Pedestrian Behavior Model

Pedestrian behavior modeling aims to create accurate representations of how people move and make decisions in various environments, primarily to improve safety and efficiency in areas like autonomous vehicle navigation and crowd management. Current research emphasizes integrating contextual information (environmental factors, pedestrian attributes) into models, often using deep learning architectures like transformers and neural networks alongside established methods such as social force models and agent-based simulations. These advancements are crucial for developing safer and more efficient systems that interact with pedestrians, particularly in complex urban settings, and for understanding crowd dynamics in scenarios like disease transmission.