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Pendyala, Ram M.; Goulias, Konstadinos G.

doi:10.1023/a:1012909228433

Cited by 41 publications

(3 citation statements)

References 3 publications

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“…Activity-based travel analysis has received substantial attention in the past three decades and has matured in the last few years (see Koppelman, 1999 andGoulias, 2002).…”

Section: Introductionmentioning

confidence: 99%

A mixed multinomial logit model analysis of weekend recreational episode type choice

Bhat

Gossen

2004

Transportation Research Part B: Methodological

165

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This paper presents a model for the type of recreational activity episodes that individuals pursue during the weekend. The choice set characterizing the type of recreational episode includes inhome, out-of-home, and pure recreational episodes (pure recreation refers to recreation trips pursued for the sole purpose of the recreational value obtained from the trip, such as walking, bicycling, and joy driving). The paper estimates a mixed multinomial logit formulation using the

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“…Activity-based travel analysis has received substantial attention in the past three decades and has matured in the last few years (see Koppelman, 1999 andGoulias, 2002).…”

Section: Introductionmentioning

confidence: 99%

A mixed multinomial logit model analysis of weekend recreational episode type choice

Bhat

Gossen

2004

Transportation Research Part B: Methodological

165

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“…The generation of the number of out-of-home activity episodes (or stops) of individuals is an important component of an activity-based analysis framework that emphasizes travel as being derived from the need to participate in activities (see Koppelman, 1999 or Pendyala andGoulias, 2002 for recent comprehensive reviews of the activity-based travel analysis approach).…”

Section: Introductionmentioning

confidence: 99%

Intershopping duration: an analysis using multiweek data

Bhat

Frusti²,

Zhao

et al. 2004

Transportation Research Part B: Methodological

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This study examines the rhythms in the shopping activity participation of individuals over a multiweek period by modeling the duration between successive shopping participations. A hazard based duration model is used to model intershopping duration, and a latent segmentation method is applied to distinguish between erratic shoppers and regular shoppers. The paper applies the methodology to examine the regularity and frequency of shopping behavior of individuals using a continuous six-week travel survey collected in the cities of Halle and Karlsruhe in Germany in the fall of 1999. The empirical results underscore the need to adopt a flexible hazard model form for analyzing intershopping durations. The results also provide important insights into the determinants of the regularity and frequency of individuals' shopping activity participation behavior.

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“…An enhanced understanding of how people allocate time to various activities can help in better forecasting and simulating activity-travel patterns under a wide range of system scenarios (1)(2)(3). In this context, much attention is being paid to understanding the value or utility that people derive from their participation in activities.…”

Section: Examination Of Two Approaches To Deriving Values Of Timementioning

confidence: 99%

Joint Analysis of Time Use and Consumer Expenditure Data

Konduri

Astroza

Greene

et al. 2011

Transportation Research Record

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economic utility-based framework involving time allocation that began with Becker (4). This early work also led to the development of the classical model by Train and McFadden who inserted travel into a goods-leisure trade-off framework (5). Later developments incorporated the concepts presented originally by DeSerpa, who offered an explicit definition for the value of leisure (6). There, a clear distinction between the value of time assigned to an activity and the willingness to pay to save time in that activity was made. In the more general version of the utility-based framework, people are seen as maximizing a utility function that depends on goods consumption and time use, whose allocation is subject to time and income constraints and to the relations between them (7). Second, there is a body of research that models time allocation to various activities using the structural equations modeling methods (8). In the structural equations modeling methods, there is no utility maximization principle underlying the model; rather, the time allocation to various activities is modeled as a system of simultaneous equations (9). Time allocation to various activities is influenced by person and household socioeconomic and demographic variables, built environment variables, and mobility factors. Relationships among endogenous variables (time use variables) are also represented in the system of equations, as are error correlations to capture the presence of possible common unobserved effects.This paper aims to compare the two modeling approaches with respect to the values of time that one would infer from using the two approaches. As mentioned earlier, values of time are of much interest to the profession from the perspective of modeling time allocation to activities, determining willingness to pay, and assessing quality-of-life measures (10-12). For example, when an infrastructure expansion project results in travel time savings, the amount of induced demand generated is likely to depend on the value that people attribute to out-of-home leisure activities. When a new toll road is being contemplated, the amount of toll that people are willing to pay is likely to be dependent on the value that people attribute to travel time savings resulting from the use of the toll road. The value of travel time savings may, in turn, be tied to the value associated with different activities that people could possibly pursue using the travel time savings. The primary objective of this paper is to quantify the value of time allocated to different activities using two different approaches and to explain, from a behavioral perspective, the similarities and differences observed in the estimates.One of the major challenges associated with estimating the value of time associated with various activities is that activity or time use data sets available to behavioral researchers do not include information about the monetary expenditures incurred in undertaking the Estimating the value of time is of considerable interest to transportation professional...

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Untitled

Cited by 41 publications

References 3 publications

A mixed multinomial logit model analysis of weekend recreational episode type choice

A mixed multinomial logit model analysis of weekend recreational episode type choice

Intershopping duration: an analysis using multiweek data

Joint Analysis of Time Use and Consumer Expenditure Data

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