An Agent-Based Approach to Pedestrian and Group Dynamics: Experimental and Real World Scenarios
Modeling Pedestrian Wayfinding with Agent Based Models
1
Using Agent Based Modeling to model Pedestrian Wayfinding Model by : , Final Project for “Complexity Based Models” Class , Professor : Alireza Karduni Moira Zelner ABM Agent Based Modeling is used to simulate and of acons interacons autonomous in agents complex systems. Complex Systems? Ÿ It’s difficult or impossible to predict complex systems. Ÿ Complex systems could be organized or disorganized. Ÿ Properes of these systems emerge from between their interacons elements. Ÿ Complex systems could have different to different behaviors situaons. Ÿ Why ABM? Ÿ By using ABM we could understand some of the behaviors in these complex systems. Ÿ Understanding how a complex system such as a transportaon network works can help us to create robust plans and policies that address the complexity of these systems Pedestrians constantly use to informaon navigate the urban space and find their desnaon. The Process of Wayfinding Ÿ The answers to these quesons are found from : Ÿ Other People, The environment, Maps, Signage and ... Number of blocks in the X and Y direction Density of Destinations Percentage of Important (Major) Destinations Number of people in the streets Percentage of Residents (pedestrians with knowledge) make the rest pedestrians (Tourists) percentage of residents’ knowledge of destinations. The radius in which the pedestrians look for destinations and other people Add or remove signs by clicking on an intersection Set up signage at a defined percentage of intersections Set up streets and Bocks Set up destinations at random locations Randomly define major destinations Place pedestrians and residents at random locations on the streets Set two random destinations as a pedestrians trip plan put a percentage of destinations in residents knowledge list Should pedestrians? have perfect information ? Set pedestrians initial knowledge list empty Let each pedestrian know all the destinations Should pedestrians have perfect information ? No Yes Setup Stage Setup Environment Setup People Setup Signage Go Is your destination on your knowledge list? Walk Randomly No No Yes Yes Move forward one ( distance unit ) Is any resident in your search radius? No No Yes Yes Does the resident/or signage have your destination in his knowledge list ? Ask Questions Yes Yes No No Move to your destination Move forward one ( distance unit ) Put it in your knowledge list Stop Everyone are at their destination? Yes Yes No No Stop the Model Calculate travel statistics for every pedestrian Do nothing Ÿ The wayfinding process is simplified as a series of pedestrians quesons asks about how to get to their desnaon(s). The Model Agents and Environment Tourist Residents Do you know Where Destination 33 is? Tourist Do you know Where Destination 33 is? Let’s see if I can find destination 45 on this sign! 46 43 47 66 Before After Results Model Process (pseudo Code) 25% Residents with 30% knowledge,no signage 25% Residents with 10% knowledge with signage on 50 percent of intersections. Try with different situations yourself! Examine how different sign policies effect travel time of pedestrians!
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Transcript of Modeling Pedestrian Wayfinding with Agent Based Models
Using Agent Based Modeling to model
Pedestrian WayfindingModel by : , Final Project for “Complexity Based Models” Class , Professor : Alireza Karduni Moira Zelner
ABMAgent Based Modeling is used to simulate and of ac�ons interac�ons autonomous in agents complex systems.
Complex Systems?Ÿ It’s difficult or impossible to predict
complex systems. Ÿ Complex systems could be organized
or disorganized.Ÿ Proper�es of these systems emerge
from between their interac�onselements.
Ÿ C o m p l ex syste m s co u l d h ave different to different behaviorssitua�ons.
Ÿ
Why ABM?
Ÿ By using ABM we could understandsome of the behaviors in these complex systems.
Ÿ Understanding how a complex system such as a transporta�on network works can help us to create robust plans and policies that address the complexity of these systems
Pedestrians constantly use to informa�onnavigate the urban space and find their des�na�on.
The Process of Wayfinding
Ÿ The answers to these ques�ons are found from :
Ÿ Other People, The environment, Maps, Signage and ...
Number of blocks in the X and Y direction
Density of Destinations
Percentageof Important (Major) Destinations
Number of peoplein the streets
Percentage of Residents(pedestrians with knowledge)
make the rest pedestrians(Tourists)
percentage of residents’ knowledgeof destinations.
The radius in whichthe pedestrians lookfor destinations andother people
Add or removesigns by clicking on an intersection
Set up signage at a defined percentageof intersections
Set up streets andBocks
Set up destinationsat random locations
Randomly definemajor destinations
Place pedestriansand residents at random locationson the streets
Set two random destinations as a pedestrians trip plan
put a percentage of destinations in residentsknowledge list
Should pedestrians? haveperfect information ?
Set pedestrians initialknowledge list empty
Let each pedestrianknow all the destinations
Should pedestrianshave perfect information ?
No Yes
Setu
p S
tage
Setup Environment Setup People Setup Signage
Go
Is your destinationon your knowledge list?
Walk Randomly
NoNo
YesYes
Move forwardone ( distanceunit )
Is any residentin your searchradius?
NoNo YesYes
Does the resident/orsignage haveyour destination in his knowledge list ?
Ask Questions
YesYesNoNo
Move to your destination
Move forwardone ( distanceunit )
Put it in your knowledge list
Sto
p
Everyone are at their destination?
YesYesNoNo
Stop the ModelCalculate travel statistics for every pedestrian
Do nothing
Ÿ The wayfinding process is simplified as a series of pedestrians ques�onsasks about how to get to their des�na�on(s).
The Model
Agents and Environment
Tourist Residents
Do you know Where Destination 33 is?
Tourist
Do you know Where Destination 33 is?
Let’s seeif I can finddestination 45on this sign!
46
43
4766
Before After
Results
Model P
roce
ss (
pse
udo C
ode)
25% Residents with 30% knowledge,no signage
25% Residents with 10% knowledge with signage on 50 percent of intersections.
Try with different situations yourself!
Examine how different sign policieseffect travel time of pedestrians!
![wayfinding interpreti]e branding - Studio L'Imagestudiolimage.com/downloads/Wayfinding-Urban.pdf · Wayfinding Program In the heart of Chinatown, bilingual wayfinding signage directs](https://static.fdocuments.us/doc/165x107/5e8e6c5f99e6632d522e7817/wayfinding-interpretie-branding-studio-l-wayfinding-program-in-the-heart-of.jpg)
