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

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, 2006en:User:Aude Graphic source:

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

Regression Aggregate (zonal) or disaggregate (household) Linear or nonlinear Dependent (Y) variable is trips Independent (X i ) 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 Example Source: 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 LM H Given (from survey) 15K 25K 55K

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

LM H B Given (from survey)

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

LM H C Given (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) Trip Rate Files 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? _Data_Form.pdfhttp:// _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