Transport models can help decision making by allowing testing of alternative solutions without costly real-world experiments. While models necessarily simplify reality, they provide a common basis for comparing solutions if their assumptions and limitations are understood. Uncertainty in forecasts grows over long time horizons due to errors in models, data, and inability to predict future scenarios perfectly. Improving models and data can reduce forecasting errors initially but may increase dependence on hard-to-predict future data. Maintaining interpretive skills is important alongside technical model-building skills.

1. ROLE OF TRANSPORT MODELLING
Luis Willumsen
How to use Transport Models to assist
decision making; assumptions and constraints
Managing Transport Modelling

2. BETTERFORECASTS
We use models to improve decision making
How to use transport models to assist decision
making.
• Better Models -> Better Forecasts
• Better Forecasts -> Better Decisions
• Short Term Decisions -> Better Accuracy
• Long Term Decisions -> Better basis for decision
making
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3. 3
• We cannot do experiments, in most cases
• Transport investments takes a long time so we
must envisage future conditions that do not yet
exist
• We can test alternative solutions in this modelled
future
Why are models required?

4. 4
Formulation of the problem
Test solutions in the model
Forecast underlying
(planning) variables
Generate solution packages
to test
Build, calibrate and
validate analytical model
Data collation and
collection
Model specification
Implement solutions
Evaluate solutions and
recommendations
Sensitivity and risk tests
How do we use models?

5. MODELSANDREALITY
The nature of Analytical Models
• Models are a simplification of reality based on some useful
theoretical assumptions and sufficient data to estimate
them
• We can deliver forecasts based on our models provided the
theoretical assumptions remain reasonable and data about
the future is reliable
• These conditions are never 100% met, so we need to
interpret the results from our models
• Even perfect models will not be able to deliver totally
accurate forecasts; some uncertainties cannot be eliminated
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6. Accuracy and precision
• Sometimes pursuing precision may lead to inaccuracy
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7. Accuracy and precision
• Sometimes pursuing precision may lead to inaccuracy
• It is better to be approximately right than precisely wrong
ONE SHOT
is not
enough!
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8. 8
MODELOBJECTIVES
 Assessing options
• Scheme testing
• Policy testing
 Assessing impacts
• Traffic and congestion
• Economic (user, non-user benefits)
• Financial (revenue)
• Environmental (CO2, NOX emissions), Safety (accidents)
• Performance
• Equity: winners and losers
 Forecasting usage (patronage, traffic levels etc.)
Where models can help

9. 9
MODELSPECIFICATION
 Need to understand and define the problem
 Have an idea of the possible solutions
 What are the main behavioural responses to these
solutions
 Resources Available: Time and Budgets, Data, Software,
Skills
 The costs of “getting it wrong”
 Important to identify what the model should be able to
do
 And what the model will NOT be able to do
 Interpretation and post modelling adjustments are fair
How to specify a Model

10. 10
MODELDESIGNSTEPS
 Scope of the model
 Area to be covered
 Behavioural responses to be included
 Government standards (WebTag, etc.)
 Data available
 Data to be collected
 Calibration/validation to be conducted of various stages
 Sensitivity Tests to be carried out
 The resources spent in modelling and analysis should be
related to the cost of a wrong decision
Model design steps

11. 11
Source: ptv
AREATOBECOVERED
Directly related to the scope of
the model
Also related to the
understanding travel patterns
Type of study, strategic or
tactical
How much detail is needed for
route choice and other
responses
Area to be covered and level of detail

12. 12
RESPONSES
• Typical 4/5 stage model
response. Change in:
• Number of trips
• Destination
• Mode used
• Time of travel
• Route used
• Generally
• Small area models with
network issues –route
choice and assignment
only
• PT models – Mode Choice
very important
• Strategic Models – All
responses
Activity System
Networks and
services
Trip Generation/Attraction,
trip frequency choice
Destination choice
Mode choice
Route choice and
assignment
Flows by link and mode
Travel costs, especially
time
Time of departure
choice
Responses to include

13. 13
DATAAVAILABILITY
Data is the key in any model development task
Each response model requires different data; always look for
what is already available
Previous studies, Census data, National Database, etc.
If no other study specific data is available then essential to
carry out surveys, most commonly:
Traffic Counts, Bus, Rail, Metro occupancy surveys/boarding-alighting
counts, Intercept surveys: Road Side Interviews and PT user OD
surveys, Travel time and service quality surveys
A rich source of travel user behaviour is a detailed Household
Interviews (HHI) or Household Travel Surveys (HTS) including a
Travel Diary; generally a 1% to 2% sample.
Data availability

14. New sensors and probes:
• Radar, loops
• CCTV
• ANPR
• SmartCards
• Transponders/Tags
• Bluetooth
• WiFi & Others
• GPS
• Mobile phones
The future is BRIGHT with new sources of mobility data
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15. 15
ESTIMATION,CALIBRATIONANDVALIDATIONOFMODEL
Three distinct aspects of model design:
• Calibration of a model is finding the best values for its
parameters
• Model estimation reflects the fact that the functional form
is not decided before hand in some cases
• Model Validation follows post estimation and tests the
model against data not used in calibration; backcasting is
ideal but seldom possible
Means different things for different response models
Demand model elements: total demand, Trip Length Distribution, mode
shares: compared against observed totals
Assignment model: Traffic flows, Boarding Alighting, Patronage
Calibration, Estimation and Validation

