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DYNAMIC DISAGGREGATE CHOICE MODELS, WITH AN APPLICATION IN TRANSPORTATION
Author(s) -
Krishnan K. S.,
Beckmann Martin J.
Publication year - 1979
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.1979.tb00020.x
Subject(s) - mode choice , mode (computer interface) , constant (computer programming) , econometrics , logit , order (exchange) , computer science , measure (data warehouse) , economics , mathematical economics , public transport , engineering , finance , database , transport engineering , programming language , operating system
A standard assumption of disaggregate mode‐choice models is that an individual faced with a choice among several available alternative transportation modes compares their utilities. These utilities are almost always expressed as a linear composite measure of various perceived benefits which are assumed constant. However, the individual's perceptions change as he learns, acquires new habits, or adapts to different physical, economic, and social circumstances. In order to account for these changes, two dynamic models of model‐choice behavior are developed herein. These two models are based on two common postulates. (1) One alternative is preferred over another only if the absolute difference in their utilities exceeds a positive constant; otherwise, the decision maker is indifferent toward the two alternatives. (2) If an alternative is preferred, it will be chosen with certainty. In the indifference state, the individual is postulated to randomly choose one of the two alternatives (Model 1) or choose the same alternative as was most recently chosen (Model 2). Choice probabilities derived from these two models are shown to differ from those obtained using the conventional logit model. If there is a strong loyalty toward a mode, the logit model underestimates its choice probability when that mode is less attractive than the competing mode. The results are illustrated using numerical examples.