A household-level activity pattern generation model with an application for Southern California
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This paper develops and estimates a Multiple Discrete Continuous Extreme Value (MDCEV) model of household activity generation that jointly predicts the activity participation decisions of all individuals in a household by activity purpose and the precise combination of individuals participating. The model is estimated on a sample obtained from the Post Census Regional Household Travel Survey conducted by the South California Association of Governments (SCAG) in the year 2000. A host of household, individual, and residential neighborhood accessibility measures are used as explanatory variables. The results reveal that, in addition to household and individual demographics, the built environment of the home zone also impacts the activity participation levels and durations of households. A validation exercise is undertaken to evaluate the ability of the proposed model to predict participation levels and durations. In addition to providing richness in behavioral detail, the model can be easily embedded in an activity-based microsimulation framework and is computationally efficient as it obviates the need for several hierarchical sub-models typically used in extant activity-based systems to generate activity patterns.
At the time of publication C.R. Bhat, and R. Sidharthan were at the University of Texas at Austin; K.G. Goulias was at the University of California, Santa Barbara; R.M. Pendyala was at Arizona State University; R. Paleti was at Parsons Brinckerhoff; L. Schmitt was at Georgia Institute of Technology; and H. Hu was at Southern California Association of Governments.