Point clouds to direct indoor pedestrian pathfinding

Increase in building complexity can cause difficulties orienting people, especially people with reduced mobility. This work presents a methodology to enable the direct use of indoor point clouds as navigable models for pathfinding. Input point cloud is classified in horizontal and vertical elements according to inclination of each point respect to n neighbour points. Points belonging to the main floor are detected by histogram application. Other floors at different heights and stairs are detected by analysing the proximity to the detected main floor. Then, point cloud regions classified as floor are rasterized to delimit navigable surface and occlusions are corrected by applying morphological operations assuming planarity and taking into account the existence of obstacles. Finally, point cloud of navigable floor is downsampled and structured in a grid. Remaining points are nodes to create navigable indoor graph. The methodology has been tested in two real case studies provided by the ISPRS benchmark on indoor modelling. A pathfinding algorithm is applied to generate routes and to verify the usability of generated graphs. Generated models and routes are coherent with selected motor skills because routes avoid obstacles and can cross areas of non-acquired data. The proposed methodology allows to use point clouds directly as navigation graphs, without an intermediate phase of generating parametric model of surfacesUniversidade de Vigo | Ref. 00VI 131H 641.02Xunta de Galicia | Ref. ED481B 2016/079-0Xunta de Galicia | Ref. ED431C 2016-038Ministerio de Economía, Industria y Competitividad | Ref. TIN2016-77158-C4-2-RMinisterio de Economía, Industria y Competitividad | Ref. RTC-2016-5257-

Hybrid agent-based and social force simulation for modelling human evacuation egress

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