An alternative methodology for calibrating cross-daaaifiaetion medals, namely multiple daaaification enelysis (MCA), it deaaribad. This tadrnique, which hes bean aveilable in the secial saienaes for acme time, does not appasr to have bean used in trwwpc+ tation planning before, although it appears to be able to everrome moat of the disadventagatnormally eaaoaietadwith atsndard crossdassifiation ralibretion tedwsiquas. The MCA prooadure is deearibad briefly, end its merits-in terms of stetiatiod aaaaesmant, ebility to permit comparisons among alternative models, end leek of susaaptibllity to smell asmples in individual aalls-ere disaueaadin detail. In addition, the method ie IMS~ on endysis of variersoa (ANOVA), whiah provides e atmeturad proaadurefor drooairrgemong alternative irrde~ ent variablesend eltarrtativegroupings of the values of eaoh independent verieble. Thaaeprooaduravwe oontmsted with standard proaadurasfor cross-dassifiation that aatimate sell valuee by obtaining the averagevalue of the dapandant variabla (ag, a tip rata) for those tam. plas that fall in the all and araunable to use any information from any othar odl. Tha proaas of salartingindapandent variablasand sderting groupingsof the rlroaanvariablesby ANOVA is illustrated with a mea study. In this study the wey in whiah this prooasaworka, and the dagrw to whidr thara is stetistiad information provided to~ ida the analyst’sjudgment, is shown. In the caea study tha aorrfirmationof intuitiva sdeatiom of variablesis noted, andalsoa mere surpri $ irtgresult is produmd that shows that thabesthousehold grouping is one that oombtnee two-and thres-parsrwrhouseholds. A aaoend csee study illustrates the use of MCAto aaloulatatripretas. A aornpsriaonoftheconventional proredura of cdl-by-all everaging, a MCAdesign that doas not arreunt for intaraations among the indepandant variables, and a MCA design thet rerraotafor intarartions is given. It is ahown that the MCAallows trip rates to be oomputad for soma cells that are ampty of data, and that MCA ramovassome pesaibly spurious rates that erisa in tha mnventiorrai mathod from gmalltemple problems in soma oalb. It is concluded tfrat MCA provides a strong methodology for arosa-dasaificationmodaling and that the prooadure is effadva in surmounting most of the drawfmrksof eorrventionalaatimation of suds medals.

In the 1950s and 1960s most of the transportation planning studies developed trip-generation equations that used linear regression, particularly for pereon trip-production models. Linear regression wae eo strongly favored that it was the central reethcdin the FNWA guide to. trip-generation analysie (~). Initially, most of the trip-production models were formulated to provide an estimate of zonal trips as a function of zonal variables that descrbe house-holds. These models were increasingly the subject of criticiem, particularly because of the lees of variance from the extremely aggregate nature Of these models(2, 3). As a result, household models of tr1P produc~ i~ n were developed, in which the dependent variable be@ MVS average daily tries Per householdr possibly by purpose, as a futtction of attributea of the household. These models remainedr however, predominantly linear-regressionmodels. In a few instances an alternative method of modeling trip generation appared. This method was known in the United States as cross-classification and in the United Kingdom as category analysis (1, 4). Thia method went through the same develop----meritaz the linear-regressionmodels, with the earliezt procedures being zonal trip estimators and subsequent models being based on household rates. For the most part, however, the …