In an increasingly populated world the importance of building architecture that is able to handle dense events is becoming increasingly significant. Crowd events cover a wide scope of different activities ranging from religious congregations like pilgrimages to sporting events and concerts. Every year there are deaths resulting from panic induced stampedes at major events across the world, one of the most recent severe incidents was in 2015 at the Hajj event where pilgrims were suffocated and crushed leading to more than 2000 fatalities.
The aim of this project is to develop an advanced pedestrian dynamics model that will be used as a tool to minimize physical stress developed under crowd conditions. The model is based on an extension of a granular dynamics model to account contact forces, ground reaction forces and torques in the pedestrians, private space, empathy and herding interaction, and peripheral vision of the pedestrians. In our current model, contact stiffness is obtained from biomedical journal articles, and coefficient of restitution is obtained by direct observations of energy loss in collisions. Existing rotational equations of motion are modified to incorporate a rotational viscous component, which allows pedestrians to come to a comfortable stop after a collision rather than rotating indefinitely. The shape of the pedestrian is obtained from a bird’s eye, cross sectional view of the human chest cavity and arms, which was edited to produce an enclosed shape. This shape is them approximated by a spheropolygon, which is a mathematical object that allow real-time simulation of complex-shape particles. The proposed method provides benefits to the accuracy on particle shape representation, and rotational dynamics of pedestrians at micro-simulation level, and it provides a new tool to calculate the risk of injuries and asphyxiation when people are trapped in dense crowds that leads to the development of high pressure on their bodies.
You will be involved in incorporating Artificial Intelligence (AI) to the pedestrians. AI will be achieved by developing algorithms in the pedestrian dynamics to model herding, and self-avoidance due to peripheral vision. Models will be validated from drone videos of real crowd dynamics by using Particle Tracking Velocimetry (PTV) analysis. Real live scenarios will be modelled, and architectural conditions to improve evacuation rates and minimize injuries will be optimized using evolutionary algorithms (EA). For this project you deep knowledge in programming, mathematics, and interest in AI, PTV, and EA.
The opportunity ID for this research opportunity is 2175