An Agent Based Model for the Simulation of Transport Demand and Land Use
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Transcript of An Agent Based Model for the Simulation of Transport Demand and Land Use
An Agent Based Model for the Simulation of Transport Demand and Land Use.
Nam Huynh, Vu Lam Cao, Rohan Wickramasuriya,
Matthew Berryman, Pascal Perez and Johan Barthélemy
SMART Infrastructure Facility
University of Wollongong, Australia
Introduction: Methodology and study area
• Agent-based model
• Randwick area(suburb in south eastern Sydney)
• Population (2006)
– 106,000 individuals
– 47,000 households
• Data
– ABS census data
– HTS data
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Introduction: Model components
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Travelmode choice
Syntheticpopulation evolution
Relocation and
liveability
Update traveldiaries
TRANSIMS
Synthetic population generation
Travel diariesassignment
Relocation, TRANSIMS and Modal choice
• Residential relocation choice:
1. Decision to move: multinomial Logit
2. Location + rent/buy choice: affordability, availability, liveability
• Traffic micro-simulation: TRANSIMS
• Modal choice: Multinomial Logit
Utility = f(fixed cost, estimated travel time, income)
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Synthetic population
• Sample-free generator
• 106,000 Individuals:
– Age
– Gender
– Household relationship
– Travel diaries
• 47,000 Households:
– Residents (individuals)
– Income
– Category
– Home location
• Evolution process: natural evolution, immigration and emigration, 2006 → 2011
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Baseline synthetic population: Validation
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Travel diaries: trip sequence assignment
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Trip diaries: Assignment of facility types to origins/destinations
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END
START
CHECK THE CURRENT
TRIP’S POSITION
First tripOrigin =
Home
Last tripDestination =
Home
CHECK THE CURRENT
TRIP’S PURPOSE
Destination =
Specific facility
type
Purpose = Home or Education or Change Mode
Destination =
randomly pick up from list of
facility types associated with
this trip purpose
YesNo
Yes
No
Yes
No
Origin =Previous
destination
Trip diaries: Activity locations
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Yes
Searches for an activity location close to the
origin and has a car park available within a
500 m walking radius
Gets the list of activity locations associated
with the activity type of the destination
No
ENDT
Mode =
CarDriver
Yes
START
Origin location ID =
destination location ID
of previous trip
Origin location ID =
household’s dwelling ID
Activity type
“Home”?Destination location ID =
household’s dwelling ID
First trip?Yes
Yes
No
No
NoFound such a
location?
Changes to public
transport mode
Destination location ID =
random location ID from the
list of activity locations
RTEND
Destination location ID =
car park location ID
Trip diaries update for successive years
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Compare attributes
of SynHhold before
and after evolution
Change in
attributes?
Yes
No
Re-assign travel
diary for this SynHhold
(Step 1)
All Hholds
checked?
Yes
No
HTS
Data
Location Data
Journey to Work Data
END
Results: Percentage of trips by mode
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Results: Percentage of trips by purposes
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Results: Trip counts by purpose (representative day, 2011)
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Results: Traffic from simulation results
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northboundsouthbound
northbound southbound
Conclusions and future work
Conclusions
• ABM for transport demand and land use for Randwick
• Simulate interactions of population evolution, transport and land use
• Results fit observations BUT discrepancies for traffic density on small roads due to
– lack of data
– no dynamic routing in TRANSIMS router
Future work
• Simulate a larger area (Sydney) -> use of HPC
• Testing alternatives to TRANSIMS
• Improve accuracy assignment of OD
• Adding dynamics to discrete choice models
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