trip generation ce 451/551
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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 - PowerPoint PPT PresentationTRANSCRIPT
Source: NHI course on Travel Demand Forecasting (152054A)
Trip GenerationCE 451/551 Grad students
… need to discuss “projects” at end of class
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
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?
Trips, by purpose (the objective)
PA Table
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
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?
• 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
Trip Generation
• Determine number of “trip ends”
• Methods– Regression– Cross Classification (tables)– Rates based on activity units (ITE)
Image: www.caliper.com
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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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
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.*
NHB
From: Amarillo 1990 model
One step Cross classification model (productions)
0-$8000
$8K-$16K
$16K-$32K
$32K-$56K
$56K plus
2007 eq.
Multi-step Cross Classification ExampleSource: ITE (Univ. of Idaho)
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
Now find … percent of households in each auto ownership/income group “class” …
A
L M H
Given (from
survey)
15K 25K 55K
Now find … trips per households in each auto ownership/income group “class” …
L M H
BGiven (from
survey)
Now find … trips by purpose in each income group “class” …
L M H
CGiven (from
survey)
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
A
B
x
=
x=
00
Low
Med
High
00xA
C
x
=
00xAxB
Cx∑(00xAxB)
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
See also Wisconsin Trip Rate Files(Madison has annotation)
Click in slideshow mode
Experience Based Analysis
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