Travel Modeling at MTC David Ory Metropolitan Transportation Commission November 17 and 18, 2011 Presentation to Triangle Region Model Expert Panel

2 Image source: flickr.com/Michael Caven Day 1

3 Technical features. Data collection and management. Time frame for model development. Key questions.

4 Technical features. Data collection and management. Time frame for model development. Key questions.

 Space  1454 TAZs ~ Census tracts  Each TAZ includes three non-spatial activity sub-zones: short-walk to transit, long-walk to transit, cannot walk to transit – activities occur in one of these three sub-zones  Time  Activities are scheduled hourly, between 5 am and midnight  Roadway and transit supply is represented for five time periods: 3 am to 6 am; 6 am to 10 am; 10 am to 3 pm; 3 pm to 7 pm; 7 pm to 3 am  Creation of agents  ARC Population Synthesizer (w/ 304 household-level control categories in the base year)  Land use  Association of Bay Area Governments  UrbanSim and PECAS models under-development 5

6 Worker or student status (from census) Google SF CBD Two cars Work and school locations selected Zone X Household auto ownership level chosen Not work Work School Not school Daily activity patterns chosen jointly 1 Tour Mandatory tours generated

7 7 to 7 Mandatory tours are scheduled 8 to 4 2 Joint Tours 1 At-work Tour 1 Non-mandatory Tour 2 Joint Tours Non-mandatory travel is generated Destinations for non- mandatory tours are selected Zoo, Market McDonald’s Jimmy’s House Zoo, Market 8 to 11; 3 to 4 12 to 1 5 to 6 8 to 11, 3 to 4 Non- mandatory tours are scheduled

8 Drive to zoo; walk to market Drive to work; drive to lunch Transit to school; bike to Jimmy’s Ride to zoo; walk to market Mode choice for all toursStop frequency; stop location; stop time No stops Starbucks near home No stops Shared ride 2; Walk Drive; Drive; Drive Transit; Bicycle Shared ride 2; Walk Trip mode choice

 Feedback  Through the entire model stream  Sampling and run-time  Scenario run: 15/25/50  ~24 hours  Conformity run: 15/25/50/100  ~36 hours  Hardware and Software  Cube & Cube Cluster  PB CT-RAMP  Four identical machines  Each with 8 processors, 48 GB of RAM 9

10 Technical features. Data collection and management. Time frame for model development. Key questions.

 Home interview survey  Year 2000, MTC, $1.5 million  Year 2011/12, with Caltrans, $1.5 million  On-board surveys  Individual operator data used for model dev.  Year 2010  ?, $750,000 so far  Goal is continuous survey program  Ridership information collected via universal fare media  Roadways  Caltrans PeMS  Struggle to get good arterial data  MTC traveler information (segment speeds) 11

12 Technical features. Data collection and management. Time frame for model development. Key questions.

Plan  Issue RFP in 2005  Consultant specifies the model structure  Consultant writes the software  MTC estimates the models (with consultant assistance)  MTC calibrates the models (with consultant assistance)  Complete estimation by ~2007 Actual  Issue RFP in 2005  Consultant specifies the model structure  Consultant writes the software  Coefficients are borrowed and estimated by consultant  Consultant calibrates the models (with MTC’s guidance)  Model ready for use December

14 Technical features. Data collection and management. Time frame for model development. Key questions.

Development  Importance of model estimation  Software, software, software  Overseeing calibration  What is your agency good at, what is the consultant team good at Application  High-occupancy toll lanes  Land development patterns on walking, transit  Cordon pricing  Roadway operation strategies  Greenhouse gas emissions  Telecommuting  Parking pricing  Diverse transit modes 15

What do you see as the benefits of having an activity-based model (ABM)? 1. The ease of communicating the model structure – behavioral realism 2. Directly answer “can you” questions – particularly those related to equity 3. Summarizing the results – endless possibilities 16

Was the value added by the ABM worth the cost? 1.Yes. The value of easily describing the model structure alone is worth the cost (e.g. never again defining a “home-based work” trip to glassy-eyed on-lookers). 2.The platform facilities extensions/further innovations. 17

What do you see as the drawbacks of having an ABM? 1.Theoretically complex 2.Technically complex 3.Computationally complex 4.Explicit answers to lots and lots of questions 18

How does the model work compared to your expectations? 1.The PB software is far more stable than I anticipated (crashes are very rare). 2.Analyzing the data is far more interesting and rewarding than I anticipated. 3.The behavioral realism and consistency is greater than I expected. 19

What would you change if you could? 1.More resources (computers, consulting budget, staff) 2.Sponsor multiple grants to software developers with guiding standards 3.More detail – smaller zones, richer roadway details, finer temporal resolution, etc… 20

In your opinion, what was the most difficult part of the model development? 1.Being brave and patient during model calibration 2.Dealing with sparse and limited data 21

If you were starting again from scratch, what would you do differently? 1.Collaborate with multiple MPOs – working with ARC was terrific 2.Let the consultants do what they do well 3.Try and improve upon what we do well 22

Knowing what you know now, would you again choose to develop an ABM? 1.Yes. There is no (real) debate as to whether the ABM approach is superior to the trip-based approach. The question is whether the costs are commiserate with the benefits. 23

Have you used your ABM to support LRTP development? 1. Currently using the model to support our 2013 RTP – through 2 of 3 rounds of alternatives analysis. 2. The demands of the model are far greater because the model is far more capable. 24

How does your agency store and manage data? 1. Runs require about 40 GB of data, depending on the sample size. 2. Runs are archived (about 1 GB of data) on a cloud server. Storage has not been an issue for us. 25

How do consultants run the model, how do you share results? 1. Too early to know 2. Currently share all files (execution and results), posted on a wiki managed by the modeling group 3. Working on a more formal data repository to share model results as well as GIS data 26

How does data management differ between trip-based models and ABM? 1. Challenges with distributing files to knowledgeable users are the same 2. Working with researchers is far easier as the results are far easier to understand 27

What innovations, if any, do you recommend regarding data collection and management? 1. Outward facing database of results 28

29 Image source: flickr.com/shapiro125 Day 2

30 Annual investment in modeling. Team structure, roles, and responsibilities. What works well and what could benefit from modifications/improvements.

31 Annual investment in modeling. Team structure, roles, and responsibilities. What works well and what could benefit from modifications/improvements.

 Full time travel modeling staff  One principal, four associates  ABAG: one principal, one associate  MTC helps fund ABAG efforts as well as significant county modeling efforts  Development support  ~$150,000 annually (varies)  Consultants do majority of the work  Application support  ~$50,000 for RTP support  MTC does the majority of the work  Data collection  ~$1.5 million every decade for home interview  ~$250,000 annually for on-board survey (goal) 32

33 Annual investment in modeling. Team structure, roles, and responsibilities. What works well and what could benefit from modifications/improvements.

 Principal  Reports to Planning Section Director  Manages development and application activities  Application lead  Oversees GIS activities  Associates  Highway network lead  Transit network lead  Data lead  Air quality lead  County modeling staff  Project-level work  ABAG  Land use development and application activities 34

35 Annual investment in modeling. Team structure, roles, and responsibilities. What works well and what could benefit from modifications /improvements.

 The Good  Play to our strengths: management, writing, coding projects, attention to detail, over-arching technical approach  Information technology staff  Consultants  The Mixed  Software  County-level models  Staff development  The Not-so-good  Land use modeling housed in a separate agency 36

37 Image source: flickr.com/Steve Punter Questions