FOCUS MODEL OVERVIEW
CLASS THREE
Denver Regional Council of Governments
July 7, 2011
Tentative Schedule
Model Steps
July 7
Final Model Steps/
How to Run the Model July 14
Theoretical UnderpinningJuly 21
SQL Database
July 28
????
August 4
Should we continue after this?
Focus Model Flow: 28 Steps
GISDK called from C#:
For DIA, I-E, E-E and Commercial Trips
GISDK called from C#:
GISDK Preprocess
Java:
3. Population Synthesizer
C#
4. PopSyn Output Processor
5. Size Sum Variable Calculator
1. DRCOG Multi-Period Highway Preprocess
2. DRCOG Multi-Period Transit Preprocess
3. DRCOG Transit Preprocess
4. Trip Generation
5. Highway and Transit Skimming
6. Trip Distribution
7. Mode Choice
FEEDBACK
C# Regular Trips
8. Regular Work Location Choice
9. Regular School Location Choice
10. Auto Availability
11. Aggregate Logsum Generation
12.Daily Activity Pattern
13. Exact Number of Tours
14.Work Tour Destination Type
15.Work-Based Subtour Generation
19 . Tour Main Mode Choice
20. Tour Time of Day Choice
21. Intermediate Stop Generation
22. Trip Time of Day Simulation
23. Trip Time Copier
24. Intermediate Stop Location
25. Trip Mode Choice
26. Trip Time of Day Choice
16. Tour Time of Day Simulation
17. Tour Primary Destination Choice
18. Tour Priority Assignment
27. Write Trips to TransCAD
GISDK called from C#:
28. Highway and Transit Assignment
Focus Model Flow
STAGE 1: Make
Population
And Network
STAGE 2:
Run GISDK to
Mode Choice
FEEDBACK
STAGE 4: GISDK
Assignment
STAGE 3: C#
Logit Models to
Create Trips
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.
Review of Class 2
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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
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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:
PREPROCESS
STAGE 2 :GISDK
Through Mode
Choice
FEEDBACK
STAGE 4: GISDK
Assignment
STAGE 3: C#
Logit Models to
Create Trips
The steps in Stage 3. Mostly Logit
Models. The heart of the model.
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8. Regular Work Location Choice
9. Regular School Location Choice
10. Auto Availability
11. Aggregate Logsum Generation
12.Daily Activity Pattern
13. Exact Number of Tours
14.Work Tour Destination Type
15.Work-Based Subtour Generation
16. Tour Time of Day Simulation
17. Tour Primary Destination Choice
18. Tour Priority Assignment
19 . Tour Main Mode Choice
20. Tour Time of Day Choice
21. Intermediate Stop Generation
22. Trip Time of Day Simulation
23. Trip Time Copier
24. Intermediate Stop Location
25. Trip Mode Choice
26. Trip Time of Day Choice
27. Write Trips to TransCAD
Long Term Choices
8. Regular Work Location Choice
 9. Regular School Location Choice
 10. Auto Availability
 11. Aggregate Logsum Generation
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Talking time: Let’s talk about ourselves
againSomeone 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 Size
Income Group
Accessibility of Home Location (by transit included)
Age of People in Household
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How the auto ownership choice looks
on the household table:
Aggregate Destination Choice Logsum
Generation (don’t fall asleep)
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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
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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?
Next we generate tours and information
about location, mode, and time.
HOME
WORK
STORE
Tour Generation Models
12.Daily Activity Pattern
 13. Exact Number of Tours
 14.Work Tour Destination Type
 15.Work-Based Subtour Generation
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How many tours do you take on a
typical workday?
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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
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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
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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)
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
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End up with info like this:
Doing what?
Number of
Tours
Number of
Stops
Working
1
0
Going to school
0
0
Shopping
0
1
Escorting others
0
0
Socializing
0
0
Eating out
1
0
Personal
business
0
0
Tours get written into the database.
Work Tour Destination Type
Work Based Subtour Generation
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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
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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.