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

Google

northbound southbound

Google

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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