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Exploring the Impact of Pedestrian Behavior Parameters on Gap Acceptance in Microsimulation Applications

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As urban environments become increasingly congested, understanding pedestrian behaviors at intersections is essential for ensuring safety and efficiency. This study explores the complexities of pedestrian behavior in urban traffic networks, focusing on sensitivity analysis using a microscopic simulation tool and a pedestrian module based on the social force model (SFM). By examining nine key pedestrian behavior parameters in isolation, this research identifies their impact on gap acceptance behavior. This exploratory approach highlights how individual parameters, such as Lambda—indicative of a pedestrian’s responsiveness to stimuli from behind—affect the variability in and distribution of gap acceptance times. The findings provide valuable insights into the interplay between pedestrian behavior parameters and their influence on decision-making processes. These results serve as a foundation for refining pedestrian behavior models, offering practical guidance for urban planners, traffic engineers, and policymakers. By emphasizing sensitivity analysis, this study demonstrates the utility of microscopic simulation models in achieving a more profound, nuanced understanding of pedestrian dynamics, contributing to the development of safer and more efficient urban environments.
Title: Exploring the Impact of Pedestrian Behavior Parameters on Gap Acceptance in Microsimulation Applications
Description:
As urban environments become increasingly congested, understanding pedestrian behaviors at intersections is essential for ensuring safety and efficiency.
This study explores the complexities of pedestrian behavior in urban traffic networks, focusing on sensitivity analysis using a microscopic simulation tool and a pedestrian module based on the social force model (SFM).
By examining nine key pedestrian behavior parameters in isolation, this research identifies their impact on gap acceptance behavior.
This exploratory approach highlights how individual parameters, such as Lambda—indicative of a pedestrian’s responsiveness to stimuli from behind—affect the variability in and distribution of gap acceptance times.
The findings provide valuable insights into the interplay between pedestrian behavior parameters and their influence on decision-making processes.
These results serve as a foundation for refining pedestrian behavior models, offering practical guidance for urban planners, traffic engineers, and policymakers.
By emphasizing sensitivity analysis, this study demonstrates the utility of microscopic simulation models in achieving a more profound, nuanced understanding of pedestrian dynamics, contributing to the development of safer and more efficient urban environments.

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