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Greater Brisbane Transport
Demand Model (BNE Model)
An Overview
Benjamin H Pool
For: AITPM National Conference, Melbourne, August 2017
2 |2 |
Our values, our diversity
3 |3 |
Queensland
Government’s
objectives for the
community
Advance Queensland
4 |
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
AITPM 2017 National Conference, Melbourne | 17 August 2017
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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
AITPM 2017 National Conference, Melbourne | 17 August 2017
8 |8 |
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
AITPM 2017 National Conference, Melbourne | 17 August 2017
9 |9 |
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
AITPM 2017 National Conference, Melbourne | 17 August 2017
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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
AITPM 2017 National Conference, Melbourne | 17 August 2017
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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
AITPM 2017 National Conference, Melbourne | 17 August 2017
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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
AITPM 2017 National Conference, Melbourne | 17 August 2017
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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
AITPM 2017 National Conference, Melbourne | 17 August 2017
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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
AITPM 2017 National Conference, Melbourne | 17 August 2017
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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
AITPM 2017 National Conference, Melbourne | 17 August 2017
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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
Time, Distance & Cost Skims
convergence met or loop count
Post
Processing
Final
Assignment
Mode Choice, Time of Day,
Destination Choice Generation
Assign to convergence
Time, Distance & Cost Skims
AITPM 2017 National Conference, Melbourne | 17 August 2017
17 |17 |
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
Internally consistent
AITPM 2017 National Conference, Melbourne | 17 August 2017
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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
AITPM 2017 National Conference, Melbourne | 17 August 2017
19 |19 |
Road Screenline Calibration
• Road Screenlines
AITPM 2017 National Conference, Melbourne | 17 August 2017
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• PT Screenlines
AITPM 2017 National Conference, Melbourne | 17 August 2017
PT Screenline Calibration
21 |21 |
Segment Calibration
• Observed vs Modelled Demand Comparison
AITPM 2017 National Conference, Melbourne | 17 August 2017
22 |22 |
Demand based criteria
Calibration and Validation is sound
Evidence based futures
Internally consistent
Soft Launch participants satisfied
First Discrete Choice transport demand model in Australia
Suitable for use?
AITPM 2017 National Conference, Melbourne | 17 August 2017
23 |23 |
Greater Brisbane Transport Demand Model
Questions?
AITPM 2017 National Conference, Melbourne | 17 August 2017
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VDF Plot form
• Modified BPR form of Volume Delay Function
AITPM 2017 National Conference, Melbourne | 17 August 2017
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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
AITPM 2017 National Conference, Melbourne | 17 August 2017