16. DATAERRORS
Sources of error: Data and ……. Models
• Base Year Data errors: Poor and small sample, limited data
for calibration/validation;
• Better methods and QA would reduce these errors
• Future Data errors in the input variables are unavoidable;
typically
• GDP, Population, employment Growth
• Key inputs like fuel prices, fares of competing modes
• Parameter changes: willingness to pay, Values of Time; fixed
tastes assumptions
• And also future Scenarios (not entirely under control)
• New Developments that do/do not materialise
• Competing facilities that DO materialise
• New disruptive technologies
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17. NZGDP
New Zealand GDP forecasts
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18. SPECIFICATIONERRORS
Sources of error: Model specification
• Bad modelling
• Poor sensitivity to price, too aggregate Values of Time
• Poor models of delay, crowding
• Ignoring relevant behavioural responses
• Including irrelevant behavioural responses
• Overconfidence in the model and its implicit assumptions
(that are never 100% right!):
• Users have perfect information and can process it efficiently
• Users respond instantly to changes
• Etc….
• Over specifying the model: unrealistic precision
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19. Consider a modelling effort with 30 years planning horizon
 Base year data is obtained the usual way (2-3 % HH survey
plus some intercept, travel time and service surveys plus
counts);
 Model is calibrated/estimated for year 0;
 …and then applied for future periods, say every 10 years;
 Using projected planning data (GDP, population,
employment, etc. allocated over time and space);
 ..and some assumptions about changes in the technologies,
networks, competitors, economic regeneration, etc.
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20. ERRORSINFORECASTS
Sources of errors in forecasts
 The model. It is simplified, ignores some behavioural
responses, network is not perfect, assumes constant
parameters, etc. It is likely to be even more uncertain as time
progresses
 Base year data: small sample, on certain days, blended with
distribution/destination choice models to produce semi-
synthetic matrices: declining influence
 Future year data: uncertainty grows over time horizon; gets
more uncertain the more granular (disaggregated) it is:
increasing influence
 Scenario uncertainty: the elements we do not influence and
cannot fully predict: competitors in a PPP, success of land
use planning and control, policy changes, disruptive
technologies: increases over time
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21. FORECASTINGERRORS
Sources of errors in forecasts
Uncertainty due to model quality
Base Year
Data
Future data
Scenario Uncertainty
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
NoonalUncertaintyIndex
Years a er forecast
No onal sources of uncertainty in forecas ng
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22. BETTERDATAANDMODEL
With improved Base Year Data and Model
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
NoonalUncertainty
Years a er original forecast
No onal sources of uncertainty a er improvements in data and model
Be er model Be er data Fut. data a er Scenario a er Original model Data Future data Scenario
Scenario uncertainty
Future data uncertainty
Be er model
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23. IMPACTOFDATAREQUIREMENTS
Model with more disaggregated data requirements
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
NoonalUncertainty
Years a er original forecast
No onal sources of uncertainty a er improved model requiring more data
Be er model Be er data Fut. data a er Scenario a er Original model Data Future data Scenario
Scenario uncertainty
Greater future data requirements
Be er model
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24. MODELSANDEQUILIBRIUM
A model is always a simplification of reality
Some simplifications make our life easier, others are essential
Take Route Choice and Assignment
We model it as a steady state phenomenon: all vehicles clear
the network during the modelled (peak) period.
The software finds a solution with an iterative process until
“equilibrium” is reached:
Wardrop’s Equilibrium:
Under equilibrium conditions traffic arranges itself in congested
networks in such a way that no individual trip maker can reduce
his path costs by switching routes
Certain conditions with respect of the relationship between
flow and travel time must be met to achieve this equilibrium
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25. Issues
• If equilibrium of routes and costs is not reached the flows
(and costs) depend on when do we stop the iterative process
• This may affect our choice of scheme
• However, reality is never in “equilibrium”
• Noise in traffic (empty taxis, delivery vehicles, cruisers, lost)
• Noise in the network: stalled vehicles, rain, poor light, roadworks
• Day-to-day variability in the trip matrices
• Moreover, the better we represent delays at junctions, the
more difficult (and sometimes impossible) it is to ensure
“equilibrium”.
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26. EQUILIBRIUM
Equilibrium Activity System
Networks and
services
Trip Generation/Attraction,
trip frequency choice
Destination choice
Mode choice
Route choice and
assignment
Flows by link and mode
Travel costs, especially
time
Time of departure
choice
There is a risk of oscillations
in the complete model for
future runs:
1. A lot of congestion, shift
to PT
2. Less congestion, back to
car
3. A lot of congestion, shift
to PT.
4. Etc….
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27. 10MESSAGES
Ten messages to take with you
1. You may outsource the Model
but not the Planning/Decision
Making
2. Engage in Model Specification,
Develop/retain capacity to
interpret results, protect and
transfer acquired knowledge
3. In specifying the model focus
on What Matters; do not aim
for the academic ideal
4. Decide how will you deal with
the inevitable uncertainties
5. What assumptions were made
in the model build? What
assumptions are made about
the future in the application?
6. Do results make sense? Check
model results by other means (e.g.
back of envelope calculations)
7. The model will be audited: good
documentation & transparency
essential; better if peer-reviewed
throughout
8. Model results will be used in a
wider context and by other
disciplines; avoid intellectual
imperialism
9. Is the model WebTAG compliant?
If not, does it matter and how
does it impact its reliability?
10. Has the model been validated
through alternative techniques to
WebTAG, e.g. benchmarking?
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28. ROUNDUP
Round up
a. Models can be very useful tools to support decision making
b. Resources devoted to analysis should be aligned to the cost
of poor decisions
c. Government guidance often drive model specification
d. Models and their application, however validated, are not
error free
e. There are significant risks in over-specifying a model: delays,
spurious accuracy and excessive faith
f. But they can be very useful to compare solutions on a
common basis and eventually under alternative scenarios
g. The interpretation of model results requires understanding
what they include and what they leave out
h. …and judgment based on experience
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29. THANK YOU
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