Activity Based

Activity-based modeling focuses on simulating individual behaviors and their aggregate effects, offering a powerful tool across diverse fields. Current research emphasizes improving model calibration and accuracy, employing techniques like Bayesian optimization and neural networks to handle complex parameter spaces and uncertainty quantification, particularly in large-scale applications such as transportation and drug discovery. These advancements enhance the reliability and predictive power of activity-based models, leading to more informed decision-making in areas ranging from urban planning and traffic management to therapeutic agent development. The ultimate goal is to create more realistic and useful simulations for understanding and predicting complex systems.