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An Activity-Based Model for Cube Voyager

Agenda• Background & motivation• Structure of the model• Scripting features• Application case study

• One (extremely common) method of forecasting travel demand

• Trip ends (productions and attractions) are generated based upon socio-economic and demographic factors

• These are distributed between zones based upon aggregate travel costs

• Logit models are used to split person trips between different travel modes

• Trips by mode are factored by time of day and assigned to specific network paths

• Modern versions of this process feedback costs from assignment to earlier steps

The Four-Step Modeling Process

Trip Generation

Trip Distribution

Mode Choice

Network Assignment

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

householdSpatial 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 a few dimensionsTemporal 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

Activity Based ModelsEarly recognition that travel is a derived demand

• derived from a person’s desire to engage in activities that are spatially separated

• Focus of the model should be on the underlying behavior: What people want to do, not where people want to go

Early attempts at implementing tour based models• San Francisco Bay Area, The Netherlands, Boise Idaho, Stockholm, New

Hampshire, Italy

Current implementations of activity-based travel demand model systems

• Portland OR, San Francisco County CA, New York City, Columbus OH, Atlanta, San Francisco Bay Area (MTC)

Auto Ownership Model

Activity Day-Pattern Choice

Tour Generation & Time-of-Day

Joint Mode/Destination Choice

• Alternative to four-step modeling approach popular in the academic transportation research community and becoming more common in practice (although still less than FSM)

• Disaggregate simulation using synthetic populations based upon micro-data

• Complete tours, or chains of trips, are analyzed, rather than 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

Motivation for the WorkWhat?• An activity-based microsimulation model

implemented completely in Cube Voyager scripting language (no external code)

Why?• A learning tool for (potential) model users• A forecasting tool for small/medium cities• A “test bed” for model developers

• Most existing activity-based models are custom programs written by consultants 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:• Intelligible 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 transferrable, not agency- or consultant-specific

Why Cube Voyager instead of ______?

The Model System Structure1. Population synthesizer2. Zonal accessibility measures3. Activity and travel simulator4. Travel aggregator5. Traffic assignment6. Feedback loop / equilibration

For background on theoretical development of model structure see:

Bowman, John L. and Mark A. Bradley (2005) Disaggregate treatment of purpose, time of day and location in an activity-based regional travel forecasting model, European Transport Conference, October 2005, Strasbourg, France.

• Uses household and person records 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 marginal distribution in each TAZ

Simple Population Synthesizer

• 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

Aggregate mode/destination choice logsums:• 3 travel purposes

• Work (total employment)• School (K-12 enrollment)• Other (retail employ. + service

employ./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 SOV

available

Zonal accessibility measures

Main loop on householdsHousehold car ownership modelLoop on people in household

Full day tour/trip activity pattern choiceLoop 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 modelTrip mode choice (usually same as tour mode)Write trip record (with tour, person and HH info)

Activity and Travel Simulator

Aggregates records to create trip matrices 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

• Uses CUBE Voyager highway assignment

• 3 separate assignments: AM peak, Midday, PM peak

• Off-peak LOS uses uncongested speeds.

Traffic assignment

• 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 data for similar zones• Ran 4 full iterations with assignment

Initial application in “Cubetown”

• 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

Get it at www.citilabs.com/tutorials.html

Model Catalog

Tutorial

ConclusionsBack to our motivations…• A learning tool for (potential) model users• A forecasting tool for small/medium MPOs• A “test bed” for model developers

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

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 with

Cube 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