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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

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trip generation ce 451 551
Trip GenerationCE 451/551

Grad students … need to discuss “projects” at end of class

Source: NHI course on Travel Demand Forecasting (152054A)

terminology
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
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?

typical trip generation process
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

PA Tables, by purpose

Trip Attractions by zone, by purpose

balancing attractions to productions
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
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
Trip Generation
  • Determine number of “trip ends”
  • Methods
    • Regression
    • Cross Classification (tables)
    • Rates based on activity units (ITE)

Image: www.caliper.com

regression

aggregation hides variability

Regression

“Estimating” a model

  • Y = f(X)
  • 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
slide10

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

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

See wiki on

Contingency tables

Cross classification models

From: Amarillo 1990 model docs, ITE

one step cross classification model productions
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 productions1
One step Cross classification model (productions)

NHB

2007 eq.

0-$8000

$8K-$16K

$16K-$32K

$32K-$56K

$56K plus

From: Amarillo 1990 model

slide15

First … Develop the family of cross class curves and find number of households in each income group

00

Given (from survey)

Note: orange lines show how to develop the curves

M

H

L

L

slide16

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

slide17

L

M

H

A

Given (from survey)

15K 25K 55K

slide18

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

slide19

L

M

H

Given (from survey)

B

slide21

L

M

H

Given (from survey)

C

recall the problem
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

slide23

00

A

Low

x

Med

High

=

00xA

B

=

x

slide24

00xAxB

C

x

Cx∑(00xAxB)

=

slide25

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

typical trip gen application
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
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