Lec 9, TD part 2: ch5.4.1 & H/O, pp : Trip Generation Estimating the number of trips generated by zonal activities Trip generation estimate by regression analysis Trip generation estimates by trip rates/unit Trip generation estimates by category analysis Method to balance trip productions and attractions TD: Part 2 topics

What is trip generation? It is the process by which measures of urban activity are converted into numbers of trips. In trip generation, the planner attempts to quantify the relationship between urban activity and travel. It means both trip productions and trip attractions.

A zone produces and attracts trips Zone i # of dwelling units Shopping center employees Etc. Depending on the activities in the zone, it can produce and/or attract trips. Transportation planners estimate these trips first.

Three ways for estimating the number of trips produced Regression models Category analysis (cross-classification analysis) Y = dependent var. (trips/household) X1, X2, etc. = independent variables Trip rates, like # of trips/1000ft 2, ITE’s trip generation rates (Tab. 5.2 &Fig of the text) (Fig of the text)

Regression models (often, simple or multiple linear models): advantages and disadvantages Easy and relatively inexpensive. Correlation among independent (explanatory) variables may create estimation problems  If correlated, choose only the variable(s) that has the highest correlation with the dependent variable. Stepwise regression may help to find it. The assumptions of linearity and additive impacts on trip generation may be wrong. “Best fit” equations may yield counterintuitive results (see Eq of Meyer & Miller). By using zonal averages, important socioeconomic variations within the zone may be obscured or may yield spurious results.

Regression models (cont): something you want to be aware… A high R 2 (Coefficient of determination) by itself mans little if the t-test is marginal or poor, Just having a large number of independent variables does not mean very much.  Choose only the independent variable that have highest correlation with the dependent variable and low correlation among the independent variables. Check the coefficients are logical or not. Trip generation is never “negative” in reality no matter what value the independent variable has. See the EXCEL file. Then, we will go through Example 1 to get some hints.

Trip generation rates This is an example of trip generation rate information taken from the ITE Trip Generation Handbook. Some land use has a lot of data points like this one, but others (many of them) have only sparse data points. This handbook is evolving and every year new data are added.

Category analysis (cross-classification analysis) Less aggregated than trip rates and regression models See the list of advantages and disadvantages in the book to see why this is popular (p.277 of the text) See Examples 2 & 3. of C2. Workers /HH Household size HB Trip Production example (Groups individual HHs according to common socioeconomic characteristics, see p.275 of the text)

Trip attraction Trip attraction rates can be made by analyzing the urban activities that attract trips. Trips are attracted to various locations, depending on the character of, location, and amount of activities taking place in a zone. Three tools are used for this end too, but obviously types of independent variables used are different. We will do go over Example 4. Also see Table 11-5 ( c) in the handout.

Control totals (ch5. P.277) The area-wide production and attraction must be the same. In general they are not the same after calculation because trip production and attraction are estimated separately by different models with different variables. CT p = control total of productions P z = trip productions for each zone P e = trip productions at each external station A e = trip attractions at each external station I-I trips (P z ) I-E trips (P e ) E-I trips (A e ) Compute the factor used to balance productions & attractions. (See Figure 5.11 of the text)