1_6_michael clarke_futura oceania moving to activity modeling
TRANSCRIPT
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An Activity-Based Model forCube Voyager
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Agenda
Background & motivation
Structure of the model
Scripting features Application case study
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One (extremely common) method of forecastingtravel demand
Trip ends (productions and attractions) aregeneratedbased upon socio-economic anddemographic factors
These are distributedbetween zones basedupon aggregate travel costs
Logit models are used to split person tripsbetween different travel modes
Trips by mode are factored by time of day andassigned to specific network paths
Modern versions of this process feedback costsfrom assignment to earlier steps
The Four-Step Modeling Process
TripGeneration
TripDistribution
Mode
Choice
NetworkAssignment
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With person-trips as the unit of analysis:
No interactions between trips made in the same trip chain
No interactions between trip chains made during the same day
No interactions between the trips made by people in the same household
Spatial aggregation of trips: Trip origins and destinations modeled as if they are located at the same point in
space
Demographic aggregation:
All households within a given zone are treated as identical or segmented along afew dimensions
Temporal aggregation:
Only a few periods of the day are considered
Proportion of trips made in each period treated as constant
Limitations of Trip Based Models
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Activity Based Models
Early recognition that travel is a derived demand derived from a persons desire to engage in activities that are spatially
separated
Focus of the model should be on the underlying behavior: What peoplewant to do, not where people want to go
Early attempts at implementing tour based models
San Francisco Bay Area, The Netherlands, Boise Idaho, Stockholm, NewHampshire, Italy
Current implementations of activity-based travel demand model systems in
USA Portland, San Francisco, New York City, Columbus, Atlanta
Underway: Baltimore, Jacksonville, Chicago, San Diego
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Auto OwnershipModel
Activity Day-Pattern Choice
Tour Generation& Time-of-Day
JointMode/Destination
Choice
Alternative to four-step modeling approach
Popular in the academic transportation researchcommunity and becoming more common in practice(although still much less than 4-step)
Disaggregate simulation using synthetic populations
based upon micro-data
Complete tours, or chains of trips, are analyzed, ratherthan individual trips
e.g. Home > Work > Shop > Home
Activity location and scheduling models
Mode choice applies to entire tour
Ideally suited for dynamic traffic assignment and meso-simulation
Activity and Tour Based Modeling
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Motivation for the Work
What?
An activity-based microsimulation model implemented completely in CubeVoyager scripting language (no external code)
Why?
A learning tool for (potential) model users
A forecasting tool for small/medium applications
A test bed for model developers
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Most existing activity-based models are custom programs written byconsultants in third-party programming languages
Examples: Java, C++, C#, Python, R
Steep learning curve to develop & maintain
Relatively difficult to scale the model to match resources
Using Voyager instead offers significant advantages:
Understandable to non-programming modelers = easy to learn & use
Easily scalable (using Cube Cluster for distributed processing)
Data models, not object models = less complex code Model structure is transferable, not agency- or consultant-specific
Why code in a Cube Voyager script?
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The Model System Structure
1. Population synthesizer
2. Zonal accessibility measures
3. Activity and travel simulator
4. Travel aggregator
5. Traffic assignment
6. Feedback loop / equilibration
For background on theoretical development of model structure see:
Bowman, John L. and Mark A. Bradley (2005)Disaggregate treatment ofpurpose, time of day and location in an activity-based regional travelforecasting model, European Transport Conference, October 2005,
Strasbourg, France.
http://jbowman.net/papers/2005.Bowman_Bradley.Disaggr_treatment_of_purp_time_loc.pdfhttp://jbowman.net/papers/2005.Bowman_Bradley.Disaggr_treatment_of_purp_time_loc.pdf -
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Uses household and personrecords from PUMS 5%microdata
Uses Census Table CTPP1-75,by TAZ
HH size(1, 2, 3, 4+)
HH income
(0-15K, 15-30K, 30-50K, >75K) Draws households randomly
from PUMA to match marginaldistribution in each TAZ
Simple Population Synthesizer
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6 highway variables
SOV distance, time and toll
HOV distance, time and toll
5 transit variables
Walk access/egress time
First wait time
Transfer time
In-vehicle time
Fare
4 time periods AM peak, Midday, PM peak, Off-peak
Highway and transit networks
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Aggregate mode/destination choice logsums: 3 travel purposes
Work (total employment)
School (K-12 enrollment)
Other (retail employ. + serviceemploy./2)
4 times of day
AM peak, Midday, PM peak, Off-peak
2 directions
Traveling away from zone, returning to
zone 2 car availability situations
With SOV available, without SOVavailable
Zonal accessibility measures
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Main loop on households Household car ownership model
Loop on people in household
Full day tour/trip activity pattern choice Loop on tours in the day
Tour time of day choice models(both directions)
Tour main mode and destination choice
Loop on trips in the tour Intermediate stop location choice model
Trip mode choice(usually same as tour mode)
Write trip record(with tour, person and HH info)
Activity and Travel Simulator
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Aggregates records to create tripmatrices by:
4 time periods
AM peak, Midday, PM peak, Off-peak
4 modes
SOV, HOV, transit, walk
Flexible to allow other
breakdowns, e.g.: Separate assignment by income class
Travel Aggregator
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Uses CUBE Voyager highwayassignment
3 separate assignments: AM
peak, Midday, PM peak
Off-peak LOS uses uncongestedspeeds.
Traffic assignment
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Coded by Victor Siu & Ken Vaughn
Used the Cubetown demo networks
and zonal files
System of 25 zones and highway,
transit networks, based on an area of
Fargo, ND
Synthetic population of 70,006
persons, based on 1990 CTPP datafor similar zones
Ran 4 full iterations with assignment
Initial application in Cubetown
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15 min. per full iteration on 3 GHZ PC
Travel/activity simulator run time mainly proportional tosize of synthetic population
Traffic assignment proportional to square of number ofzones
4 full iterations with 500,000 people would take 7 hours +,depending on number of zones
Observation: script could be optimized to run faster
Initial Performance
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Re-implementation using DBI
Simple population synthesizer
Enhanced integration:
Cube Cluster
Model Catalog
Geodatabase inputs
GIS Mapping
Cube Reports
Added to 5.1 Cubetowndemonstration model set
2009 Update to Activity-Based Model
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Get it at www.citilabs.com/tutorials.html
Model Catalog
Tutorial
http://www.citilabs.com/tutorials.htmlhttp://www.citilabs.com/tutorials.html -
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Application Structure in Cubetown 5.1
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Use of DBI vs. RECI:
70-80% Runtime Reduction Factor (RRF)
Two cores vs. one:
55-65% RRF
Four cores vs. one:
30-40% RRF
Performance gains from additional cores will depend upon
structure and size of population, zone system
Update: Performance Improvements
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As a learning tool A quick and easy way to learn about the
properties of activity-based microsimuation
Sensitivity tests on a wide range of policies
Reporting on several levels and variables(network, trip, tour, person, household)
Practical context for advanced Cube Voyager functions
Further development
Further standardize data model & parameters
Explore benefits of multi-dimensional arrays in 5.1
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As a forecasting tool Provides many advantages over 4-step
The framework is feasible for small and medium-sized regions.
You can always integrate custom programs withCube Voyager (e.g. for large regions) if preferred
Further development
Calibrate and validate on region-specific data
Transfer to other regions(structure and many parameters should be transferable)
Continue to improve run-time performance
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Helping You to Create a Better Future