Multiple Regression Analysis

The three major techniques used for Trip Generation Analysis are Cross-Classification, Multiple Regression Analysis, and Experience Based Analysis. Multiple Regression Analysis is based on trip generation as a function of one or more independent variables. The approach is mathematical and all of the variables are considered random, and with normal distributions.

For example, consider the following equation:

T_i = 0.34 (P) + 0.21 (DU) + 0.12 (A)
A_j = 57.2 + 0.87 (E)

Where:
T_i = Total number of trips produced in zone I
A_j = Total number of trips attracted in zone j
P = Total Population for zone I
DU = Total number of dwelling units for zone I
A = Total number of automobiles in zone I
E = Total employment in zone j

Multiple Regression Analysis is relatively simple to understand. First, data regarding the actual number of productions and attractions is coupled with data about the area that is thought to impact the production and attraction of trips. For instance, the total population is believed to impact the number of trips produced. If we know the number of trips produced and the population for the present and a few time periods in the past, it is possible to develop a relationship between these parameters using statistical regression. Once we are satisfied with the relationship that has been developed, we can extrapolate into the future by plugging the future population into our relationship and solving for the number of productions. The process is called Multiple Regression, because there are normally several variables that impact trip production and attraction.