Pedestrian movement analysis and simulation in public spaces

1. Pedestrian movement analysis and simulation in public spaces Jukka Jokelainen 26.6.2010 ERES Milan

2. Structure of the presentation • Motivation • What is agent based modeling • Tracking • Model • Incomplete information • Decision making dilemmas • Optimization problem • Simulation

3. Motivation • Demand for new services for customers is increasing • Shopping center management need reliable ways to motivate rent for shop owners • New design tool for designers of future shopping centers

4. Agent based modeling ”An agent-based model (ABM) is a class of computational models for simulating the actions and interactions of autonomous agents (both individual or collective entities such as organizations or groups) with a view to assessing their effects on the system as a whole. The models simulate the simultaneous operations and interactions of multiple agents, in an attempt to re-create and predict the appearance of complex phenomena.” • K.I.S.S. (keep it simple & stupid)

5. Five levels of agent based modeling • Numerous agents specified at various scales • Decision-making heuristics • Learning rules or adaptive processes • Interaction topology • Non-agent environment

6. Pedestrian tracking sensors and uncertainity • Different sensor types for different spaces and uses are various • Very different variations in accuracy, area coverage and price • Accuracy of data?

7. Imprecise propability • Precise propability • There is a 40% chance that it rains in the afternoon • Imprecise propability • Chance for rain is somewhere between 20 and 50 percent. • More levels of freedom • Allows for weaker information states • Makes inferences and decisions more robust

8. Decision making • Different kinds of decisions • Strictly prefer • Indifferent • Incomparable • In many cases multiple criteria need to be taken into account

9. Multi-objective decision making • Usually the case with pedestrians • ”I want ice cream and go shop for clothes, but if I have ice cream with me I can’t go to the clothing store” • Multiple criteria that need to be taken in account

11. Optimization problem • Traditional Objective Function: Max or Min F = W1 ⋅G1 (X) + W2 ⋅G2 (X) + W3 ⋅G3 (X) + … Wi = Weight for i, Gi (X)=Performance of Objective i, X= Decision variables • Known problems with models • What if G1 (X), G2 (X), G3 (X) are different units? • How to set the weights W1 , W2 , W3 , .. ?

12. Multi-objective optimization problem • Treat separately • Different objectives • Different constraints • Satisfaction of the individual objectives • Solution: at the Max- Min point, or Maximum satisfaction of the least satisfied. • Uncertainty

13. Minimizing uncertainty • Use as much qualitative and quantitative data as possible • Not really flexible

14. Maximizing uncertainty • Admit ignorance and make no commitment to the data • = maximize entropy • Measure of randomness • Measure of unpredictability • Measure of uncertainty • Mathemathical model exists

15. Data handling and optimization • Available information • Minimum uncertainty • Entropy max • Maximum uncertainty • To satisfy both we need fuzzy logic and dynamic optimization

16. Summary for the method • The values for which no information is available. • Maximize entropy • Analyst’s incomplete knowledge – constraint • Use fuzzy numbers and fuzzy relations • Incorporate the two to complete the model. • Multi-objective optimization – fuzzy optimization

17. Thank You!