transportation - trip generation

1. Source: NHI course on
Travel Demand Forecasting
(152054A)
Trip Generation
CE 451/551 Grad students …
need to discuss
“projects” at end
of class

2. Terminology
• Trip generation
• Person trip
• Vehicle trip
• Trip end
• Trip production
• Trip attraction
• Trip purposes
– Home-based work (HBW) trip
– Non-home based (NHB) trip … others
• Special generator
• Socioeconomic data
• Demographic data
Image: http://www.angryspec.com/scrounge.htm

3. Trip purposes
Practice has shown that better travel forecasting models
are obtained if trips by different purposes are identified and
modeled separately. The most common trip purposes are:
– HBW
– HBO
– NHB
In TDF, trip productions and attractions are used to
represent the ends of a trip. A production is the home end
of an HB trip and the beginning of a NHB trip.
HB trips (urban) constitute ~70% of all trips
Others?

4. Trips, by purpose (the objective)
PA Table

5. Typical Trip Generation Process
Cross Classification Model
Regression model
Demographic and Socioeconomic inputs
Employment, attraction landuse data
Trip Attractions by zone,
by purpose
Trip Productions by
zone, by purpose
Balance (system-wide)
PA Tables,
by purpose

6. Balancing attractions to productions
Rule of thumb:
original
estimates of
total production
and attractions
should be
within 10% of
each other.

7. What is trip generation a
function of?
• Land use
• Intensity
• Location/accessibility
• Time
• Type (person, transit, auto,
walking …)
Photo by en:User:Aude, taken on March 7, 2006 Graphic source: http://www4.uwm.edu/cuts/utp/routeloc.pdf

8. Trip Generation
• Determine number of “trip ends”
• Methods
– Regression
– Cross Classification (tables)
– Rates based on activity units (ITE)
Image: www.caliper.com

9. Regression
• Aggregate (zonal) or disaggregate (household)
• Linear or nonlinear
• Dependent (Y) variable is trips
• Independent (Xi) variables are …
– Household attributes
• E.g., population, auto ownership, income level
– Employment attributes
• E.g., number of employees or size of establishments
– Could include network attributes?
• Be careful of … co-linearity, power
• Can use your own data (best?) or borrow parameters
Y = f(X)
“Estimating”
a model
aggregation
hides
variability

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Attribution-NonCommercial 2.5 License. This means you're free to copy
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11. Cross classification models
• Breaks the trip generation process
into steps
• Relies on aggregate data collected
from surveys (like Census), like
average income by
– income categories
– auto ownership
– Trip rate/auto
– Trip purpose %
• Resembles regression, but non-
parametric (like regression with
dummy variables)
• Groups households in different strata
• 1-4+ submodels (table based)
• Improved by adding info
• Advantages
– No prior info on
shape of curves
must be assumed
– Simple, easy to
understand
– Can be used to
account for time,
space
• Disadvantages
– Does not permit
extrapolation
– No goodness of fit
measures
– Requires large
sample size
From: Amarillo 1990 model docs, ITE
See wiki on
Contingency tables

12. One step Cross classification
model (productions)
HBW
From: Amarillo 1990 model
* Note: US avg. median HH income = $30K in 1990 … is now $50,000 (2007)
0-$8000
$8K-$16K
$16K-$32K
$32K-$56K
$56K plus
2007 eq.*

13. NHB
From: Amarillo 1990 model
One step Cross classification
model (productions)
0-$8000
$8K-$16K
$16K-$32K
$32K-$56K
$56K plus
2007 eq.

14. Multi-step Cross Classification
Example
Source: ITE (Univ. of Idaho)

15. Given
(from
survey)
First … Develop the family of cross class curves and find number of
households in each income group
00
Note: orange
lines show
how to
develop the
curves
L
M H
L

16. Now find … percent of
households in each auto
ownership/income group
“class” …

17. A
L M H
Given
(from
survey)
15K 25K 55K

18. Now find … trips per
households in each
auto ownership/income
group “class” …

19. L M H
B
Given
(from
survey)

20. Now find … trips by
purpose in each income
group “class” …

21. L M H
C
Given
(from
survey)

22. Recall the problem …
For the zone … multiply the number of households in each income group (00)
by the percent of households owning certain number of cars by income group
(A) to get the total number of households by auto ownership in each income
group (00 x A) …see next slide series
Multiply the result (00xA) by the number of trips generated by each income
group/auto ownership category (B) to get trips by income group/auto ownership
category (00xAxB). Sum to get trips by income level (∑(00xAxB)).
Multiply this sum by the percent of trips by purpose (C) to get trips by purpose
by income group (Cx∑(00xAxB)).
Sum over all income groups to get (total trips by purpose from the zone). ANS

23. A
B
x
=
x
=
00
Low
Med
High
00xA

24. C
x
=
00xAxB
Cx∑(00xAxB)

25. Cross classification model
(attractions)
1998 Austin, TX household travel survey
Note: Less data than for productions, can use cross-class or regression,
most common classification is by type of employment

26. See also Wisconsin
Trip Rate Files
(Madison has
annotation)
Click in slideshow mode
Experience Based Analysis

27. Typical trip gen application
• Traffic engineers use rates (e.g. ITE),
why? (data, peak)
• Planners use cross class and regression,
why? (purpose, forecasting)
• Can we use rates in the TDF? How?
• http://www.ite.org/tripgen/Trip_Generation
_Data_Form.pdf

28. Special generators
• Shopping malls (large)
• Hospitals (different)
• Military institutions
• Airports (large)
• Colleges and universities (large, different)
• Stadiums (off peak)
• Elderly housing (small)
Click in slideshow mode