Transcript ABM Data Needs

Model Task Force Data Committee
October 17, 2008
Activity Based Models Review
Thomas Rossi
Krishnan Viswanathan
Cambridge Systematics Inc.
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Presentation Overview

Study Background and Objectives

Models Studied

Study Findings

Data for Activity Models

Discussion
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Study Background and Objectives

Examine existing activity based models to determine model features,
application procedures, and requirements

Determine planning analysis needs for which travel models are used

Summarize the ability of activity based models to provide accurate
information for planning analysis needs
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Models Studied

Urban Models
– San Francisco County, CA (2001)
– New York, NY (2002)
– Columbus, OH (2005)
– Sacramento, CA (2007)
– Lake Tahoe, NV/CA (2007)
– Atlanta, GA
– Portland, OR
– Denver, CO
– San Francisco Urban Area (MTC), CA
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Models Studied

Statewide Models
– Ohio Model (2007)
– Oregon Model

Research Models
– FAMOS (University of South Florida)
– CEMDAP (University of Texas)
– TASHA (University of Toronto)
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Models Studied
SFCTA
New
York
Columbus
Year Completed
2001
2002
Base Year
2000
Forecast Year
Denver
San
Francisco
(MTC)
Ohio
Oregon
2008 (est.)
2009 (est.)
2007
2008 (est.)
2000
2005
2000
2035
2030
2035
2030, 2050
2001
1994
1997
2000
2003
No Survey
Sacramento
Lake
Tahoe
Atlanta
Portland
2005
2007
2007
2008 (est.)
2008 (est.)
1996
2000
2005
2000
2020
2030
Survey Data Year
1990
1998
1999
2000
Number of Households in
Survey
1,300
11,000
5,600
3,900
1,220
8,100
6,000
4,900
15,000
15,000
No Survey
1,700
(750 in SF)
3,600
1,800
1,500
289
2,000
2,000
2,800
1,454
5,300
3,000
Zones (approximate)
Area Size
(square meters)
50
(SF only)
150 (est.)
4,000
501
Base Year Population
750,000
(SF only)
1,500,000
2,000,000
63,448
4,700,000
500
7,000
1,600,000
6,783,760
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Study Findings – Model Structure

Individuals simulated

Model structure
– Generate daily activity patterns
– Location, time and mode made at two levels : Tour and Trip

Five to eight activity purposes
– work, school, shop, meal, social/recreation, and personal business

Some models consider household interactions
– Evidence regarding forecasting effectiveness mixed when compared to costs
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Study Findings – Model Components

Population Synthesizer

Long Term Choice Models
– Auto ownership
– Usual workplace location

Daily Activity Pattern Models

Tour Level Models (primary activity)

Trip Level Models (intermediate stops)

Trip Assignment
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Study Findings – Model Development Process

Model development between 1.5 to 8 years

Consultants used for model development

Most models used local household activity survey data along with
other sources such as transit on-board, external or visitor surveys

Lake Tahoe model was transferred from Columbus
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Study Findings – Model Execution

Standard transportation modeling software such as CUBEVoyager/TP+, TransCAD along with custom programs in C++, Java
and Python used

Run times range from 10 hours to 2 days
– Distributed computing preferable to reduce runtime

Models need around 7 to 10 GB of storage per run
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Study Findings – Policy Planning Analysis

Activity Based Models benefit the following types of analysis
– Congestion Management Systems
– Toll Feasibility Studies
– High-Occupancy Vehicle (HOV) Lane Studies
– New Starts/Small Starts Analyses
– Hurricane Evacuation Modeling Support
– Air Quality Conformity Determinations
– Integrated Land Use Model
– Incorporate Ability to Test Impact of Gasoline Prices
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Study Findings – Data Needs

No special data needs required to develop activity based models
beyond what is used for four-step models

Existing household travel surveys can be used to develop data for
activity based models

Other data sources such as transit on-board surveys, external and
visitor surveys are also vital for activity based models

Census data sources such as PUMS useful for population synthesis
– ACS disclosure rules can be problematic
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Use of Survey Data in Activity Models
Trip-based Approach
Activity-based Approach
 Number of trips by purpose
 Activities undertaken
 Trip-end locations (TAZs)
 Time-of-day of activity/travel
episodes
 Trip mode
 Time-of-day of Travel
 Duration of activity/travel episodes
 Locations of activity episodes
 Temporal sequencing (Trip
chaining / tour formations)
 Tour and trip modes
 Intra-household interdependencies
(task allocation and joint
travel/activities – not used in all
models)
Acknowledgment : Siva Srinivasan, University of Florida
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Use of Survey Data in Activity Models

Household and Person characteristics from Household surveys
– Age; gender; employment; drivers license
– Household size; vehicle ownership; household income; resident type

Zonal data from MPOs/DOTs
– Population density; industry employment; land use characteristics

Skim data from model network and MPOs/DOTs
– Travel time; fare; distance; transfers
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Use of Survey Data in Activity Models
Convert Trip data to Tours
Work Based Subtour
Trip 1 Car,
subway, walk
Work
8:00 AM
7:30 AM
Home Based
Tour
Trip 2
Walk
12:00 PM
Home
1:00 PM
Trip 4 Walk
Subway, car
6:30 PM
Trip 7
Car
Trip 3
Walk
Lunch at
Restaurant
Gas
Station
Daycare
center
5:30 PM
6:15 PM
Trip 6
Car
Trip 5
Car
6:00 PM
Grocery
store
5:45 PM
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Discussion
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