Monis Hafiz Abdul GhaffarHafiz Taha Jamil2026-02-012026-02-012017https://escholar.umt.edu.pk/handle/123456789/19466Crowd management in large events with hundreds of thousands of individuals is a critical challenge. Use of computer vision techniques on visual feed has been the typical approach throughout the years to monitor mobility and behaviors of crowds. Despite the substantial advancement in this field, the massive deployment cost of a camera network and limitations of image processing restricts the productivity of this approach when applied at large scale. On the other hand, the evolving trend of smart or context aware environments has led to the proposition of geographically informative systems in which advance tracking technologies are used for collection of quantitative movement data. Individuals within a crowd are observed as profiled users, thus, making it an identifiable crowd. GPS localization [1] and proximity-based [2] tracking has been used to capture complex crowd dynamics during an event. Following the above line of thought, our approach emphasizes on managing large scale events through multilevel contextual information and visualizations. Productive information on crowd condition is attained by performing real time analytics on positional data of individuals in a crowd. The nectar from raw data is extracted by harnessing the power of distributed processing platforms to process large amounts of data parallelly. The generated data is filtered and modeled to ensure the consistency of data coming at high volume and velocity. The other aspect of the project is to simulate crowd dynamics beforehand to predict probable outcomes. For realistic simulation of such events, the approach of Multi-Agent System (MAS) is considered suitable as the agents are expected to move to their goals, interact with their environment, and respond to each other. MAS is also postulated as preferred approach for emergency evacuation simulations [3] because MAS model problem in terms of autonomous interacting component-agents, which is proving to be a more natural way of simulating the unpredictability factor of large crowds. A generic simulator [4] is used for basic modeling of entities in a scenario. The simulator itself is broad in terms of information sources but limited in terms of even-driven activities and behaviors. We have extended and quantified the concept toward immensely crowded events and activities of individuals it comprehends. We advocate a seamless approach to model, simulate, analyze, and visualize crowd situations for actual venue settings. We equip crowd modelers and event organizers with a tool to effectively manage large events.en-USThesis