M. Ben Akiva, S. Lerman, "Discrete Choice Analysis: Theory and application to Travel Demand", MIT Press, Cambridge, Mass (1985).  Home/Search   Document Not in Database   Summary   Related Articles   Check

....i ) satisfies p j (XXX i ) 0; and k X j=1 p j (XXX i ) 1: There are two common forms of parameterization for these models, which we will term choice models and classification models. Multinomial choice models have a long history in economics and transportation, see e.g. Anas (1983) Ben Akiva and Lerman (1985), and Anderson, de Palma and Thisse (1992) As a simple example, consider the following model for choosing a location to shop. Person i has k available shopping location choices. Each location has properties that make it more or less attractive as a shopping destination such as the distance from ....

Ben-Akiva, M. and Lerman, S. (1985). Discrete Choice Analysis: Theory and Application to Travel Demand, The MIT Press.

....are normally distributed but not necessarily independent. This model is however more difficult to implement because it leads to difficult numerical questions and also requires additional information on the choices (the covariance matrix) compared to the logit approach. The reader is referred to [9] for a comprehensive coverage of discrete choice theory in the framework of transportation modelling. 3.7 Least squares fitting We close this overview of the methodologies used in transportation research by mentioning that, as is the case in many scientific applications, one often tries to fit a ....

M. E. Ben-Akiva and S. R. Lerman. Discrete Choice Analysis: theory and application to travel demand. MIT Press, Cambridge, USA, 1985.

....As demand forecasting techniques, they have been used for transportation applications for more than thirty years. The multinomial and nested logit models are probably the most widely applied in this context. They have been analyzed in details in the literature (see, for example, McFadden, 1981 and Ben Akiva and Lerman, 1985). Alternative specific constants (ASCs) play an important role in these models: they correspond to the expectation of the random error term, and thus represent preferences, which are inherent and independent of specific attribute values, towards the alternatives. Furthermore, an incorrect choice ....

....attribute values, towards the alternatives. Furthermore, an incorrect choice of ASCs can cause model overspecification. Despite their importance, a detailed analysis of the role of ASCs is not available in the literature, at least to the authors knowledge. Amongst the published references, Ben Akiva and Lerman (1985) suggest avoiding overspecification of multinomial models by defining only J 1 ASCs for a choice set of size J . A common practice in this case is thus to assume that one (arbitrary) ASC is equal to zero. Simple generalizations of this principle to nested logit models typically result in either ....

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Ben-Akiva, M. E. and Lerman, S. R. (1985). Discrete Choice Analysis: Theory and Application to Travel Demand, MIT Press, Cambridge, USA.

....models, the Multinomial Logit Model (Section 4) the Nested Logit Model (Section 5) and the Generalized Extreme value Model (Section 6) are then introduced, with special emphasis on the Nested Logit model. Among the many publications that can be found in the literature, we refer the reader to Ben Akiva and Lerman (1985), Anderson, de Palma and Thisse (1992) Hensher and Johnson (1981) and Horowitz, Koppelman and Lerman (1986) for more comprehensive developments. 2 2 Modeling assumptions In order to develop models capturing how individuals are making choices, we have to make specific assumptions. We will ....

....Estimated Model value utility Linear L = 1 2 2 p 3 V Probit oe = 1 1 V Logit = 1 p 3 V Table 1: Model comparison The list of models presented here above is not exhaustive. Other assumptions about the distribution of the error term will lead to other families of models. For instance, Ben Akiva and Lerman (1985) cite the arctan and the truncated exponential models. These models are not often used in practice and we will not consider them here. 3.2 Assumptions on the deterministic term The utility of each alternative must be a function of the attributes of the alternative itself and of the decision maker ....

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Ben-Akiva, M. E. and Lerman, S. R. (1985). Discrete Choice Analysis: Theory and Application to Travel Demand, MIT Press, Cambridge, Ma.

