transport modelling for managers 2014 willumsen
DESCRIPTION
Short introduction to Transport Modelling for Managers.TRANSCRIPT
ROLE OF TRANSPORT MODELLING
Luis Willumsen
How to use Transport Models to assist decision making; assumptions and constraints
Managing Transport Modelling
BETTER FORECASTS
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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• 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?
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Formulation of the problem
Test solutions in the model
Forecast underlying(planning) variables
Generate solution packagesto test
Build, calibrate andvalidate analytical model
Data collation andcollection
Model specification
Implement solutions
Evaluate solutions andrecommendations
Sensitivity and risk tests
How do we use models?
MO
DELS AN
D REALITY
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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Accuracy and precision
• Sometimes pursuing precision may lead to inaccuracy
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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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MO
DEL O
BJECTIVES
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
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MO
DEL SPECIFICATIO
N
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
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MO
DEL D
ESIGN
STEPS
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
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Source: ptv
AREA TO BE CO
VERED
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
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RESPON
SES
• 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 SystemNetworks and
services
Trip Generation/Attraction,trip frequency choice
Destination choice
Mode choice
Route choice andassignment
Flows by link and modeTravel costs, especially
time
Time of departurechoice
Responses to include
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DATA AVAILABILITY
Data is the key in any model development taskEach 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
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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ESTIMATIO
N, CALIBRATIO
N AN
D VALIDATIO
N O
F MO
DEL
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 modelsDemand model elements: total demand, Trip Length Distribution, mode
shares: compared against observed totalsAssignment model: Traffic flows, Boarding Alighting, Patronage
Calibration, Estimation and Validation
DATA ERRORS
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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NZ G
DP
New Zealand GDP forecasts
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SPECIFICATION
ERRORS
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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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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ERRORS IN
FORECASTS
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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FORECASTIN
G ERRO
RSSources of errors in forecasts
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BETTER DATA AND
MO
DEL
With improved Base Year Data and Model
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IMPACT O
F DATA REQU
IREMEN
TSModel with more disaggregated data requirements
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MO
DELS AN
D EQ
UILIBRIU
MA model is always a simplification of reality
Some simplifications make our life easier, others are essentialTake 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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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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EQU
ILIBRIUM
Equilibrium Activity SystemNetworks and
services
Trip Generation/Attraction,trip frequency choice
Destination choice
Mode choice
Route choice andassignment
Flows by link and modeTravel costs, especially
time
Time of departurechoice
There is a risk of oscillations in the complete model for future runs:1. A lot of congestion, shift
to PT2. Less congestion, back to
car3. A lot of congestion, shift
to PT.4. Etc….
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10 MESSAG
ES 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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ROU
ND
UP
Round up
a. Models can be very useful tools to support decision makingb. Resources devoted to analysis should be aligned to the cost
of poor decisionsc. Government guidance often drive model specificationd. Models and their application, however validated, are not
error freee. There are significant risks in over-specifying a model: delays,
spurious accuracy and excessive faithf. But they can be very useful to compare solutions on a
common basis and eventually under alternative scenariosg. The interpretation of model results requires understanding
what they include and what they leave outh. …and judgment based on experience
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THANK YOU
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