Modeling Skeletal Components of Workers’ Daily Activity Schedules

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.

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References

Doherty

S. T.

Miller

E. J.

Axhausen

K. W.

and Gärling

. A Conceptual Model of the Weekly Household Activity/Travel Scheduling Process. In Travel Behavior: Spatial Pattern, Congestion and Modeling ( Stern

Solomon

and Bovy

P. H. L.

, eds.), Edward Elgar Publishing, Cheltenham, United Kingdom, 2001.

Litwin

and Miller

E. J.

. Event-Driven Time-Series-Based Dynamic Model of Decision-Making Processes: Philosophical Background and Conceptual Framework. Presented at 83rd Annual Meeting of the Transportation Research Board, Washington, D.C., 2004.

Bowman

J. L.

The Day Activity Schedule Approach to Travel Demand Analysis. Ph.D. thesis. Massachusetts Institute of Technology, May 1998.

Gärling

Kwan

M. P.

and Golledge

R. G.

. Computational Process Modeling of Household Activity Scheduling. Transportation Research, Vol. 28B, No. 5, 1994, pp. 355–364.

Ettema

Borgers

and Timmermans

. Simulation Model of Activity Scheduling Behavior. In Transportation Research Record 1413, TRB, National Research Council, Washington, D.C., 1993, pp. 1–11.

Kitamura

and Fujii

. Two Computational Process Models of Activity-Travel Behavior. In Theoretical Foundations of Travel Choice Modeling ( Gärling

Laitila

and Westin

, eds.), Elsevier Publishing, Amsterdam, Netherlands, 1998.

Lee

and McNally

M. G.

. On the Structure of Weekly Activity/Travel Patterns. Transportation Research, Vol. 37A, 2003, pp. 823–839.

Frusti

Bhat

C. R.

and Axhausen

K. W.

. Exploratory Analysis of Fixed Commitments in Individual Activity-Travel Patterns. In Transportation Research Record: Journal of the Transportation Research Board, No. 1807, Transportation Research Board of the National Academies, Washington, D.C., 2002, pp. 101–108.

Schlich

and Axhausen

K. W.

. Habitual Travel Behavior: Evidence of a Six-Week Travel Diary. Transportation, Vol. 30, 2003, pp. 13–36.

10.

Miller

E. J.

and Roorda

M. J.

. Prototype Model of Household Activity-Travel Scheduling. In Transportation Research Record: Journal of the Transportation Research Board, No. 1831, Transportation Research Board of the National Academies, Washington, D.C., 2003, pp. 114–121.

11.

Arentze

and Timmermans

. ALBATROSS Version 2.0: A Learning Based Transportation Oriented Simulation System. European Institute of Retailing and Service Studies, 2005.

12.

Speybroeck

Berkvens

Ntsakala

A. M.

Aerts

Hens

Huylenbroeck

G. V.

and Thys

. Classification Trees Versus Multinomial Models in the Analysis of Urban Farming System in Central Africa. Agricultural Systems, Vol. 80, 2004, pp. 133–149.

13.

Zorman

Stiglic

M. M.

Kokol

and Malcic

. The Limitations of Decision Trees and Automatic Learning in Real World Medical Decision Making. Journal of Medical Systems, Vol. 21, No. 6, 1997.

14.

Bhat

C. R.

Guo

J. Y.

Srinivasan

and Sivakumar

. Comprehensive Econometric Microsimulator for Daily Activity Travel Patterns. In Transportation Research Record: Journal of the Transportation Research Board, No. 1894, Transportation Research Board of the National Academies, Washington, D.C., 2004, pp. 57–66.

15.

Bhat

C. R.

Guo

J. Y.

Srinivasan

and Sivakumar

. Activity-Based Travel-Demand Modeling for Metropolitan Areas in Texas: A Micro-Simulation Framework for Forecasting. Research Report 4080-4. Center for Transportation Research, University of Texas, Austin, Feb. 2003.

16.

Pendyala

R. M.

Kitamura

Kikuchi

Yamamoto

and Fujii

. Florida Activity Mobility Simulator: Overview and Preliminary Validation Results. In Transportation Research Record: Journal of the Transportation Research Board, No. 1921, Transportation Research Board of the National Academies, Washington, D.C., 2005, pp. 123–130.

