A Multiple Discrete-Continuous Nested Extreme Value (MDCNEV) Model: Formulation and Application to Non-worker Activity Time-Use and Timing Behavior on Weekdays

This paper formulates a multiple discrete-continuous nested extreme value (MDCNEV) model that relaxes the independently distributed error terms assumption of the multiple discrete-continuous extreme value (MDCEV) model proposed by Bhat (2005 and 2008). The MDCNEV model captures inter-alternative correlations among alternatives in mutually exclusive subsets (or nests) of the choice set, while maintaining the closed-form of probability expressions for any (and all) consumption pattern(s). The MDCNEV model is applied to analyze non-worker out of home discretionary activity time-use and activity timing decisions on weekdays using data from the 2000 San Francisco Bay Area data. The model estimation results provide several insights into the determinants of non-workers’ activity time-use and timing decisions. The MDCNEV model performs better than the MDCEV model in terms of goodness of fit. However, the nesting parameters are very close to 1, indicating low levels of correlation. Nonetheless, even with such low correlation levels, empirical policy simulations indicate non-negligible differences in policy predictions and substitution patterns exhibited by the two models. Experiments conducted using simulated data also corroborate this result.

[1]  R. Kitamura,et al.  An analysis of time allocation to in-home and out-of-home discretionary activities across working days and non- working days , 1999 .

[2]  Chandra R. Bhat,et al.  Comprehensive Model of Worker Nonwork-Activity Time Use and Timing Behavior , 2009 .

[3]  Chandra R. Bhat,et al.  A multiple discrete–continuous extreme value model: formulation and application to discretionary time-use decisions , 2005 .

[4]  Hjp Harry Timmermans,et al.  A learning-based transportation oriented simulation system , 2004 .

[5]  Sivaramakrishnan Srinivasan,et al.  A multiple discrete-continuous model for independent- and joint-discretionary-activity participation decisions , 2006 .

[6]  M. Bradley,et al.  A model for joint choice of daily activity pattern types of household members , 2005 .

[7]  John W. Polak,et al.  Utility of Schedules: Theoretical Model of Departure-Time Choice and Activity-Time Allocation with Application to Individual Activity Schedules , 2004 .

[8]  Shlomo Bekhor,et al.  Link-Nested Logit Model of Route Choice: Overcoming Route Overlapping Problem , 1998 .

[9]  S. Fujii,et al.  Analysis of Individuals’ Joint-Activity Engagement Using a Model System of Activity-Travel Behavior and Time Use , 1999 .

[10]  F. Koppelman,et al.  The generalized nested logit model , 2001 .

[11]  Charles L Purvis,et al.  Activities, Time, and Travel: Changes in Women's Travel Time Expenditures, 1990-2000 , 2005 .

[12]  Emiliano A. Valdez,et al.  Simulating from Exchangeable Archimedean Copulas , 2007, Commun. Stat. Simul. Comput..

[13]  Chandra R. Bhat,et al.  MODELING DEPARTURE TIME CHOICE FOR HOME-BASED NON-WORK TRIPS , 2000 .

[14]  E. Miller,et al.  Modelling activity generation: a utility-based model for activity-agenda formation , 2009 .

[15]  Ta Theo Arentze,et al.  Activity pattern similarity : a multidimensional sequence alignment method , 2002 .

[16]  P. Embrechts,et al.  Quantitative Risk Management: Concepts, Techniques, and Tools , 2005 .

[17]  K. Small.  A Discrete Choice Model for Ordered Alternatives , 1987 .

[18]  H. Fang,et al.  A discrete–continuous model of households’ vehicle choice and usage, with an application to the effects of residential density , 2008 .

[19]  Patricia L. Mokhtarian,et al.  Tradeoffs between Time Allocations to Maintenance Activities/Travel and Discretionary Activities/Travel , 2005 .

[20]  Ram M. Pendyala,et al.  Time use and activity perspectives in travel behavior research , 2002 .

