00389957

Component

http://onlinepubs.trb.org...s/trr/1983/944/944-013.pdf

https://scholar.google.co...pher&publication_year=1983

The variables used to predict household trip-generation rates have long been an area of concern for transportation planners; these variables included household size, number of vehicles owned, and income. However, a recent NCHRP study that used linear regression analysis has proposed that a household-structure variable would correlate more strongly with trip rates than almost any other variable, except vehicle ownership. In particular, this should improve the model significantly where it is combined with vehicle ownership and used as a substitute for household size. The results of a trip-generation analysis performed on data from the Midwest by using multiple classification analysis (MCA) in contrast to linear regression are described. The household-structure variable was tested by using both analysis of variance and MCA to determine how well the variable performs in various model structures when compared with other variables. The other variables tested were number of cars or vehicles available to the household, household size, housing type, total number of employed persons, household income, and total number of licensed drivers. It was concluded that the household-structure variable did not perform significantly better than the other variables tested. (Author)

Publication of this paper sponsored by Committee on Traveler Behavior and Values. Distribution, posting, or copying of this PDF is strictly prohibited without written permission of the Transportation Research Board of the National Academy of Sciences. Unless otherwise indicated, all materials in this PDF are copyrighted by the National Academy of Sciences. Copyright © National Academy of Sciences. All rights reserved

00395366

pp 92-100

1983

Transportation Research Record

0309036658

Print

Figures (4) ; References (4) ; Tables (7)

Households; Income; Linear regression analysis; Motor vehicles; Ownership; Regression analysis; Trip generation

Trip rate

Data and Information Technology; Highways; Planning and Forecasting; Society; I72: Traffic and Transport Planning

TRIS, TRB

Nov 30 1984 12:00AM

• Bus, Taxi, and Walk Frequency Models that Account for Sample Selectivity and Simultaneous Equation Bias
• DEVELOPMENT OF A TRAVEL-DEMAND MODEL SET FOR THE NEW ORLEANS REGION
• DEVELOPMENT OF SURVEY INSTRUMENTS SUITABLE FOR DETERMINING NONHOME ACTIVITY PATTERNS
• EFFECT OF SAMPLE SIZE ON DISAGGREGATE CHOICE MODEL ESTIMATION AND PREDICTION
• EQUILIBRIUM TRAFFIC ASSIGNMENT ON AN AGGREGATED HIGHWAY NETWORK FOR SKETCH PLANNING
• ESTIMATING TRIP TABLES FROM TRAFFIC COUNTS: COMPARATIVE EVALUATION OF AVAILABLE TECHNIQUES
• ESTIMATION AND USE OF DYNAMIC TRANSACTION MODELS OF AUTOMOBILE OWNERSHIP
• EXPERIMENTS WITH OPTIMAL SAMPLING FOR MULTINOMIAL LOGIT MODELS
• IDENTIFYING TIME AND HISTORY DEPENDENCIES OF ACTIVITY CHOICE
• MAXIMUM-LIKELIHOOD AND BAYESIAN METHODS FOR THE ESTIMATION OF ORIGIN-DESTINATION FLOWS
• MOBILITY ENTERPRISE: ONE YEAR LATER
• NETWORK DESIGN APPLICATION OF AN EXTRACTION ALGORITHM FOR NETWORK AGGREGATION
• PERSON-CATEGORY TRIP-GENERATION MODEL
• PROCEDURE FOR PREDICTING QUEUES AND DELAYS ON EXPRESSWAYS IN URBAN CORE AREAS
• QUICK-RESPONSE PROCEDURES TO FORECAST RURAL TRAFFIC
• RESPONDENT TRIP FREQUENCY BIAS IN ON-BOARD SURVEYS
• SEQUENTIAL, HISTORY-DEPENDENT APPROACH TO TRIP-CHAINING BEHAVIOR
• TRIP DISTRIBUTION USING COMPOSITE IMPEDANCE
• TRIP GENERATION BY CROSS-CLASSIFICATION: AN ALTERNATIVE METHODOLOGY
• TRIP TABLE SYNTHESIS FOR CBD NETWORKS: EVALUATION OF THE LINKOD MODEL