1 Activity Based Models Review Thomas Rossi Krishnan Viswanathan Cambridge Systematics Inc. Model Task Force Data Committee October 17, 2008

2 Presentation Overview Study Background and Objectives Models Studied Study Findings Data for Activity Models Discussion

3 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

4 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

5 Models Studied Statewide Models – Ohio Model (2007) – Oregon Model Research Models – FAMOS (University of South Florida) – CEMDAP (University of Texas) – TASHA (University of Toronto)

6 Models Studied SFCTA New YorkColumbusSacramento Lake TahoeAtlantaPortlandDenver San Francisco (MTC)OhioOregon Year Completed (est.) 2009 (est.) (est.) Base Year Forecast Year , 2050 Survey Data Year No Survey Number of Households in Survey 1,30011,0005,6003,9001,2208,1006,0004,90015,000 No Survey Zones (approximate)1,700 (750 in SF) 3,6001,8001, ,000 2,8001,4545,3003,000 Area Size (square meters) 50 (SF only) 150 (est.)4, ,000 Base Year Population750,000 (SF only) 1,500,0002,000,00063,4484,700,0001,600,0006,783,760

7 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

8 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

9 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

10 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

11 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

12 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

13 Use of Survey Data in Activity Models 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) Trip-based Approach Activity-based Approach Acknowledgment : Siva Srinivasan, University of Florida

14 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

15 Use of Survey Data in Activity Models Work Based Subtour Home Based Tour 7:30 AM 8:00 AM 12:00 PM 1:00 PM 5:30 PM 5:45 PM 6:00 PM 6:15 PM 6:30 PM Convert Trip data to Tours

16 Discussion