[21]  Laurie A. Garrow,et al.  The importance of synthetic datasets in empirical testing: comparison of NL models and MNL with error components models , 2006 .

[22]  Chandra R. Bhat,et al.  The Impact of Demographics, Built Environment Attributes, Vehicle Characteristics, and Gasoline Prices on Household Vehicle Holdings and Use , 2009 .

[23]  S. Srinivasan,et al.  Modeling household interactions in daily in-home and out-of-home maintenance activity participation , 2005 .

[24]  C. Bhat.  The multiple discrete-continuous extreme value (MDCEV) model : Role of utility function parameters, identification considerations, and model extensions , 2008 .

[25]  Karthik K. Srinivasan,et al.  Integrating Household-Level Mode Choice and Modal Expenditure Decisions in a Developing Country , 2008 .

[26]  Chandra R. Bhat,et al.  Discretionary activity time allocation of individuals between in-home and out-of-home and between weekdays and weekends , 1999 .

[27]  T. Wales,et al.  Estimation of consumer demand systems with binding non-negativity constraints☆ , 1983 .

[28]  Chandra R. Bhat.  A model of post home-arrival activity participation behavior , 1998 .

[29]  Satoshi Fujii,et al.  Analysis of Time Allocation, Departure Time, and Route Choice Behavior Under Congestion Pricing , 2000 .

[30]  You-Lian Chu,et al.  Modeling Workers' Daily Nonwork Activity Participation and Duration , 2005 .

[31]  D. Hensher,et al.  Residential self-selection effects in an activity time-use behavior model , 2009 .

[32]  Moshe Ben-Akiva,et al.  Integration of an Activity-based Model System and a Residential Location Model , 1998 .

[33]  A. Daly,et al.  Flexible substitution patterns in models of mode and time of day choice: new evidence from the UK and the Netherlands , 2007 .

[34]  Kenneth S Kurani,et al.  SYNTHESIS OF PAST ACTIVITY ANALYSIS APPLICATIONS , 1997 .

[35]  M. Ben-Akiva,et al.  Discrete choice models and their applications to short term travel decisions , 1999 .

[36]  Randolph W. Hall,et al.  Handbook of transportation science , 1999 .

[37]  Daniel J. Phaneuf,et al.  Kuhn-Tucker Demand System Approaches to Non-Market Valuation , 2005 .

[38]  F. Koppelman,et al.  Activity-Based Modeling of Travel Demand , 2003 .

[39]  Konstadinos G. Goulias,et al.  On altruists and egoists in activity participation and travel: who are they and do they live together? , 2006 .

[40]  Chandra R. Bhat,et al.  Modifiable Areal Units: Problem or Perception in Modeling of Residential Location Choice? , 2004 .

[41]  Peter E. Rossi,et al.  Modeling Consumer Demand for Variety , 2002 .

[42]  Elisabetta Strazzera,et al.  Modeling Elicitation effects in contingent valuation studies: a Monte Carlo Analysis of the bivariate approach , 2005 .

[43]  S. Fujii,et al.  A DISCRETE-CONTINUOUS ANALYSIS OF TIME ALLOCATION TO TWO TYPES OF DISCRETIONARY ACTIVITIES WHICH ACCOUNTS FOR UNOBSERVED HETEROGENEITY , 1996 .

[44]  D. Ettema,et al.  Modelling the joint choice of activity timing and duration , 2007 .

[45]  M. Bierlaire,et al.  Discrete Choice Methods and their Applications to Short Term Travel Decisions , 1999 .

[46]  Chandra R. Bhat,et al.  A retrospective and prospective survey of time-use research , 1999 .

[47]  Chandra R. Bhat,et al.  Incorporating residential self-selection effects in activity time-use behavior: formulation and application of a joint mixed Multinomial Logit-Multiple Discrete Continuous Extreme Value model , 2008 .