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Thesis
Modeling collision detection and avoidance in pedestrian dynamics
University of West Florida Libraries
Master of Science (MS), University of West Florida
2024
Abstract
Pedestrian dynamics models simulate how individuals move in crowded environments. It is used in the planning of pedestrian spaces, mitigation of disease spread, and in ensuring safety in crowds. The social force model is an approach to simulate pedestrian dynamics by modeling the movement of individuals as if they were subject to social forces. Social forces represent the internal motivations driving individuals to take specific actions. However, these simulations can exhibit unrealistic deadlocks, where simulated pedestrians get stuck due to unintended interactions with other pedestrians. Current social force models do not have adequate generalized resolution algorithms in place to resolve such deadlocks. This thesis proposes a race detection and collision resolution algorithm to solve this, and in turn models stopping as a collision avoidance strategy. Swerving is another collision avoidance strategy, where pedestrians change speed and direction to avoid an obstacle or pedestrian. The thesis further explores modeling of swerving as a collision avoidance maneuver. Real time crowd control can be accomplished using a digital twin, for which dynamic prediction of crowd behavior is desirable. This thesis examines dynamic collision avoidance modeling for use in a digital twin. This thesis aims to advance pedestrian dynamics modeling by proposing these novel models that are crucial for enhancing safety and efficiency in crowded environments.
Details
- Title
- Modeling collision detection and avoidance in pedestrian dynamics
- Resource Type
- Thesis
- Contributors
- Ashok Srinivasan (Committee Chair)Sirish Namilae (Committee Member)Andrew Arash Mahyari (Committee Member)
- Publisher
- University of West Florida Libraries
- Format
- pdf
- Number of pages
- 57
- Copyright
- Permission granted to the University of West Florida Libraries by the author to digitize and/or display this information for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires the permission of the copyright holder.
- Identifiers
- 99380569896806600
- Academic Unit
- Computer Science
- Language
- English
- Awarding Institution
- University of West Florida; Master of Science (MS)
- Theses and Dissertations
- Master of Science (MS), University of West Florida