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Modeling skeletal components of workers' daily activity schedules Habib, Khandker M. Nurul ; Miller, Eric J

By: Contributor(s): Series: ; 1985Publication details: Transportation research record, 2006Description: s. 88-97Subject(s): Bibl.nr: VTI P8167:1985Location: Abstract: Four sets of econometric models are presented for time use decisions (durations and start times) regarding the basic, regular, and committed components (skeleton) of workers' daily life: gap before work, work, gap after work, and night sleep. Two types of models are compared for each component: multilevel linear models and continuous-time hazard models. The multilevel models consider three-level random effects (temporal, personal, and household) and the hazard models consider individual-based unobserved heterogeneity. On the basis of performance in fitting observed data, hazard models are selected for the first three components, and a multilevel model is selected for the last component. For parametric hazard models, the Gompertz distribution shows promising performance in fitting activity data. The models are estimated by using 2002-2003 Toronto [Canada] Computerized Household Activity Scheduling Elicitor data.
Item type: Reports, conferences, monographs
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Statens väg- och transportforskningsinstitut Available

Four sets of econometric models are presented for time use decisions (durations and start times) regarding the basic, regular, and committed components (skeleton) of workers' daily life: gap before work, work, gap after work, and night sleep. Two types of models are compared for each component: multilevel linear models and continuous-time hazard models. The multilevel models consider three-level random effects (temporal, personal, and household) and the hazard models consider individual-based unobserved heterogeneity. On the basis of performance in fitting observed data, hazard models are selected for the first three components, and a multilevel model is selected for the last component. For parametric hazard models, the Gompertz distribution shows promising performance in fitting activity data. The models are estimated by using 2002-2003 Toronto [Canada] Computerized Household Activity Scheduling Elicitor data.