17.

Miller

E. J.

Propositions for Modeling Household Decision-Making. In Integrated Land-Use and Transportation Models: Behavioral Foundations ( Lee-Gosselin

and Doherty

, eds.), Elsevier, New York, 2005.

18.

Doherty

S. T.

Nemeth

Roorda

and Miller

E. J.

. Computerized Household Activity-Scheduling Survey for Toronto, Canada, Area: Design and Assessment. In Transportation Research Record: Journal of the Transportation Research Board, No. 1894, Transportation Research Board of the National Academies, Washington, D.C., 2004, pp. 140–149.

19.

Goulias

K. G.

Multilevel Analysis of Daily Time Use and Time Allocation to Activity Types Accounting for Complex Covariance Structures Using Correlated Random Effects. Transportation, Vol. 29, 2002, pp. 31–48.

20.

Habib

K. M. N.

and Miller

E. J.

. Modeling Individuals’ Frequency and Time Allocation Behavior for Shopping Activities Considering Household-Level Random Effects. In Transportation Research Record: Journal of the Transportation Research Board, No. 1985, Transportation Research Board of the National Academies, Washington, D.C., 2006, pp. 78–87.

21.

Bryk

and Raudenbush

S. W.

. Hierarchical Linear Models for Social and Behavioral Research: Application and Data Analysis Methods. Sage, Newbury Park, Calif., 2001.

22.

McCullagh

and Schenker

. Generalized Linear Models, 2nd ed. Chapman and Hill, London, 1989.

23.

Rabe-Hesketh

Toulopoulou

and Murray

R. M.

. Multilevel Modeling of Cognitive Function in Schizophrenic Patients and Their First Degree Relatives. Multivariate Behavioral Research, Vol. 36, No. 2 2001, pp. 279–298.

24.

Skrondal

and Rabe-Hesketh

. Generalized Latent Variable Modeling: Multilevel, Longitudinal and Structural Equation Models. Chapman & Hall/CRC, Boca Raton, Fla., 2004.

25.

Vermunt

J. K.

An EM Algorithm for Estimations of Parametric and Nonparametric Hierarchical Nonlinear Model. Statistica Neerlandica, Vol. 58, No. 2 2004, pp. 220–233.

26.

Rabe-Hesketh

Skrondal

and Pickles

. Reliable Estimation of Generalized Linear Mixed Models Using Adaptive Quadrature. Stata Journal, Vol. 2, No. 1 2002, pp. 1–21.

27.

Stata Base Reference Manual. Stata Press, College Station, Tex., 2003.

28.

Gould

and Scribney

. Maximum Likelihood Estimation with Stata. Stata Press, College Station, Tex., 1999.

29.

Habib

K. M. N.

and Miller

E. J.

. Modeling Duration of Work/School Episode Using Activity Diary Data for the Specification of Activity-Travel Scheduler. Presented at the PROCESSUS Second International Colloquium on the Behavioral Foundation of Land-Use Transportation Models: Frameworks, Models and Application, Toronto, Canada, June 12–15, 2005.

30.

Cox

D. R.

Partial Likelihood. Biometrika, Vol. 62, 1976, pp. 269–276.

31.

Akaike

A New Look at the Statistical Model Identification. IEEE Transactions and Automatic Control, Vol. AC-19, 1974, pp. 716–723.

32.

Doherty

S. T.

and Miller

E. J.

. A Computerized Household Activity Scheduling Survey. Transportation, Vol. 21, No. 1 2000, pp. 75–97.

33.

Roorda

M. J.

and Miller

E. J.

. Toronto Area Panel Survey: Demonstrating the Benefits of a Multiple Instrument Panel Survey. Presented at Seventh International Conference on Travel Methods, Costa Rica, Aug. 1–2, 2004.

34.

Adham

S. A.

and Walker

S. G.

. A Multivariate Gompertz Type Distribution. Journal of Applied Statistics, Vol. 28, No. 8 2001, pp. 1051–1065.

35.

Sanchez-Choliz

J. G.

and Fatas-Villafranca

. “S-Shaped” Economic Dynamics: The Logistic and Gompertz Curve Generalization. Electronic Journal of Evolutionary Modeling and Economic Dynamics, No. 1048, 2005. http://www.e-jemed.org/1048/index.php.