Research on Human Behaviour Simulation in the Built Environment Bauke de Vries Bauke de Vries

Programme Who am I / Where do I come from Research programma Design and Decision Support System PhD and Graduation projects on Behaviour Simulation in the Built Environment

Who am I Name: Bauke de Vries Age: 49 Occupation: Married, 2 children Education: Msc in Architecture, Building and Planning, PhD at Eindhoven University of Technology (TU/e) Profession: Professor at TU/e in Design Systems

Where am I from Eindhoven University of Technology, founded by Philips 50 year ago Faculty Architecture, Building and Planning Bachelor students Master students - 50 PhD students

DDSS Design Planning Artificial Intelligence ICT Research Programme: Design and Decison Support Systems

EU and PhD projects Building Management Simulation Centre Decision Support System for Building Refurbishment Measuring User Satisfaction through Virtual Environments Using a Virtual Environment for Understanding Real- World Travel Behavior A Learning Based Transportation Oriented Simulation Systems Human Behavior Simulation in the Built Environment

Projects on Behaviour Simulation 1.VR experiments (Amy Tan) 2.Space utilisation (Vincent Tabak) 3.Pedestrian behavior (Jan Dijkstra) 4.AMANDA framework (Joran Jessurun) 5.Evacuation and smoke simulation (Martin Klein) 6.Safety assesment (Ruben Steins)

The Reliability and Validity of Interactive Virtual Reality Computer Experiments: SPIN System

SPIN: Demo (start CD-ROM)

The Reliability and Validity of Interactive Virtual Reality Computer Experiments: Conclusions 1.The structural dimensions (number of stops, number of activities) were better measured by SPIN. 2.The PAPI questionnaire yielded better responses for durations (of shopping activity, services activity, out-of-home leisure activity, travel between activities, whole schedule). 3.Route choice data indicated that SPIN was not able to measure this dimension better than PAPI.

User Simulation of Space Utilisation Existing models focus on evacuation behaviour Aim: Analyze the performance of a design through user behaviour simulation

System overview The User Simulation of Space Utilisation (USSU) system. Important aspect: interaction between persons.

System overview Input  The organisation: Roles, activities, persons (FTE)  The design of the building in which the organisation is (or will be) housed: the spatial conditions.

System overview Output Movement pattern for each member of the organisation. From this performance indicators can be deduced, like:  Average/maximum walking distance/time per individual.  Number of persons per space in time.  Usage of facilities.

Skeleton activities The core activities for a certain period (a workday). Activities depend on the organisational workflow. Some activities require interaction between employees. TimeActivityResources 09:00-10:00ResearchOffice space X 10:00-11:00Get coachingOffice space Y 11:00-16:00ResearchOffice space X 16:00-17:00Attend presentationMeeting room Z 17:00-18:00ResearchOffice space X

Intermediate activities Activities adjust/complement the skeleton activities. Categories of activities:  Physiologic: getting a drink, having lunch, going to toilet.  Social: having a chat with colleague. TimeActivityResources 09:00-09:15ResearchOffice space X 09:15-09:20Get a drinkCoffee corner 09:20-10:00ResearchOffice space X 10:00-11:00Get coachingMeeting room Z 11:00-12:15ResearchOffice space X 12:15-13:15LunchCanteen 13:15-14:00ResearchOffice space X TimeActivityResources 09:00-10:00ResearchOffice space X 10:00-11:00Get coachingOffice space Y 11:00-16:00ResearchOffice space X 16:00-17:00Attend presentationMeeting room Z 17:00-18:00ResearchOffice space X

Intermediate activities S-curve method to predict the intermediate activities. Shape of curve influenced by:  Time pressure.  History of executed activities.  Skeleton activity (task).

System design

Scheduler After drawing the skeleton activities: scheduler is activated. Consists of 9 AI (Artificial Intelligence) modules. Responsible for (among others):  Scheduling skeleton activities (SkeletonScheduler)  Scheduling intermediate activities (IntermediateScheduler)  Repairing schedules (OverlapRemover & GapRemover)  Determining interaction between activities (InteractionScheduler)  Finding combinations of activities (CombinationFinder)  Finding an appropriate location (ResourceFinder)

Experiment Capture the real space utilisation Using RFID to capture the real space utilisation. Merge spaces into zones.

