Greater Brisbane transport demand model

1 |

Greater Brisbane Transport

Demand Model (BNE Model)

An Overview

Benjamin H Pool

For: AITPM National Conference, Melbourne, August 2017

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Our values, our diversity

3 |3 |

Queensland

Government’s

objectives for the

community

Advance Queensland

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Our strategic plan

5 |5 |

About us…

3,029bridges

20ports

3,260taxis licensed

5mvehicles registered

3.5mdrivers licensed

256,151recreational vessel

registrations

997,289boat licenses

180min SEQ

12.1moutside SEQ

trips taken annually on bus,

rail, ferry and light rail

33,343kmstate-controlled roads

As at 30 June 2016 we manage: As at 30 June 2016: As at 30 June 2016 there were:

3.63mcustomers served

face-to-face at

59Customer Service Centres

2.5mgo cards

in use

Over 490,000passengers travel on the

south-east Queensland

network on average

each day Our customers conducted

6.68monline services

Creating a single integrated transport network accessible to everyone

AITPM 2017 National Conference, Melbourne | 17 August 2017

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• Demand

• Network

• Sub Models

• Calibration Validation

Greater Brisbane Transport Demand Model

Persons, Purpose Choice

Activity / Access Choice

Available Facilities(Network, Services, Policy)

Suitable for Use

• Determined by Individuals

• Influenced by Facilities


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• Gravity models have fewer parameters

& tend to consider only “how much”

Form: Discrete Choice vs Gravity

$

$

$

• Discrete Choice models have a high level of complexity

and consider choices that are different or unique from

each other. Discrete choice models are developed to also

consider, “which one” across a range of variables


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Contrast: BNE vs. BSTM-MM

Converge to zonal

demand by period

More full use of HTS

Finer person/purpose

segmentation

Discrete choice form

provides sensitivity

Time, Distance, Cost

Peak & Period spreading

Internally consistent

Converge to daily

assignment road statistic

Less full use of HTS

Aggregate form reduces

segmentation

Singular gravity form is

insensitive

Generalised cost

No time choice

Prone to externalities


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Demand Segments

• 8 person types by 5

purposes

• Informed by network

conditions

• Singly constrained

• NHB trips reference

home based trip leg

• Adjustment for under-

reporting (discretionary)

State School

High School

University

Blue Collar

Worker

White Collar

WorkerAdult 18-54

Adult 55-74

Adult 75+

Home to

Primary

Primary to

Home

Home to

Secondary

Secondary

to Home

Non-Home

based OtherHome


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Demography

• Household based to Person-based

• SA1 Zones

• Zone consistency (in/out, land use,

greenfield development)

• Futures Demography defined by

TMR

• Informed by ABS Census & QGSO

(Queensland Government Statistics

Office)

MANY MORE

dwellings to

2041


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Networks

• Assign informs Demand model

– Time, Distance, Cost

• Facilities, lanes & bendiness

• Active modes, on & off road

• Light commercial vehicles (LCV)

• Network futures defined by TMR

QTRIP, TIPPS, financial constraints

S0.5 1.0 1.5 2.0 km


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Volume Delay Functions

• Modified BPR Volume Delay Function (VDF)

• Considers link end control:

Freeflow, Giveway, Signal

• Multiple (capacity based) VDF values

for each control type

• Give-way and signal inherent delay

• All VDF attributes internal to model

supports model being internally consistent


13 |13 |

Parking Model & Toll Model

• Logit Toll Model determines toll or no-toll

routing choice

• Time savings, average (zonal) income and

toll cost

• Logit attributes internal to model –

supports model being internally consistent

• Two car classes; do (ct) or do’t (c) use toll

• Logit Parking Model - focus on Central Traffic Area

• Capacity constrained across 4 parking types

• Remote park and walk to destination

P


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Public Transport

• PnR Lot capacity formal & informal parking

• PnR parking period accumulations

• KnR no capacity constraint

• Fares - concessions, peak/offpeak

• PT Patronage calibrated to

screenlines, board/alight


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Demand Model Process

At the highest model level, a daily composite utility of travel can be calculated

Logsums up

Composite utilities from

nested logit formulation

estimated at each stage

and fed upward

Probabilities down

Demand estimates

made using these

composite utilities

feeding down


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Overall Process

Assign to convergence

Time, Distance & Cost Skims

Seed

matricesMode Choice, Time of Day,

Destination Choice Generation

Demand

Trip Tables

Convergence Check

Trip Tables Previous vs Current

FEED

BACK

Big LoopNew Demand matrix via

variable blending to

¾ previous ¼ current convergence not met

convergence

met

Small LoopNew Demand matrix via

variable blending to

¾ previous ¼ current not met

convergenceConvergence Check

Trip Tables Previous vs Current

Mode Choice,

Time of Day

Demand

Trip Tables

Assign to Convergence


convergence met or loop count

Post

Processing

Final

Assignment

Mode Choice, Time of Day,

Destination Choice Generation

Assign to convergence



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Convergence Criteria

• Demand Based

• <1% mean absolute (mabs) difference in car trip demand

matrices between iterations on an zonal basis for all

scenarios

• Variable blending: an efficient convergence process

• Final loop

• Demand in is consistent with demand out

Both practically and theoretically sound



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Assignment Calibration

• Screenline r2 & Y values Road and PT

R²

(>0.85)

y

(0.9x - 1.1x)

R²

(>0.85)

y

(0.9x -

1.1x)

AM6 0.976 0.911 Y 0.848 0.932 N

AM7 0.983 0.999 Y 0.86 1.006 Y

AM8 0.985 1.172 N 0.87 1.153 N

IP 0.982 0.899 Y 0.889 0.945 Y

PM3 0.983 0.921 Y 0.899 0.941 Y

PM4 0.984 0.928 Y 0.9 0.956 Y

PM5 0.983 1.066 Y 0.884 1.076 Y

RD 0.984 0.902 Y 0.9 0.966 Y

Daily (aggregated)0.989 0.95 Y 0.94 1.001 Y

Road Screenline Counts Road Individual Counts

R²

(>0.85)

y

(0.9x - 1.1x)

R²

(>0.85)

y

(0.9x -

1.1x)

AM6 0.962 2.718 N 0.884 2.433 N

AM7 0.992 1.508 N 0.905 1.495 N

AM8 0.956 0.799 N 0.924 0.832 N

AM peak Total 0.985 1.284 N 0.92 1.235 N

IP 0.993 0.757 N 0.957 0.657 N

PM3 0.951 1.565 N 0.915 1.108 N

PM4 0.975 1.183 N 0.893 0.972 Y

PM5 0.972 1.086 Y 0.926 1.808 N

PM peak Total0.973 1.218 N 0.924 1.322 N

RD 0.871 0.661 N 0.933 0.88 N

Daily (aggregated)0.99 1.143 N 0.943 1.044 Y

Rail Screenline Counts Bus Screenline Counts


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Road Screenline Calibration

• Road Screenlines


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• PT Screenlines


PT Screenline Calibration

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Segment Calibration

• Observed vs Modelled Demand Comparison


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Demand based criteria

Calibration and Validation is sound

Evidence based futures


Soft Launch participants satisfied

First Discrete Choice transport demand model in Australia

Suitable for use?


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Greater Brisbane Transport Demand Model

Questions?


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VDF Plot form

• Modified BPR form of Volume Delay Function


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Trip Distance by Segment

• Observed vs Modelled Trip Distance by Segment

Secondary Student Home to School

75+ Home to Other

Adult Worker WC Home to Work

Adult Worker BC Home to Work


Greater Brisbane transport demand model

Education

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