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Nonparametric identification of daily activity durations using kernel density estimators

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  • Kharoufeh, Jeffrey P.
  • Goulias, Konstadinos G.

Abstract

Modeling of activity duration has become an important aspect in characterizing activity and travel patterns. The standard approach for analyzing activity duration is to use hazard-based models to account for the duration dependence within an activity while estimating covariate effects and heterogeneity. The effects of covariates and heterogeneity on duration have been modeled using parametric and nonparametric regression techniques. In this paper, we present a nonparametric pattern recognition approach that can precede hazard-based duration modeling to identify heterogeneity patterns that may undermine duration modeling if not detected. The technique utilizes a kernel estimate of the probability density function (pdf) of various activity durations and allows for statistical comparison of distributions to evaluate differences between groups of individuals. Preliminary testing on the first wave of a panel survey indicates that the approach is insightful in evaluating covariate effects while allowing visual inspection of heterogeneity in the data.

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  • Kharoufeh, Jeffrey P. & Goulias, Konstadinos G., 2002. "Nonparametric identification of daily activity durations using kernel density estimators," Transportation Research Part B: Methodological, Elsevier, vol. 36(1), pages 59-82, January.
    • Handle: RePEc:eee:transb:v:36:y:2002:i:1:p:59-82
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    References listed on IDEAS

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    1. Goulias, Konstadinos G., 1992. "Forecasting the Impact of Sociodemographic Changes On Travel Demand: Experiments With a Dynamic Microsimulation Model System," University of California Transportation Center, Working Papers qt5zj9m2wm, University of California Transportation Center.
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    10. Bell, Brian D & Pitt, Michael K, 1998. "Trade Union Decline and the Distribution of Wages in the UK: Evidence from Kernel Density Estimation," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 60(4), pages 509-528, November.
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