Typical Trip Generation Models

Typical Trip Generation Models Trip generation models generally assume a linear form in which the number of vehicle-based (automobile, bus, or subway) trips is a function of various socioeconomic and/or distributional (residential and commercial) characteristics. An example of such a model, for a given trip type, is

0 1 1 2 2i i i k kiT b b z b z b z= + + +…+ (8.1)

where

Ti = number of vehicle-based trips of a given type (shopping or social/recreational) in some specified time period made by household i,

bk = coefficient estimated from traveler survey data and corresponding to characteristic k, and

zki = characteristic k (income, employment in neighborhood, number of household members) of household i.

The estimated coefficients (b’s) are usually estimated by the method of least squares regression (linear regression) using data collected from traveler surveys. A brief description and example of this method are presented in Appendix 8A.

EXAMPLE SHOPPING-TRIP GENERATION

A simple linear regression model is estimated for shopping-trip generation during a shopping-trip peak hour. The model is Number of peak-hour vehicle-based shopping trips per household = 0.12 + 0.09(household size) + 0.011(annual household income in thousands of dollars) − 0.15(employment in the household’s neighborhood, in hundreds) A particular household has six members and an annual income of $50,000. They currently live in a neighborhood with 450 retail employees, but are moving to a new home in a neighborhood with 150 retail employees. Calculate the predicted number of vehicle-based peak-hour shopping trips the household makes before and after the move.

SOLUTION

Note that the signs of the model coefficients (b’s, +0.09, and +0.011) indicate that as household size and income increase, the number of shopping trips also increases. This is reasonable because wealthier, larger households can be expected to make more vehicle-based shopping trips. The negative sign of the employment coefficient (−0.15) indicates that as retail employment in a household’s neighborhood increases, fewer vehicle-based shopping trips will be generated. This reflects the fact that larger retail employment in a neighborhood implies more shopping opportunities nearer to the household, thereby increasing the possibility that a shopping trip can be conducted without the use of a vehicle (a non–vehicle-based trip, such as walking). Turning to the problem solution, before the household moves,

Number of vehicle trips = 0.12 + 0.09(6) + 0.011(50) − 0.15(4.5) = 0.535

After the household moves,

Number of vehicle trips = 0.12 + 0.09(6) + 0.011(50) − 0.15(1.5) = 0.985

Thus the model predicts that the move will result in 0.45 additional peak-hour vehicle- based shopping trips due to the decline in neighborhood shopping opportunities as reflected by the decline in neighborhood retail employment.