HPC Enhanced Large Urban Area Evacuation Simulations with Vision based Autonomously Navigating Multi Agents

AbstractAn evacuation simulation code based on Multi Agent Systems (MAS), with moderately complex agents in 2D grid envi- ronments, is developed. The main objective of this code is to estimate the effectiveness of the measures taken to smoothen and speedup the evacuation process of a large urban area, in time critical events like tsunami. A vision based autonomous navigation algorithm, which enables the agents to move through an urban environment and reach a far visible destination, is implemented. This simple algorithm enables a visitor agent to navigate through urban area and reach a destination which is several kilometers away. The navigation algorithm is verified comparing the simulated evacuation time and the paths taken by individual agents with those of theoretical. Further, a parallel computing extension is developed for studying mass evacuation of large areas; vision based autonomous navigation is computationally intensive. Several strategies like communication hiding, dynamic load balancing, etc. are implemented to attain high parallel scalability. Preliminary tests on the K-computer attained strong scalability above 94% at least up to 2048 CPU cores, with 2 million agents

High Performance Computing for City-Scale Modelling and Simulations

The 21st Century is witnessing a rapid rise of urbanization both in the developed and the developing world. Cities increasingly need to be able to do more with less in order to provide for the well-being of their citizens in a sustainable way. The promise of Smart City is an emerging ability to understand, to respond to, and to shape human activity at urban population and geographic scales so that a more agile, adaptive, and sustainable urban environment can be created (see Batty et al., 2012; Caragliu et al., 2011; Chourabi et al., 2012; Su et al., 2011 for early adoption of Smart City). To be effective, this requires the predictive power of data-driven modelling and city-scale computational simulations. Recently city-scale simulations are becoming possible thanks to a surge of development in the high-performance computing (HPC) domain including advanced hardware, computational and algorithmic techniques such as domain decomposition across multi-GPUs and multigrid techniques. Advanced high performance computing systems (a billion billion calculations per second) are now becoming available to performance city-scale simulations with micro-scale models of an individual objective (structure, people, vehicle, etc.) (e.g. Sánchez-Medina et al., 2010; Hori, 2011; Zia et al., 2012; Wijerathne et al., 2013; Pijanowski et al., 2014; Yoshimura et al., 2016; Johansen et al., 2017; Lu and Guan., 2017

Beyond the shortest-path: Towards cognitive occupancy modeling in BIM

Current approaches to simulate occupants' wayfinding in AEC mostly employ direct routing algorithms that assume global knowledge of the navigation environment to compute a shortest path between two locations. This simplification overlooks evidence concerning the role of perception and cognition during wayfinding in complex buildings, leading to potentially erroneous predictions that may hinder architects' ability to design wayfinding by architecture. To bridge this gap, we present a novel simulation paradigm entitled Cognitive Occupancy Modeling in BIM to simulate wayfinding by means of a vision-based cognitive agent and a semantically-enriched navigation space extracted from BIM (Building Information Modeling). To evaluate the predictive power of the proposed paradigm against human behavior, we conducted a wayfinding experiment in Virtual Reality (VR) with 149 participants, followed by a series of simulation experiments with cognitive and direct routing agents. Results highlight a significant correspondence between human participants' and cognitive agents' wayfinding behavior that was not observed with direct routing agents, demonstrating the potential of cognitive modeling to inform building performance simulations in AEC