....of this thesis is twofold. First, improved speci#cations of existing models are provided. Secondly, new e#cient algorithms are developed and implemented. An introduction to some mathematical models dedicated to OD matrices estimation is available in Chapter 2. The reader is referred to Ben Akiva and Lerman #1985# for a methodological approach of discrete choice models in general, and the logit model in particular. The MEUSE model, described in Chapter 4, is the result of the collaboration between rei ii preface searchers and practioners. The consultancy #rm Stratec had collected valuable data ....

....importance of such techniques has been proven not only for transportation studies, but also for marketing, behavioural psychology, geography, and more. In this context, random utility theory, and its corresponding models, is widely used #see, namely, Domencich and McFadden, 1975, Williams, 1977, Ben Akiva and Lerman, 1985#. These models are based on the assumption that individuals follow a rational behaviour pattern, and that they always select the alternatives that maximize their pro#t, or utility. Consequently, each alternative is associated with a quantity called utility, that mathematically expresses the ....

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Ben-Akiva, M. E. and Lerman, S. R. #1985#.Discrete ChoiceAnalysis: Theory and Application to Travel Demand, MIT Press, Cambridge, USA.

....ppp(XXX i ) satis es p j (XXX i ) 0; and k X j=1 p j (XXX i ) 1: There are two common forms of parameterization for these models, which we will term choice models and classi cation models. Multinomial choice models have a long history in economics and transportation, see e.g. Anas (1983) Ben Akiva and Lerman (1985), and Anderson, de Palma and Thisse (1992) As a simple example, consider the following model for choosing a location to shop. Person i has k available shopping location choices. Each location has properties that make it more or less attractive as a shopping destination such as the distance from ....

Ben-Akiva, M. and Lerman, S. (1985). Discrete Choice Analysis: Theory and Application to Travel Demand, The MIT Press.

....be differentiated by trip purpose. Municipalities are used as the unit of geographical aggregation in this study. Travel modes are classified into public transit, automobile, bicycle, walk . The uncertainty in the choice set is represented during the model estimation using the method outlined in Ben Akiva Lerman (1985) The explanatory variables used in the model are: Destination Choice . zonal population, number of commercial establishments, intra zone destination dummy, the possible minimum travel time to the destination zone, then to the location of the next fixed activity, and . probability that ....

Ben-Akiva, M. and S.R. Lerman (1985) Discrete Choice Analysis: Theory and Application to Travel Demand. MIT Press, Cambridge.

....cost, comfort, availability, ffl factors characterizing the choice maker such as age, income, social status, car ownership, The question is then to determine which of these attributes affect modal choices and how. Following the utility maximization approach [1], an individual s preferences towards each alternative can be described by a utility measure associated with it. In many studies (mentioned in [1, 6] the utility of each mode, denoted by U i (i = 1; m) where m is the number of available modes, is supposed to be dependent on travel time ....

....ownership, The question is then to determine which of these attributes affect modal choices and how. Following the utility maximization approach [1] an individual s preferences towards each alternative can be described by a utility measure associated with it. In many studies (mentioned in [1, 6], the utility of each mode, denoted by U i (i = 1; m) where m is the number of available modes, is supposed to be dependent on travel time and cost differentiated by socio economic class. A Random Utility Model (RUM) such as a LOGIT model, is then used to describe choice decisions for ....

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M.E. Ben-Akiva and S.R. Lerman. Discrete Choice Analysis: theory and application to travel demand. MIT Press, Cambridge, USA, 1985.

....These unobserved factors are also likely to make some car ownership alternatives closer substitutes than others (Small, 1987) thus inducing an intratemporal correlation among alternatives. From a modeling perspective, this implies that the independence of irrelevant alternatives (IIA) assumption (Ben Akiva and Lerman, 1985), often maintained in discrete choice models, is likely to be violated in the context of car ownership choices. Ideally, then, car ownership choices should be modeled accounting for both intertemporal correlation of unobserved determinants over time and intratemporal correlation of unobserved ....

Ben-Akiva, M. and Lerman, S. (1985). Discrete Choice Analysis: Theory and Application to Travel Demand, The MIT Press.

