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