Travel Modeling at MTC

David Ory (dory@mtc.ca.gov)Metropolitan Transportation CommissionNovember 17 and 18, 2011

Presentation to Triangle Region Model Expert Panel

2Image 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

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 tours

Stop frequency; stop location; stop time

No stops

Starbucks near home

No stops

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 2010

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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 complex2. Technically complex3. Computationally complex4. 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

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29Image 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

activities34

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

agency36

37Image source: flickr.com/Steve Punter

Questions