Integrating spatio-temporal data in agent-based simulations for emergency navigation support Zhiyong Wang In cooperation with Sisi Zlatanova Emergency navigation creates a rich set of new challenges for GIS tools and agent simulation technology. Since both GIS and agent-based simulation have their own advantages and disadvantages and the existing work that combine them also have their deficiencies, we propose novel approaches that can incorporate real-time data into agent-based simulation model to provide more accurate analysis and prediction of simulation results. The proposed approach will be applied into 2 use cases: Agent-based modeling of human movements during disasters; Agent-based approach for emergence navigation considering the dynamics of disasters (flood or plume). 1 1 1

Content Background Research question Research methodology The prototype system architecture Use cases Data and software Proposed timetable Planned Publications 2 2 2

Disaster management and Emergency navigation When disaster occurs, navigation plays a important role in the rescue operation. Before first responders plan their routes, it is quite important for them to be aware of the situations including the evolution of disasters and human activities during disasters. While successful navigation could help them reach their destination safely and fast, un-successful routing could lead them into the trouble.

Pros and cons of GIS tools database management spatial analysis geographical visualization ………. Cons dynamic modelling simulation Let’s look at the GIS first, GIS tools can do many things. The point I want to make with this slide is that most GIS tools have some disadvantages. For example, no dynamic modelling and no simulation, specifically, they can not simulate the dynamics of disasters or human behaviors. 4 4 4

Agent based Modeling of Simulation Agent is: - a piece of code to describe dynamic phenomena (moving people, events, plume...) An agent-based model (ABM) is: - a class of computational models - provides simulation of actions and interactions of autonomous agents (both individual or groups) The goal is assessing effects on the dynamic system as a whole (e.g. disaster management, evacuation, natural phenomena, etc. ) Let’s turn to the ABM. Read definitations. Conlude with: For example, ABM has been applied to the 5 5 5

Examples of Agent-based modeling Swarm following the leader Predator Prey model http://www.youtube.com/watch?v=qj_86P4hf2s Here I will show some animations. First the predator and prey model, simulates interaction between the populations of predator and prey in an isolated area. The green point represents and prey, and the red point is the predator, predator moves and eats the prey but both predator and prey can reproduce themselves and are surviving. As you can see, the blue object is the leader followed by the green points. The green follower does not only follow the leader, but also considers the positions and speed of other followers, so the group as a whole behave in a well- organized way. No collision. http://www.youtube.com/watch?v=f510pmahpE8 http://www.xjtek.com/anylogic/demo_models/11/ 6 6 6

Pros and cons of Agent Model simulation Complex dynamics Behavior and interaction between agents Prediction ………. Cons Data used are mostly simulated(no real data) Predetermined model From the demos, you can see ABM can simulate the complex dynamic phenomena, it still has some weakness. The simulation is run in simple environment that can not reflect the complexity of the real world. 7 7 7

Integration of GIS and Agent-Based Model Simulation Agents and Networks: Commuting Arena Evacuation Simulation in EPT To make up the deficiencies of ABM, Some reach try to integrate geographic data into the ABM to simulate the spatial-temporal phenomena. GIS data provides the world in which agents interact with each other and spatial objects. The first one is the basic traffic model that explores how agents travel to from one place to another for work. The idea is that if you increased the number of agents (people) more congestion will arise. Road lines are imported from shapefiles. The second uses agent-based simulation modeling to conduct evacuation analyses for buildings. The user specifies what type of threat is to occur, where the threat will develop, then executes the model, and outputs data reports, for example, evacuation times. 3D GID data model is used to build the simulation environment. http://www.youtube.com/watch?v=mvkz1HwEWXU&feature=socblog_th http://www.youtube.com/watch?v=ixTiuLwlLSc&feature=related 8 8

Deficiencies of the existing integration of ABM and GIS Uses lacks real data Uses real maps and 3D models but hardly real-time data ( e.g. GPS tracks, plume movement) Rigid input parameters Predefined model (No correction) 9 9 9

Application System (DDDAS) Dynamic Data Driven Application System (DDDAS) DDDAS formulates simulation models and methods with: - dynamically measured data, - algorithms, - system tools, - mathematical and statistical advances The challenges are: - how to incorporate additional data how to dynamically steer the measurement process? 10 10 10

Main Research question Is integration of spatial data (static such as 2D/3D models and dynamic such as real time measurements) into agent-based simulations able to better support the emergency navigation considering human movements and moving disasters? 11 11 11

The prototype system architecture 12 12 12

1. Research questions related to GIS What kind of information will be needed for agent-based simulation? How to derive the network from these information What kind of relationships between agents and spatio-temporal objects will support the simulation? What kind of data model should be used for management of the dynamic data that includes the real-time data, the information of moving objects (disasters, pedestrian, vehicles, etc.) 13 13 13

2. Research questions related to Agents How many types of agents should be developed? What kind of user profile should be considered for route determination under emergencies? What kind of rules should be designed for agents to dictate their behaviors? How can we evaluate the behaviors of the agents(e.g. rescue vehicle agent, etc.)? 14 14 14

3. Research questions related to routing What kind of routing algorithms should be considered? What kind of re-routing strategy should be designed for the agent to avoid obstacles and disasters? How does the agent-based simulation system provide navigation services for both first responders and citizens? On what kind of network (2D and 3D) the algorithms will be run? 15 15 15

4.Research questions to DDDAS What kinds of real-time information can be incorporated into the simulation model? How to correct the agent-based model with real-time data? How to verify and validate the developed simulation model? 16 16 16

Research Methodology Literature study and technology investigations Literature study and technology requirements Literature study and technology investigations Conceptual design Design a multi-agent simulation framework for moving objects (considering GPS tracks) 2. Extend the multi-agent simulation network by considering disaster models (considering real measurements) Implementation developing prototype Validation and Adaptation Conduct tests in different scenarios Make improvement based on the test results. 17 17 17

Use case 1: Agent-based modeling of human movements during disasters Jam Different pattern s of human movements Flee Modeling of human movements under with real GPS data 18 18 18

Demo 1: updating positions with GPS tracks The demo based on this pic is under development, I will send it to you when it is finished During the demo is running you have to explain in when GPS tracks will be used 19 19 19

Use case 2: Agent-based approach for emergence navigation considering the dynamics of disasters 20 20 20

Demo 2: moving plume updated with measurements obtained from the field This demo will be animation of moving polygon sent to you {position, type of geometry, moving speed, direction} 21 21 21

Software and Data Software: Programing language: Java Programming tools: Eclipse, Java 3D, Java OpenGL Visualization tool: Bentley Agent-based modelling toolkit: REPAST, MASON DBMS: PostGIS or Oracel Spatial Data: available 2D and 3D data 22 22 22

Proposed timetable Year 1 Literature study and technology investigations Create the agents for modeling the movements of rescuers corrected with GPS tracking Make predictions based on mathematical models Implement the data models for moving objects Test the prototype system in the road network Improve the system based on test results Extend the work into 3D environment 23

Proposed timetable Year 2: Design and implement a multi-agent system for emergency navigation Connect the multi-agent simulation model with the disaster model Extend the work into 3D environment Year 3: Test the agent-based simulation system in different scenarios Improve the models of agents. Year 4 Extend the work considering the disasters and communication between agents Assess and improve our systems. Write the thesis. 24

Planned publications Regular reports 2 or 3 conference papers per year e.g. ISCRAM, AAMAS, DDDAS,UDMS etc. 2 papers in reviewed scientific journals e.g. Computational Geosciences, Computers and Geosciences, international journal of emergency response 25

Thanks for your attention! Any questions and suggestions? 26 26