Simulation has become one of the popular techniques to model evacuation scenarios. Simulation is used as an instrumental for examining human movement during both normal and emergencies such as evacuation. During an evacuation, people will be in a panic situation and egress behaviour that will find the way out from a dangerous place to a safe place. Two well-known techniques in simulation that can incorporate human behaviour inside the simulation models are Agent-Based Simulation and Social Force Simulation. ABS is using the concept of a multi-agent system that consists of decentralized agents which can be autonomous, responsive and proactive. Meanwhile, SFS is a physical force to drive humans dynamically to perform egress actions and human self-organised behaviour in a group. However, the main issue in modelling both ABS or SFS alone is due to their characteristic as ABS have difficulty in modelling the force element and collective behaviours while SFS does not focus on free movements during the evacuation. This behaviour was due to the structure of humans (agents) inside ABS is decentralized which resulting collision among agents and the desired formation of evacuation was not achieved. On the other hand, in a single SFS model, the human was not proactive in finding the way out which was not reflecting the actual behaviour of humans during the evacuation. Both ABS and SFS are potential techniques to be combined due to their characteristics of self-learning and free movement in ABS and self organization in SFS. The research methodology based on modelling and simulation (M&S) life-cycle has been utilized for this work; consists of three main phases, namely preliminary study, model development and validation and verification and finally the experimentation and the results analysis. The M&S life-cycle was utilized aligned with the research aim which is to investigate the performance of the combined ABS and SFS in modelling the egress behaviour during evacuation. To achieve the aim, five evacuation factors have been chosen namely obstacles, the number of exits, exit width, triggered alarm time, and the number of people that have been the most chosen factors in the literature review. Next, three simulation models (using techniques: SFS, ABS and hybrid ABS/SFS) have been developed, verified, and validated based on the real case study data. Various simulation scenarios that will influence the human evacuation movement based on the evacuation factors were modelled and analysed. The simulation results were compared based on the chosen performance measurement parameters (PMP): evacuation time, velocity, flow rate, density and simulation time (model execution time). The simulation results analysis revealed that SFS, ABS, and hybrid ABS/ SFS were found suitable to model evacuation egress (EE) based on the reported PMP. The smallest standard error (SSE) values reported 66% for hybrid ABS/ SFS, 17% for ABS and 17% for SFS where the highest percentage of SSE indicated the most accurate. Based on the experiment results, the hybrid ABS/ SFS revealed a better performance with high effectiveness and accuracy in the simulation model behaviour when modelling various evacuation egress scenarios compared to single ABS and SFS. Thus, hybrid ABS/ SFS was found the most appropriate technique for modelling EE as agents in the hybrid technique were communicating to each other by forming a decentralised control for smooth and safe EE movement. In addition, the impactful factors that affected the result accuracy were exits, the exit width (size), the obstacle and the number of people. Conclusively, this thesis contributed the hybrid ABS/ SFS model for modelling human behaviour during evacuation in a closed area such as an office building to the body of knowledge. Hence, this research was found significant to assist the practitioners and researchers to study the closer representation of human EE behaviour by considering the hybrid ABS/SFS model and the impactful factors of evacuation
