Focus Model Overview CLASS THREE

FOCUS MODEL OVERVIEWCLASS THREEDenver Regional Council of GovernmentsJuly 7, 2011

Tentative ScheduleModel Steps July 7Final Model Steps/How to Run the Model July 14Theoretical UnderpinningJuly 21SQL Database July 28???? August 4Should we continue after this?

Focus Model Flow: 28 StepsOutside The Speed Feedback Loop: Run Once-STAGE 1GISDK called from C#:GISDK PreprocessJava:3. Population SynthesizerC#4. PopSyn Output Processor5. Size Sum Variable Calculator

STAGE 2 GISDK called from C#:For DIA, I-E, E-E and Commercial Trips1. DRCOG Multi-Period Highway Preprocess 2. DRCOG Multi-Period Transit Preprocess3. DRCOG Transit Preprocess4. Trip Generation5. Highway and Transit Skimming6. Trip Distribution7. Mode Choice

STAGE 3 C# Regular Trips8. Regular Work Location Choice 19 . Tour Main Mode Choice 9. Regular School Location Choice 20. Tour Time of Day Choice10. Auto Availability 21. Intermediate Stop Generation11. Aggregate Logsum Generation 22. Trip Time of Day Simulation12.Daily Activity Pattern 23. Trip Time Copier13. Exact Number of Tours 24. Intermediate Stop Location14.Work Tour Destination Type 25. Trip Mode Choice15.Work-Based Subtour Generation 26. Trip Time of Day Choice16. Tour Time of Day Simulation 27. Write Trips to TransCAD17. Tour Primary Destination Choice18. Tour Priority Assignment

STAGE 4 GISDK called from C#:28. Highway and Transit Assignment

FEEDBACK

Focus Model FlowSTAGE 1:

Make Population

And Network

STAGE 2:Run GISDK to Mode Choice

STAGE 3: C#

Logit Models to

Create Trips

STAGE 4: GISDK

Assignment

FEEDBACK

Review of Class 2In stage one, we create a disaggregate list of people and households.We know a lot of details like age, income, household size, worker status, student status, and number of children.We can use this to help determine how people travel.For example, Chris P. is more likely to drop off kids because he has them in his household.

Review of Class 2 After stage one: 1. A synthesized population living at X-Y

coordinates 2. A database filled with point locations

and people, model variables Slight digression: we know a lot more

about the places too: mixed use density, intersection density

3. A set of highway and transit networks ready for use.

Review of Class 2 After Stage 2: Now we have a LOT of matrices: All highway and transit skims A set of commercial and external trips O-

Ds A set of DIA trips O-Ds and modes And all stage one outputs: population,

networks, a ready database.

Focus Model Flow: Stage 3

STAGE 1: PREPROCES

S

STAGE 2 :GISDK Through

Mode Choice

STAGE 3: C#

Logit Models to

Create Trips

STAGE 4: GISDK

Assignment

FEEDBACK

The steps in Stage 3. Mostly Logit Models. The heart of the model.

8. Regular Work Location Choice 19 . Tour Main Mode Choice 9. Regular School Location Choice 20. Tour Time of Day Choice 10. Auto Availability 21. Intermediate Stop Generation 11. Aggregate Logsum Generation 22. Trip Time of Day

Simulation 12.Daily Activity Pattern 23. Trip Time Copier 13. Exact Number of Tours 24. Intermediate Stop Location 14.Work Tour Destination Type 25. Trip Mode Choice 15.Work-Based Subtour Generation 26. Trip Time of Day Choice 16. Tour Time of Day Simulation 27. Write Trips to TransCAD 17. Tour Primary Destination Choice 18. Tour Priority Assignment

Long Term Choices

8. Regular Work Location Choice

9. Regular School Location Choice

10. Auto Availability11. Aggregate Logsum Generation

Talking time: Let’s talk about ourselves again-

Someone volunteer or I will pick you

How many cars does your household have? What about you or where you live or what you do determines this?

Long term choice 3: How many cars will our household own?

Auto Ownership Model Final Choice:Number of Household Cars =0, 1, 2, 3, 4+ Type of Model: Multinomial Logit Inputs: (What do you think predicts?)Household SizeIncome GroupAccessibility of Home Location (by transit

included)Age of People in Household

How the auto ownership choice looks on the household table:

Aggregate Destination Choice Logsum Generation (don’t fall asleep)

The aggregate destination choice logsums are a measure of total accessibility for a household.

A measurement of how easy it is to get to all destinations (shopping, hospitals, schools, etc) by all modes (walking, biking, transit, driving)

Size of destination= number of jobs End up with a number that describes the

accessibility the household has to do many activities: social recreational, meal etc

Long term choices made Each individual has regular work

location, school location. We know how many cars a household has and how it accessible it is to various types of services.

Now we need daily choices for travel. How many trips will each person take and for what purposes?

HOME WORK

STORE

Next we generate tours and information about location, mode, and time.

Tour Generation Models

12.Daily Activity Pattern 13. Exact Number of Tours 14.Work Tour Destination Type 15.Work-Based Subtour Generation

How many tours do you take on a typical workday?

Seven tour types: work, school, escort, personal business, shop, meal, social recreation

What are the drivers of how many tours you take?

Someone volunteer again.

Daily Activity Pattern Choice Model knows now: - where each person works, goes to school- How many cars their household has- How accessible their home is to other

locations

In Daily Activity Pattern, the model predicts:How many different and what type of

activities will a person conduct in a day?

Daily Activity Pattern Choice Choice: 0-1 if they make a tour or make a stop or

both for: Work, School, Drive Passenger, Meal, Shopping, Social Recreation, or Personal Business

Model Type: Logit 300 possible choices (some limitations on number

of types of activities to make tours and stops) Inputs: (What do you think predicts?)

Worker Status Income GroupAgeHousehold Accessibility (Logsums)

Exact Number of Tours Given if a person will make tours from DAP,Choice: This predicts the number of tours; for

example 1, 2, 3+ work tours by purpose: work, school, escort, personal business, meal, shop, social/recreation

Model Type: LogitInputs: (What do you think predicts?)Similar to DAPAuto OwnershipGenderStudent Status

Doing what? Number of Tours

Number of Stops

Working 1 0Going to school 0 0

Shopping 0 1

Escorting others 0 0

Socializing 0 0

Eating out 1 0Personal business

0 0

End up with info like this:

Tours get written into the database.

Work Tour Destination TypeWork Based Subtour Generation

A couple of simple models related to tour generation

On each work tour, will I go to my regular workplace or not= work tour destination type? Or will I got some other place to work?

Work-Based Subtour Generation: How many times will I leave work and return in one day? (i.e. go to lunch and come back)

Now we need to know more information about the tours

16. Tour Time of Day Simulation (When)

17. Tour Primary Destination Choice (Where)

18. Tour Priority Assignment (Priority)19 . Tour Main Mode Choice (Mode)20. Tour Time of Day Choice (Time)

Tour Time of Day Simulation Tour Time of Day Simulation: Type of Model =

Monte Carlo This is a weird one! Before we pick where a person goes and which

mode they use on a tour we need a skim time period to pick from to choose how long it takes

We use a weighted random assignment of the TOUR DESTINATION ARRIVAL TIME/ TOUR DESTINATION DEPARTURE TIME based on the purpose of the tour.