Modelling behavior in agent based simulations of crowds

Crowd simulation is gaining an increasing amount of importance in recent years for a variety of purposes ranging from movies and games to safe design of buidlings, planning for major events like the Olympics and for preparing for emergencies. Over the last few decades, crowd simulation models have come a long way from the simpler network based approaches to simulations using agent based modelling (ABM) which have a much greater amount of detail. The bottom up approach of ABM allows modelers to consider the complexity of human behavior in much more detail than was traditionally feasible. It is possible to consider thousands of individuals with their individual behavior characteristics occupying and interacting in complex indoor environments like shopping malls. However, existing computational models fail to take into consideration the large body of work on human behavior during emergencies and make unsubstantiated assumptions like perfect knowledge of the layout and immediate evacuation on hearing a fire alarm. In this thesis, the existing work on human behavior during emergency egress is studied and perception, event identification, spatial knowledge acquisition and navigation are identified as the four key building blocks of a behavior model for agent based crowd simulation. Following this, key shortcomings in existing models of each of these are identified and addressed. Existing simulations model perception as a simple process of visual sensing of a fixed circular range. A more realistic perception model considering the limitations of human information processing capacity is proposed and its usefulness is demonstrated through its effect on existing motion planning systems. Pre-evacuation behavior is generally either simplified or ignored despite studies showing its importance. A model for simulating pre-evacuation behavior is introduced and its importance in developing better strategies for egress management is demonstrated. Due to limited understanding of the impact of partial spatial knowledge on indoor wayfinding, existing models assume complete knowledge. A game-based methodology was used to reveal patterns in indoor wayfinding like decision points and the impact of short-term memory which enables more accurate modeling of evacuation. The effect that movement model choice has on egress dynamics has never been studied systematically. The final contribution is a method for quantitatively comparing motion planning systems and a comparison of three popular motion planning systems that revealed the importance of zoned evacuation time as an important metric for validation.DOCTOR OF PHILOSOPHY (SCE

Sound and smoke propagation models for virtual crowd environments

Virtual crowd simulations are important for various applications like defense, social studies, etc. While literature exists on agent design and sensor modeling, there is still no generic model for sound and smoke propagation that can be integrated into existing virtual crowd simulation frameworks. This project attempts to address this issue by creating models for sound and smoke propagation in areas like MRT stations and parade grounds. It begins with an introduction to the basics of sound and smoke and a survey of the background and existing work in the fields of intelligent agents, virtual crowds and sensory and perception models. The concept of cellular automata and the application of the finite difference algorithm to a cellular automata is also discussed. The project then moves on to explain the characteristic features and properties of sound and smoke and also the constraints involved in modeling such a propagation model for virtual crowd environments. The proposed models for smoke and sound are then explained in detail and their performance in an agent based virtual crowd simulation is examined. The sound model suggested in this project could be further enhanced by adding additional capabilities like reflection and echoes for sound. The smoke model can be improved by improving the performance and making it smoother and less memory intensive to run. Work also needs to be done to model the perception capabilities of agents in a realistic way.Bachelor of Engineering (Computer Engineering

EOG based virtual keyboard

The signal resulting from measuring the resting potential of the retina is called the electrooculogram. This signal can be used to select keys on a virtual keyboard which can be used by paralyzed patients to do activities like operating computers. [4th Award

Traffic state estimation using floating car data

There is an increasing availability of floating car data both historic, in the form of trajectory datasets and real-time, in the form of continuous data streams. This paves the way for several advanced traffic management services such as current traffic state estimation, congestion and incident detection and prediction of the short-term evolution of traffic flow. In this paper, we present an analysis of using probe vehicles for reconstructing traffic state. We employ detailed agent-based microscopic simulations of a real world expressway to estimate the state from floating car data. The probe penetration required for accurate traffic state estimation is also determined.NRF (Natl Research Foundation, S’pore)Published versio

Analysing the effectiveness of wearable wireless sensors in controlling crowd disasters

The Love Parade disaster in Duisberg, Germany lead to several deaths and injuries. Disasters like this occur due to the existence of high densities in a limited area. We propose a wearable electronic device that helps reduce such disasters by directing people and thus controlling the density of the crowd. We investigate the design and effectiveness of such a device through an agent based simulation using social force. We also investigate the effect of device failure and participants not paying attention in order to determine the critical number of devices and attentive participants required for the device to be effective.Published versio

Quantitative comparison between crowd models for evacuation planning and evaluation

Crowd simulation is rapidly becoming a standard tool for evacuation planning and evaluation. However, the many crowd models in the literature are structurally different, and few have been rigorously calibrated against real-world egress data, especially in emergency situations. In this paper we describe a procedure to quantitatively compare different crowd models or between models and real-world data. We simulated three models: (1) the lattice gas model, (2) the social force model, and (3) the RVO2 model, and obtained the distributions of six observables: (1) evacuation time, (2) zoned evacuation time, (3) passage density, (4) total distance traveled, (5) inconvenience, and (6) flow rate. We then used the DISTATIS procedure to compute the compromise matrix of statistical distances between the three models. Projecting the three models onto the first two principal components of the compromise matrix, we find the lattice gas and RVO2 models are similar in terms of the evacuation time, passage density, and flow rates, whereas the social force and RVO2 models are similar in terms of the total distance traveled. Most importantly, we find that the zoned evacuation times of the three models to be very different from each other. Thus we propose to use this variable, if it can be measured, as the key test between different models, and also between models and the real world. Finally, we compared the model flow rates against the flow rate of an emergency evacuation during the May 2008 Sichuan earthquake, and found the social force model agrees best with this real data