Model Task Force Data Committee

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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 Sacramento Lake Tahoe Atlanta Portland Denver San Francisco (MTC) Ohio Oregon
Year Completed 2001 2002 2005 2007 2007 2008 (est.) 2008 (est.) 2008 (est.) 2009 (est.) 2007 2008 (est.)
Base Year 2000 1996 2000 2005 2000 2000 2005 2000
Forecast Year 2020 2030 2035 2030 2035 2030, 2050
Survey Data Year 1990 1998 1999 2000 2001 1994 1997 2000 2003 No Survey
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
Zones (approximate) 1,700(750 in SF) 3,600 1,800 1,500 289 2,000 2,000 2,800 1,454 5,300 3,000
Area Size (square meters) 50 (SF only) 150 (est.) 4,000 501 500 7,000
Base Year Population 750,000(SF only) 1,500,000 2,000,000 63,448 4,700,000 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
  CUBE-Voyager/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
• Trip-end locations (TAZs)
• Trip mode
• Time-of-day of Travel

• Activities undertaken
• Time-of-day of activity/travel episodes
• 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
800 AM
730 AM
1200 PM
100 PM
630 PM
530 PM
615 PM
600 PM
545 PM
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Discussion