Pedestrian Behaviour Shopping environment populated with agents representing pedestrians Agents –are supposed to carry out a set of activities A i motivational states –have different motivational states –move across the network perceptualfields awareness thresholdsignalling intensity –have perceptual fields that may vary according agent’s awareness threshold and the signalling intensity of a store Context

Basic Equation Behavioural Aspects perceptual field of agent i is the awareness threshold of agent i is the signalling intensity of store j

Data Collection

Data Collection

Estimation Results Basic equation is estimated for fixed distances The dichotomous response variable –is the awareness of a store category within the perceptual field Explanatory variables are –store category –motivation for visiting the city centre

AMANDA framework Extension of pedestrian/user behaviour models with destination and route choice, and activity scheduling Domain: pedestrian behaviour in a public space (e.g. shopping environment), user movement in a building (e.g. office building)

Agent Architecture

Environment Pedestrians move in a built and/or urban environment –Pedestrians are represented by agents –A hybrid (grid and polygon) based model is used to simulate their behaviour across the network Each cell in the grid can be considered as an information container object; it has information about which agents and polygons occupy it. Context

Simulation of Individual Behaviour Context Action Selection strategy, goals, planning Steering path determination Pedestrian Movement

AMANDA demo (start AMANDA test application)

Evacuation and smoke simulation Simple evacuation behaviour: shortest route to exit CAD vendor independent: IFC based Using existing smoke simulation: CFAST No interaction between evacuation and smoke simulation

Occupants data Building model (IFC) Fire data Source file (XML) Evacuation simulation (AMANDA) Results Smoke simulation (CFAST) Designer User Interface

Testcase: Vertigo building Model created with Autodeks/Revit and exported to IFC 9-th floor –26 rooms –2 exits

IFC input IFC Smoke simulation Evacuation simulation

Evacution Simulation: AMANDA

Smoke simulation: CFAST Consolidated Model of Fire Growth and Smoke National Institute of Standards and Technology (NIST) Import/export facilities Max 30 spaces, 50 openings

File Input –3D geometry –Openings –Simulation time, output interval User interface input –Fire specification

Linking results Evacuation and Smoke simulation Required egress time < available egress time Simulation results –Evacuation  Space location for each person at any time –Smoke  (harmful) conditions in each space at any time. + =

Test results Total and everage evacuation time Numbers per exit Per agent: –Distance covered –Spaces crossed (!) –Walking speed Per space: –Space utilisation

Safety assesment The main purpose of the Dutch Working Conditions Act (WCA) is to ensure three things: –Safety: no acute dangers for people at work –Health: no long term or chronic physical health risks –Wellbeing: no psychological problem caused by working conditions

Compliance checking Soft coded regulations Each firm must have a policy stating in what way the personal privacy of individuals is guaranteed. Hard coded regulations For seated work a free space is present beneath the working surface of at least 70 cm in height and 60 centimeters in depth and width. For office-work the minimal depth for legs and feet is 65 and 80 centimeters respectively.

Example: Soft coded Privacy factors (self defined) In offices that are shared by many people, the chance of privacy problems is higher. Rooms with high ceilings have more sound resonance, which means more inconvenience, which results in less privacy Rooms adjacent to busy corridors suffer from higher sound levels, resulting in more inconvenience Rooms next to windows give a higher feeling of privacy, since people can ‘lose’ themselves in the view

Method: Fuzzy logic (1) Input Membership function: amountOfPeople Input Membership function: officeHeight

Method: Fuzzy logic (2) Output membership function: privacyProblem

WCA system

Input data IFC file created with Autodesk/Revit: Building geometry Organisational data generated with USSU: Acitivity and location for each person at any time Building physics data generated with ecoTect

Output data

Thanks !