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

Trip Generation
CE 451/551
Grad students …
need to discuss
“projects” at end
of class
Source: NHI course on
Travel Demand Forecasting
(152054A)
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
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
Others?
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
Trips, by purpose (the objective)
PA Table
Typical Trip Generation Process
Demographic and Socioeconomic inputs
Cross Classification Model
Trip Productions by
zone, by purpose
Employment, attraction landuse data
Balance (system-wide)
Regression model
Trip Attractions by zone,
by purpose
PA Tables,
by purpose
Balancing attractions to productions
Rule of thumb:
original
estimates of
total production
and attractions
should be
within 10% of
each other.
What is trip generation a
function of?
•
•
•
•
•
Photo by en:User:Aude, taken on March 7, 2006
Land use
Intensity
Location/accessibility
Time
Type (person, transit, auto,
walking …)
Graphic source: http://www4.uwm.edu/cuts/utp/routeloc.pdf
Trip Generation
• Determine number of “trip ends”
• Methods
– Regression
– Cross Classification (tables)
– Rates based on activity units (ITE)
Image: www.caliper.com
aggregation
hides
variability
Regression
Y = f(X)
•
•
•
•
“Estimating”
a model
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
http://xkcd.com/503/ This work is licensed under a Creative Commons
Attribution-NonCommercial 2.5 License. This means you're free to copy
and share these comics (but not to sell them). More details
See wiki on
Contingency tables
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 nonparametric (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
One step Cross classification
model (productions)
HBW
2007 eq.*
0-$8000
$8K-$16K
$16K-$32K
$32K-$56K
$56K plus
* Note: US avg. median HH income = $30K in 1990 … is now $50,000 (2007)
From: Amarillo 1990 model
One step Cross classification
model (productions)
NHB
2007 eq.
0-$8000
$8K-$16K
$16K-$32K
$32K-$56K
$56K plus
From: Amarillo 1990 model
Multi-step Cross Classification
Example
Source: ITE (Univ. of Idaho)
First … Develop the family of cross class curves and find number of
households in each income group
Given
(from
survey)
Note: orange
lines show
how to
develop the
curves
00
M
L
L
H
Now find … percent of
households in each auto
ownership/income group
“class” …
A
Given
(from
survey)
15K
L
M
H
25K 55K
Now find … trips per
households in each
auto ownership/income
group “class” …
Given
(from
survey)
L
M
B
H
Now find … trips by
purpose in each income
group “class” …
Given
(from
survey)
L
M
C
H
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
00
A
Low
Med
x
High
=
00xA
B
=
x
00xAxB
C
x
= Cx∑(00xAxB)
Cross classification model
(attractions)
Note: Less data than for productions, can use cross-class or regression,
most common classification is by type of employment
1998 Austin, TX household travel survey
Experience Based Analysis
See also Wisconsin
Trip Rate Files
(Madison has
annotation)
Click in slideshow mode
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
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