Estimating a mixed-profile MDCEV: case of daily activity type and duration

Abstract Multiple Discrete Continuous Extreme Value (MDCEV) has become popular in the past years. Yet, the model suffers from an ‘empirical identification’ issue that is mainly due to inter-relations between two of its parameters, α and γ. This paper presents a hybrid optimization paradigm (named HELPME) to address this issue in a basic MDCEV formulation and take full advantage of the model by estimating a ‘mixed-profile.’ HELPME benefits from a coarse-to-fine search strategy, in which a customized Electromagnetism-like meta-heuristic precedes a gradient-based approach. The Atlanta Regional Travel Survey (2011) is used to empirically analyze performance of HELPME as well as significance of the accuracy gap between the mixed-profile, and α and γ profiles. As part of the results, it is observed that in-sample fit is significantly improved, percentage error of out-of-sample prediction is reduced up to 97% in a 90% confidence level, and bias of out-of-sample predictions are reduced up to 67%.

[1]  H. Mahmassani,et al.  GLOBAL MAXIMUM LIKELIHOOD ESTIMATION PROCEDURE FOR MULTINOMIAL PROBIT (MNP) MODEL PARAMETERS , 2000 .

[2]  Chandra R. Bhat,et al.  Fractional Split-Distribution Model for Statewide Commodity-Flow Analysis , 2002 .

[3]  Greg M. Allenby,et al.  Multivariate Analysis of Multiple Response Data , 2003 .

[4]  N. McGuckin,et al.  Working Retirement: Travel Trends of the Aging Workforce , 2006 .

[5]  D. Bolduc,et al.  The Multiple Discrete-continuous Extreme Value Model (MDCEV) with Fixed Costs , 2014 .

[6]  Kenneth E. Train,et al.  Discrete Choice Methods with Simulation , 2016 .

[7]  Philip Wolfe,et al.  An algorithm for quadratic programming , 1956 .

[8]  Naveen Eluru,et al.  Evolution of Adults’ Weekday Time Use Patterns from 1992 to 2010: A Canadian Perspective , 2014 .

[9]  Junyi Zhang,et al.  Representing in-home and out-of-home energy consumption behavior in Beijing , 2011 .

[10]  L. Armijo.  Minimization of functions having Lipschitz continuous first partial derivatives. , 1966 .

[11]  Alkin Yurtkuran,et al.  A new Hybrid Electromagnetism-like Algorithm for capacitated vehicle routing problems , 2010, Expert Syst. Appl..

[12]  Chandra R. Bhat,et al.  A household-level activity pattern generation model with an application for Southern California , 2013 .

[13]  Welch Bl.  THE GENERALIZATION OF ‘STUDENT'S’ PROBLEM WHEN SEVERAL DIFFERENT POPULATION VARLANCES ARE INVOLVED , 1947 .

[14]  N. Eluru,et al.  A UNIFIED MODEL SYSTEM OF ACTIVITY TYPE CHOICE, ACTIVITY DURATION, ACTIVITY TIMING, MODE CHOICE, AND DESTINATION CHOICE , 2009 .

[15]  Ching-Jong Liao,et al.  Integrating production and transportation scheduling in a two-stage supply chain , 2015 .

[16]  Akshay Vij,et al.  Hybrid Choice Models: The Identification Problem , 2014 .

[17]  P. Goodwin,et al.  On the asymmetry of the symmetric MAPE , 1999 .

[18]  Rob J Hyndman,et al.  Another look at measures of forecast accuracy , 2006 .

[19]  Joan L. Walker,et al.  Identification of parameters in normal error component logit-mixture (NECLM) models , 2007 .

[20]  B. L. Welch.  The generalisation of student's problems when several different population variances are involved. , 1947, Biometrika.

[21]  Chandra R. Bhat,et al.  A Multiple Discrete-Continuous Nested Extreme Value (MDCNEV) Model: Formulation and Application to Non-worker Activity Time-Use and Timing Behavior on Weekdays , 2010 .

[22]  C. Bhat,et al.  An Efficient Forecasting Procedure for Kuhn-Tucker Consumer Demand Model Systems , 2010 .

[23]  Abdul Rawoof Pinjari,et al.  Analysis of long-distance vacation travel demand in the United States: a multiple discrete–continuous choice framework , 2011 .

[24]  Liang Gao,et al.  An improved electromagnetism-like mechanism algorithm for constrained optimization , 2013, Expert Syst. Appl..

[25]  Shu-Cherng Fang,et al.  An Electromagnetism-like Mechanism for Global Optimization , 2003, J. Glob. Optim..

[26]  S. Srinivasan,et al.  A multidimensional mixed ordered-response model for analyzing weekend activity participation , 2005 .

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

[28]  Chandra R. Bhat,et al.  Joint Model for Perfect and Imperfect Substitute Goods Case: Application to Activity Time-Use Decisions , 2006 .

[29]  W. Greene,et al.  计量经济分析 = Econometric analysis , 2009 .

[30]  Sunil Gupta,et al.  The Shopping Basket: A Model for Multicategory Purchase Incidence Decisions , 1999 .

[31]  G. Freytag.  [CORRELATION AND CAUSALITY]. , 1964, Psychiatrie, Neurologie, und medizinische Psychologie.

[32]  I. Hendel,et al.  Estimating Multiple-Discrete Choice Models: An Application to Computeri-Zzation Returns , 1994 .

[33]  Jeremy W. Mattson,et al.  Travel Behavior and Mobility of Transportation-Disadvantaged Populations: Evidence from the National Household Travel Survey , 2012 .

[34]  Chandra R. Bhat,et al.  An annual time use model for domestic vacation travel , 2008 .

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

[36]  J. Armstrong,et al.  Evaluating Forecasting Methods , 2001 .

[37]  W. Michael Hanemann,et al.  Functional Forms in Discrete/Continuous Choice Models With General Corner Solution , 2008 .

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

[39]  Fred L. Collopy,et al.  Error Measures for Generalizing About Forecasting Methods: Empirical Comparisons , 1992 .

[40]  Naveen Eluru,et al.  A latent segmentation based multiple discrete continuous extreme value model , 2013 .

[41]  Spyros Makridakis,et al.  The M3-Competition: results, conclusions and implications , 2000 .

[42]  Abdelhay A. Sallam,et al.  Swarming of intelligent particles for solving the nonlinear constrained optimization problem , 2001 .