1 / 17

Pedestrian movement analysis and simulation in public spaces

Explore the use of agent-based modeling to improve pedestrian movement analysis and simulation in public spaces, focusing on shopping center management. Learn about decision-making dilemmas, optimization problems, and handling incomplete information.

rrow
Download Presentation

Pedestrian movement analysis and simulation in public spaces

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  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

  10. 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 , .. ?

  11. 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

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

  13. 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

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

  15. 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

  16. Thank You!

More Related