....or allowing for the chaining of trips between activity locations. 1.2.2 Activity and travel decision framework Figure 1. 5 shows how activity and travel scheduling decisions are made in the context of a broader framework, surrounded by and connected to other relevant decisions (Ben Akiva, 1973; Ben Akiva and Lerman, 1985; Ben Akiva, Bowman and Gopinath, 1996) Urban development decisions of governments, real estate developers and other businesses influence the opportunities available to households and individuals. Governments may invest in infrastructure, provide services, and tax and regulate the behavior of ....

....the detailed review of three activity based travel forecasting models in the next section examines only econometric model systems. We limit this review to disaggregate model systems that have been developed during the past 25 years and are based on the methods of discrete choice analysis (Ben Akiva and Lerman, 1985). More specifically, these model systems are based on hierarchies of decisions that are modeled using the nested logit model, first estimated by Ben Akiva (1973) almost 25 years ago. 1.4 MODEL SYSTEMS 1.4.1 Discrete choice methods Activity based econometric travel demand model systems achieve ....

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Ben-Akiva, M. and S.R. Lerman (1985). Discrete Choice Analysis: Theory and Application to Travel Demand. Cambridge, Massachusetts, MIT Press.

....Class choice model. Finally, we elaborate on the applications of these models to two specific short term travel decisions: route choice and departure time choice. 2.2 Discrete Choice Models We provide here a brief overview of the general framework of discrete choice models. We refer the reader to Ben Akiva and Lerman (1985) for the detailed developments. Handbook of Transportation Science General Modeling Assumptions The framework for a discrete choice model can be presented by a set of general assumptions. We distinguish among assumptions about the: 1. decision maker defining the decision making entity and ....

....e e e e e e t t P = bike 2 car 2 2 metro 1 bus 1 1 metro 1 bus 1 1 metro 1 bus 1 bus 1 ln ln ln ) bus ( b b b b b b q q q b b b , with 0q 1 ,q 2 1. This formulation simplifies the estimation process. For this reason, it has been adopted by the Ben Akiva and Lerman (1985) textbook and in estimation packages like ALOGIT (Daly, 1987) and HieLoW (Bierlaire, 1995, Bierlaire and Vandevyvere, 1995) We emphasize here that these packages should be used with caution when the same parameters are present in more than one nest. Specific techniques inspired from artificial ....

Ben-Akiva, M. E. and Lerman, S. R. (1985). Discrete Choice Analysis: Theory and Application to Travel Demand, MIT Press, Cambridge, Ma.

....Class choice model. Finally, we elaborate on the applications of these models to two specific short term travel decisions: route choice and departure time choice. Discrete Choice Models We provide here a brief overview of the general framework of discrete choice models. We refer the reader to Ben Akiva and Lerman (1985) for the detailed developments. General modeling assumptions The framework for a discrete choice model can be presented by a set of general assumptions. We distinguish among assumptions about the: 1. decision maker defining the decision making entity and its characteristics; 2. ....

.... = bike 2 car 2 2 metro 1 bus 1 1 metro 1 bus 1 1 metro 1 bus 1 bus 1 ln ln ln ) bus ( b b b b b b q q q b b b , with 0q 1 ,q 2 1. This formulation simplifies the estimation process. For this reason, it has been adopted by the Ben Akiva and Lerman (1985) textbook and in estimation packages like ALOGIT (Daly, 1987) and HieLoW (Bierlaire, 1995, Bierlaire and Vandevyvere, 1995) We emphasize here that these packages should be used with caution when the same parameters are present in more than one nest. Specific techniques inspired from artificial ....

Ben-Akiva, M. E. and Lerman, S. R. (1985). Discrete Choice Analysis: Theory and Application to Travel Demand, MIT Press, Cambridge, Ma.

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M. Ben Akiva, S. Lerman, "Discrete Choice Analysis: Theory and application to Travel Demand", MIT Press, Cambridge, Mass (1985).

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Ben Akiva, M.E, and S.R. Lerman, Discrete Choice Analysis: Theory and application to Travel Demand, The MIT Press, Cambridge, Mass.

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M. Ben-Akiva and S. Lerman. Discrete Choice Analysis: Theory and Application to Travel Demand, the MIT Press, Cambridge, MA, 1985.

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