1. FUSING LONG AND SHORT DISTANCE TRAVEL IN THE COLORADO STATEWIDE MODEL
Transportation Planning Applications Conference
Jeffrey Newman
May 17, 2017

2. Motivation
Challenges
Application

3. Fort Collins
Denver
Colorado Springs
Grand Junction
Pueblo

4. Colorado’s 1st Statewide Model
Grand Junction
Fort Collins
Denver
Colorado Springs
Pueblo
Colorado’s 1st Statewide Model
Uses the same framework as DRCOG’s Focus ABM

5. The Long and Short of It
Common practice for statewide travel modeling is to use two travel model components:
a “typical” daily travel model that includes short(er) and more frequent trips, and
an "atypical" travel model that includes long distance trips that are undertaken infrequently

6. Trouble with the Usual Approach
In Colorado, drawing a line between long and short is not easy or desirable
CDOT does not want an artificial boundary
Solution: just one model to describe both long and short travel
50 MILES
Typical days for plenty of people include a commute over 50 miles to Denver. Atypical days can include overnight trips into the mountains much less than 50 miles from home. Drawing an artificial boundary can create problems in modeling.

7. Benefits of the Seamless Approach Simplicity of application
No artificial breakpoint causing “cliff effects”
Models regular intra- regional tours of 50+ miles
Allows regular travel between MPOs to increase
50 MILES
- Although there are no artificial breakpoint causing “cliff effects” in behavior, actual cliffs (i.e. mountains) can and do impact behavior, especially in Colorado.
- DRCOG alone spans about 75 miles north-to-south, and over 100 miles east-to-west!
- Long distance travel between Front Range MPOs is expected to continue to increase with population and employment growth

8. Challenges of Seamless Approach
Survey data does have artificial break points:
Daily diary contains all trips in a particular day, truncated at 3 am
Long distance travel log includes only long (50+ miles) trips over 14 days
Overnight travel cannot just be discarded or artificially truncated but it doesn’t fit neatly
Taking an long overnight trip can impinge on other activity the same day
But there still may be some such activity

9. Selected Key Model Changes
Auto Availability
Non-closed tours explicitly accommodated
Nontraditional (overnight) closed tours
Non-closed tours have a different distance profile
Need a more detailed destination model with multiple distance segments for reasonable calibration
Must accommodate tour legs that occur before the beginning of the day or after the end
Daily Activity Pattern
Exact Number of Tours
Work Tour Dest Type
Subtour Generation
Tour Destination
Tour Main Mode
Tour Time of Day
Interim Stop Generation

10. Daily Activity Patterns
TOURS
STOPS
DAP’s in Focus are modeled as 14 joint binary choices
Yes or No on having 1+ tours for each of 7 purposes
Same for intermediate stops by purpose
Unrestricted this gives 214 = 16,384 alternatives, but most are infeasible or disallowed
Only 2,080 alternatives that are considered valid, which are modeled by MNL
DAPs can be disallowed for a variety of reasons:
1+ tours must exist for any stops to exist,
Can’t have more than 3 active tour purposes or 4 active stop purposes,
Can’t have a work or school stop unless there is a work or school tour, etc.

11. New Daily Activity Patterns
TOURS
STOPS
DAP’s for the statewide model add two extra binary choices for non-closed tours:
work, and
other purposes (social/recreation and personal business; we exclude overnight tours for school, escort, meals and shopping)
Again we disallow a bunch of rare or infeasible combination, so we get down to 2,146 alternatives
Just 66 more than before
Drop Details
This could give 4,156 alternatives, but we add yet more restrictions:
Only one non-closed tour of any type,
Only 2 (not 3) tour purposes allowed when non-closed tours are included, …
NON-
CLOSED
TOURS

12. Data for Destination Choice
Tour Purpose
Tour Distance
Less than 50 mi
More than 50 mi
Work/Daily Commute
(same day closed tour)
Diary Only
Work/Other Business
Log Only
Other Purposes
The Daily Diary and the Two Week Travel Log contain overlapping data (in theory), only one or the other should be used for any particular category
The Diary was seen to capture daily commuting patterns better regardless of distance
The Log has a better picture of non-regular destinations that are within its domain
Non-weekday travel is removed from the Log and weights adjusted to be compatible
In each “bucket” we only use one category of input data and not both, so as to avoid double-counting.

13. Social/Recreational Destinations
Almost no trips to the plains for recreation trips

14. Social/Recreational Destinations
Model slightly over-predicts travel to Grand Junction

15. Social/Recreational Tour Distance
Hump about 70 miles is an artifact of Colorado geography
Relative Frequency (log scale)
Distance (mi)

16. Social/Recreational Closed Tours
Relative Frequency (log scale)
Distance (mi)
Similar distribution, skewed more heavily to short
The hump is not just overnight travel

17. Social/Recreational Non-Closed Tours
Relative Frequency (log scale)
Distance (mi)
Skewed more heavily to long distance
Still a notable number of short tours too

18. Social/Recreational Utility Function by Distance
Multi-point piecewise linear (smoothed) function for utility by distance
Allows for better calibration over the very wide range of distances
Closed Tours

19. Tour Time of Day Closed Tours, Same Day Closed Tours, Span Overnight
Same as the DRCOG model: identify start and end hour for tours, as an MNL of all feasible alternatives
Closed Tours, Span Overnight
Tours split in two, one starts “before” the travel day with a choice of end times, other starts “after” with a choice of start times.
Non-Closed Tours
Tour must start “before” or end “after”, not both Di

20. Summing Up
Fusing long and short distance travel into a single model has a lot of knock on effects in model development
The fused approach has some significant benefits in model application and interpretation
Colorado is a good candidate for the fused approach because of its geography
We’ll report back more complete results in a future TRB forum

21. Jeffrey Newman Cambridge Systematics Erik Sabina Colorado Department of Transportation David Kurth Jason Lemp Tom Rossi Cambridge Systematics