1. 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

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

3. 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.

4. 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

5. 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

6. Examples of Agent-based modeling
Swarm following the leader
Predator Prey model
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. 6 6 6

7. 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

8. 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. 8 8

9. 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

10. 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

11. 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

12. The prototype system architecture12 12 12

13. 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

14. 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

15. 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

16. 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

17. 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

18. 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

19. 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

20. Use case 2: Agent-based approach for emergence navigation considering the dynamics of disasters20 20 20

21. 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

22. 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

23. 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

24. 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

25. 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

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