1. Circulation Simulation Andrew Moeding

2. Simulation Types Traffic flow pattern simulation Building/pedestrian circulation simulation

3. Who is using it ? Engineers, Architects, Urban Planners, Corporations, Universities, Event Organizers, Police –Security / egress testing –Walkability studies –Building circulation –Pedestrian / traffic interaction Highway / Transportation departments –Interchange Design –Signal coordination –Rapid transit operations –Transit station design –http://horstmann.com/applets/RoadApplet/RoadApplet.htmlhttp://horstmann.com/applets/RoadApplet/RoadApplet.html –http://www.phy.ntnu.edu.tw/oldjava/Others/trafficSimulation/applet.htmlhttp://www.phy.ntnu.edu.tw/oldjava/Others/trafficSimulation/applet.html –http://www.traffic-simulation.de/http://www.traffic-simulation.de/

4. Software Products Trafficware – Sim Traffic TSI-CORSIM SimWalk, Legion Studio, http://www.legion.com/case-studies/sydney-olympics- popup1.php http://www.legion.com/case-studies/sydney-olympics- popup1.php AI Implant Massive

5. How it works Agent Based Model –Agent based models consist of dynamically interacting rule based agents. The systems within which they interact can therefore create complexity like that which we see in the real world. –The idea is that a system adapts to internal and external pressures so as to maintain functionalities. The task of harnessing that complexity requires consideration of the agents themselves -- their diversity, connectedness, and level of interactions. –The system of interest is simulated by capturing the behavior of individual agents and their interconnections. Agent-based modeling tools can be used to test how changes in individual behaviors will affect the overall, emergent system behavior. –http://www.casa.ucl.ac.uk/repast/JohnWard_ThursdayMeeting2006_01_19.pdfhttp://www.casa.ucl.ac.uk/repast/JohnWard_ThursdayMeeting2006_01_19.pdf – http://www.casa.ucl.ac.uk/repast/AndrewCrooks_19_1_06.pdf http://www.casa.ucl.ac.uk/repast/AndrewCrooks_19_1_06.pdf –http://www.youtube.com/watch?v=Cw1b_RYi784http://www.youtube.com/watch?v=Cw1b_RYi784 – http://www.youtube.com/watch?v=0pILzhLpMPc&mode=related&searchhttp://www.youtube.com/watch?v=0pILzhLpMPc&mode=related&search Wikipedia

6. How it works Multi-Agent System –(MAS) is a system composed of several software agents, collectively capable of reaching goals that are difficult to achieve by an individual agent or monolithic system.software agents system –Multi-agent systems can manifest self-organization and complex behaviors even when the individual strategies of all their agents are simple.self-organization –The study of Multi-Agent Systems is concerned with the development and analysis of sophisticated Artificial intelligence problem solving and control architectures for both single-agent and multiple-agent systems.Artificial intelligence –Another paradigm commonly used with MAS systems is the pheromone, where components "leave" information for other components "next in line" or "in the vicinity". These "pheromones" may "evaporate" with time, that is their values may decrease (or increase) with time. –http://digitalurban.blogspot.com/search/label/3D%20Agentshttp://digitalurban.blogspot.com/search/label/3D%20Agents Wikipedia

7. How it works Multi-Agent System Cont. –MAS systems are also referred to as "self-organized systems" as they tend to find the best solution for their problems "without intervention". There is high similarity here to physical phenomena, such as energy minimizing, where physical objects tend to reach the lowest energy possible, within the physical constrained world.self-organized systems Crowd Simulation –The entities - also called agents - are given artificial intelligence, which guides the entities based on one or more functions, such as sight, hearing, basic emotion, energy level, aggressiveness level, etc.. The entities are given goals and then interact with each other as members of a real crowd would. They are often programmed to respond to changes in environment, enabling them to climb hills, jump over holes, scale ladders, etc. This system is much more realistic than particle motion, but is very expensive to program and implement.agentsartificial intelligence Wikipedia

8. AI Implant What it is –http://www.ai- implant.com/solutions/simulation__training/technical_specifications.htmhttp://www.ai- implant.com/solutions/simulation__training/technical_specifications.htm Examples –http://www.youtube.com/watch?v=lCH0QZFs7iYhttp://www.youtube.com/watch?v=lCH0QZFs7iY –http://www.youtube.com/watch?v=T5VZFxRJ6sshttp://www.youtube.com/watch?v=T5VZFxRJ6ss –http://www.massivesoftware.com/http://www.massivesoftware.com/

9. Sulan Kolatan, Columbia studio AIA - Architectural Intelligence Agency: In choosing to work with software specifically created for industrial design and film animation rather than for architectural design, our studio explicitly engaged the issue of cross-categorical pollination by problematizing it in the design process itself. In this way, the architectural design process was affected by a "productive inadequacy". The design tool was not entirely but somewhat inadequate in that it had not been made to address the conventions of architectural design but rather those of another kind of design. It was like having to write with a knife. One had to rethink "writing" through the logic of "cutting" to arrive at "carving". The studio's intent was to introduce computational methodologies into architectural design through the use of self-organizing system software. Play was combined with analytical and speculative thought to diagram and construct architectures without fixed scale but with set rules. Scalability was to be understood as referring to a diagram (set of rules) capable of being translated into many scales and -- by extension -- contexts. Particularly viable scales and contexts were those where performative affinities to the diagram already existed. The studio was structured in five phases focusing on Investigative Play, Identity Programming, Dynamic Chimerization, Variable Inhabitation and Performative Taxonomy.

10. Sulan Kolatan, Columbia studio

13. Projects Frank Gesualdi “We were scripting behavior into a population of agents and allowing these agents to mix as if in a virtual petry dish of sorts. We were particularly interested in scripting behavior that was "programmatic" ie this population of agents represented quiet space, this population was akin to attraction to groups over 10…”

14. Additional Research http://www.mukogawa-u.ac.jp/~okazaki/OK/PMOVE/paper1/London.pdf http://www.crowddynamics.com/index.htm http://www.casa.ucl.ac.uk/projects/projectDetail.asp?ID=58 http://www.digitalurban.blogspot.com/