1. FOCUS MODEL OVERVIEW CLASS TWO Denver Regional Council of Governments June 30, 2011

2. Notes  http://www.drcog.org/index.cfm?page=FocusTechni calResources http://www.drcog.org/index.cfm?page=FocusTechni calResources  Next week’s class is in Independence Pass from 2-3 PM; New Go To Webinar next week  On-going classes through August 4; Thursdays 2-3 PM in Monarch  Tentative Schedule: Model Steps July 7 How to Run the Model July 14 Theoretical UnderpinningJuly 21 SQL DatabaseJuly 28 ?????August 4

3. Review of General Concepts  1. Logit Models are models that make assign probabilities to a set of choices for an individual from a list of discrete choices.  2. The actual choice is made using a monte carlo process.  3. Travel in the model is made on a tour-level, and then a trip level.  4. We can divide the model into four stages.  5. We use four types of code in the model: T-SQL, C#, GISDK, and Java.  6. Much of the input and output data is stored in SQL Server.  7. We still have to run parts of our old GISDK code for path building, skimming and assignment.  8. We are doing this because we can get much finer detail and answer planning questions better using the model.

4. Focus Model Flow: 28 Steps FEEDBACK

5. Focus Model Flow: Stage 1 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

6. STAGE 1: Make Population and Network  Java: Population Synthesizer  C# to process in database: Size Sum Variable Calculator; PopSyn Output Processor  GISDK called from C#: GISDK Preprocess Creating networks for example

7. Population Synthesizer ACS or PUMS Disaggregate Data Aggregate Data that We Need to Match: Economic Forecasts, Land Use Forecasts Disaggregate Population With the Right Portions Matching the Economic and Land Use Forecasts Questions?

8. Example Family: Mother, Age 33 Part Time Service Worker Father, Age 34 Full Time Education Worker Son, Age 4 Pre-School Student Family Income : $61,000 People come out as disaggregate unique entities with many characteristics.

9. Data Gets Processed into a Database Structure

10. Database Structure Created and Filled People

11. Database Structure Created and Filled Places

12. What are: Tours, Half Tours, Half Tour Stops, Trips STORE HOME WORK

13. Database Structure Created and NOT Filled YETTravel

14. Household Monte Carlo  PopSyn assigns the zone they live in. Then the model randomly assign the households to a point within the zone.

15. Give Each Household a Housing Unit to Live in. A disaggregate X-Y point

16. GISDK: Preprocess Socio-economic Inputs Area Type Parking Cost Network Processing & Data Preparation Highway Skimming Transit Skimming Trip Generation Trip Distribution Mode Choice Highway Assignment Transit Assignment Highway Network Inputs Transit Network Inputs Time-of-Day

17. Stage One is done. Everything outside feedback loop is done. Now we have :  1. A synthesized population  2. A database filled with point locations and people, model variables  3. A set of highway and transit networks ready for use.

18. Focus Model Flow: Stage 2 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

19. GISDK: For Skimming and External/ Commercial Trips Socio-economic Inputs Area Type Parking Cost Network Processing & Data Preparation Highway Skimming Transit Skimming Trip Generation Trip Distribution Mode Choice Highway Assignment Transit Assignment Highway Network Inputs Transit Network Inputs Time-of-Day

20. Stage 2: GISDK Through Mode Choice  All C#, trip-making components use travel time and distance skims for highway and transit  Internal-External, External-External, trips destined to DIA and Commercial Trips created from Compass GISDK  Later these trips get combined with the regular Internal-Internal Trips from C# into matrices by time-of-day by mode

21. Skim matrices(distance)

22. Time Periods for Skims  TIMES OF DAY  Highway Times of DayTransit Times of Day  AM1: 6:30 – 7:00 AM; AM: 6:30- 9:00 AM  AM2: 7:00 – 8:00 AM;  AM3: 8:00 – 9:00 AM;  OP2: 9:00 – 11:30 AM; MD: 9:00 AM- 3:00 PMPM  OP3: 11:30 AM – 3:00 PM;  PM1: 3:00 – 5:00 PM; PM: 3:00 PM -7:00 PM  PM2: 5:00 – 6:00 PM;  PM3: 6:00 – 7:00 PM;  OP4: 7:00 – 11:00 PM.EL: 7:00 PM – 6:00 AM  OP1: 11:00 PM – 6:30 AM;

23. Trip Tables (this is commercial)

24. GISDK Through Mode Choice  So we run trip generation, trip distribution, and mode choice for the funky trips  This is run from C# calling GISDK.  The C# pops open a TransCAD window and calls the macros that have been specified to run.

25. Stage 2 is done.  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.

26. 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

27. Talking time: Let’s talk about ourselves  What are the set of choices that make you travel like you do?  What is the highest priority?  What is unique about you that guides your choices?  What are the smaller choices you make each day?  What strange behaviors to you have that would be really hard to model?

28. 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

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

30. Long Term Choices: Regular Workplace Location  Where will I work? Final Choice: Two nests- Work at Home, Work Outside Home Zone and X,Y Location of Work Type of Model: Nested Logit Inputs: (What do you think predicts?) Number of Jobs by type in in a zone Distance from Home to Work Full Time Worker or Part Time Worker Job Sector Accessibility of Work Location from Home

31. Here’s how the choice looks, sent back to the database Persons table:

32. Long term choice 2: Regular School Location Final Choice: Zone and X,Y Location of School Type of Model: Multinomial Logit Inputs: (What do you think predicts?) Grade Level in School Distance to School from Home Income Group Older Sibling’s School