Carlo Simulations


    Contributors:

    Conference:

    International Conference on Transportation and Development 2025 ; 2025 ; Glendale, Arizona



    Publication date :

    2025-06-05




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English





    Pedestrian Safety at Signalized Intersections

    Alluri, Priyanka / Gan, Albert / Haleem, Kirolos | ASCE | 2014



    Pedestrian safety evaluation of signalized intersections using surrogate safety measures

    Sankaran Marisamynathan / Perumal Vedagiri | DOAJ | 2020

    Free access