1. FOCUS MODEL OVERVIEW CLASS THREE Denver Regional Council of Governments July 7, 2011

2. Tentative Schedule Model Steps July 7 Final Model Steps/ How to Run the ModelJuly 14 Theoretical UnderpinningJuly 21 SQL DatabaseJuly 28 ????August 4 Should we continue after this?

3. Focus Model Flow: 28 Steps FEEDBACK

4. Focus Model Flow STAGE 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

5. Review of Class 2 In 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.

6. 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.

7. 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.

8. Focus Model Flow: Stage 3 STAGE 1: PREPROCESS STAGE 2 :GISDK Through Mode Choice STAGE 3: C# Logit Models to Create Trips STAGE 4: GISDK Assignment FEEDBACK

9. The steps in Stage 3. Mostly Logit Models. The heart of the model.  8. Regular Work Location Choice19. Tour Main Mode Choice  9. Regular School Location Choice20. Tour Time of Day Choice  10. Auto Availability21. Intermediate Stop Generation  11. Aggregate Logsum Generation22. Trip Time of Day Simulation  12.Daily Activity Pattern23. Trip Time Copier  13. Exact Number of Tours24. Intermediate Stop Location  14.Work Tour Destination Type25. Trip Mode Choice  15.Work-Based Subtour Generation26. 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

10. Long Term Choices  8. Regular Work Location Choice  9. Regular School Location Choice  10. Auto Availability  11. Aggregate Logsum Generation

11. 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?

12. 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 Size Income Group Accessibility of Home Location (by transit included) Age of People in Household

13. How the auto ownership choice looks on the household table:

14. 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

15. 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?

16. HOME WORK STORE Next we generate tours and information about location, mode, and time.

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

18. 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.

19. 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?

20. 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 Group Age Household Accessibility (Logsums)

21. 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: Logit Inputs: (What do you think predicts?) Similar to DAP Auto Ownership Gender Student Status

22. Doing what?Number of Tours Number of Stops Working10 Going to school00 Shopping0 1 Escorting others00 Socializing00 Eating out10 Personal business 00 End up with info like this:

23. Tours get written into the database.

24. Work Tour Destination Type Work 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)

25. 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)

26. 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.