A Bivariate Multinomial Probit Model for Trip Scheduling: Bayesian Analysis of the Work Tour

Published Online:https://doi.org/10.1287/trsc.1110.0397

References

  • AASHTO Combatting congestion through leadership, innovation, and resources: A summary report on the 2007 National Congestion Summits. (2007) . American Association of State Highway and Transportation Officials (AASHTO). Accessed July 2010, http://downloads.transportation.org/CTL-1.pdfGoogle Scholar
  • Abkowitz M. D. An analysis of the commuter departure time decision. Transportation (1981) 10(3):283–297CrossrefGoogle Scholar
  • Abou Zeid M., Rossi T. F., Gardner B. Modeling time of day choice in the context of tour and activity based models. Transportation Res. Record (2006) 1981:42–49CrossrefGoogle Scholar
  • Albert J. H., Chib S. Bayesian analysis of binary and polychotomous response data. J. Amer. Statist. Assoc. (1993) 88(422):669–679CrossrefGoogle Scholar
  • Ashiru O., Polak J. W., Noland R. B. Utility of schedules: Theoretical model of departure-time choice and activity-time allocation with application to individual activity schedules. Transportation Res. Record (2004) 1894:84–98CrossrefGoogle Scholar
  • Bhat C. R. Analysis of travel mode and departure time choice for urban shopping trips. Transportation Res. Part B (1998) 32(6):361–371CrossrefGoogle Scholar
  • Bhat C. R., Steed J. L. A continuous-time model of departure time choice for urban shopping trips. Transportation Res. Part B (2002) 36(3):207–224CrossrefGoogle Scholar
  • Bierlaire M. BIOGEME: A free package for the estimation of discrete choice models. (2003) Presentation 3rd Swiss Transportation Research ConferenceMarch 19–21Ascona, SwitzerlandGoogle Scholar
  • Bowman J. L., Bradley M. A., Gibb J. The Sacramento activity-based travel demand model: Estimation and validation results. (2006) Presentation 2006 European Transport ConferenceSeptember 18–20Strasbourg, FranceGoogle Scholar
  • Chib S., Greenberg E. Analysis of multivariate probit models. Biometrika (1998) 85(2):347–361CrossrefGoogle Scholar
  • Chin A. T. H. Influences on commuter trip departure time decisions in Singapore. Transportation Res. Part A (1990) 24(5):321–333CrossrefGoogle Scholar
  • Cressie N. Bayesian smoothing of rates in small geographic areas. J. Regional Sci. (1995) 35(4):659–673CrossrefGoogle Scholar
  • de Jong G., Daly A., Pieters M., van der Hoorn T. The logsum as an evaluation measure: Review of the literature and new results. Transportation Res. Part A (2007) 41(9):874–889Google Scholar
  • Ettema D., Timmermans H. Modeling departure time choice in the context of activity scheduling behavior. Transportation Res. Record (2003) 1831:39–46CrossrefGoogle Scholar
  • Ettema D., Ashiru O., Polak J. W. Modeling timing and duration of activities and trips in response to road-pricing policies. Transportation Res. Record (2004) 1894:1–10CrossrefGoogle Scholar
  • Ettema D., Bastin F., Polak J., Ashiru O. Modeling the joint choice of activity timing and duration. Transportation Res. Part A (2007) 41(9):827–841Google Scholar
  • Gamerman D., Lopes H. F.Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference (2006) 2nd ed.(Chapman and Hall/CRC, Boca Raton, FL) Google Scholar
  • Geweke J., Keane M., Runkle D. Alternative computational approaches to inference in the multinomial probit model. Rev. Econom. Statist. (1994) 76(4):609–632CrossrefGoogle Scholar
  • Golob T. F., Regan A. C. Trucking industry adoption of information technology: A multivariate discrete choice model. Transportation Res. Part C (2002) 10(3):205–228CrossrefGoogle Scholar
  • Hamilton J. D.Time Series Analysis (1994) (Princeton University Press, Princeton, NJ) CrossrefGoogle Scholar
  • Hendrickson C., Plank E. The flexibility of departure times for work trips. Transportation Res. Part A (1984) 18(1):25–36CrossrefGoogle Scholar
  • Holden L., Hauge R., Holden M. Adaptive independent metropolis-hastings. Ann. Appl. Probab. (2009) 19(1):395–413CrossrefGoogle Scholar
  • Huber J., Train K. On the similarity of classical and Bayesian estimates of individual mean partworths. Marketing Lett. (2001) 12:259–269CrossrefGoogle Scholar
  • Kissling W. D., Carl G. Spatial autocorrelation and the selection of simultaneous autoregressive models. Global Ecology and Biogeography (2008) 17(1):59–71Google Scholar
  • Kockelman K. M., Lemp J. D. Anticipating new-highway impacts: Opportunities for welfare analysis and credit-based congestion pricing. Transportation Res. Part A (2011) 45(8):825–838Google Scholar
  • Lee B., Timmermans H. J. P. A latent class accelerated hazard model of activity episode durations. Transportation Res. Part B (2007) 41(4):426–447CrossrefGoogle Scholar
  • Lemp J. D. Capturing random utility maximization behavior in continuous choice data: Application to work tour scheduling. (2009) . Doctoral Dissertation, Department of Civil, Architectural, and Environmental Engineering, University of Texas at Austin, AustinGoogle Scholar
  • Lemp J. D., Kockelman K. M. Understanding and accommodating risk and uncertainty in toll road projects: A review of the literature. Transportation Res. Record (2009) 2132:106–112CrossrefGoogle Scholar
  • Lemp J. D., Kockelman K. M. Empirical investigation of continuous logit for departure time choice with Bayesian methods. Transportation Res. Record (2010) 2165:59–68CrossrefGoogle Scholar
  • Lemp J. D., Kockelman K. M., Damien P. The continuous cross-nested logit model: Formulation and application for departure time choice. Transportation Res. Part B (2010) 44(5):646–661CrossrefGoogle Scholar
  • Lichstein J. W., Simons T. R., Shriner S. A., Franzreb K. E. Spatial autocorrelation and autoregressive models in ecology. Ecological Monographs (2002) 72(3):445–463CrossrefGoogle Scholar
  • McCulloch R., Rossi P. E. An exact likelihood analysis of the multinomial probit model. J. Econometrics (1994) 64(1–2):207–240CrossrefGoogle Scholar
  • McFadden D., Karlquist A., Jundgquist L., Snickbars F., Weibull J. W. Modeling the choice of residential location. Spatial Interaction Theory and Planning Models (1978) (North-Holland, Amsterdam) 75–96Google Scholar
  • McFadden D. A method of simulated moments for estimation of discrete response models without numerical integration. Econometrica (1989) 57(5):995–1026CrossrefGoogle Scholar
  • Okola A. Departure time choice for recreational activities by elderly non-workers. Transportation Res. Record (2003) 1848:86–93CrossrefGoogle Scholar
  • Parent O., LeSage J. P. Using the variance structure of the conditional autoregressive spatial specification to model knowledge spillovers. J. Appl. Econometrics (2008) 23(2):235–256CrossrefGoogle Scholar
  • PB Consult The MORPC travel demand model: Validation and final report. (2005) . Prepared for the Mid-Ohio Regional Planning Commission as part of the MORPC Model Improvement Project, Columbus, OHGoogle Scholar
  • Popuri Y., Ben-Akiva M., Proussaloglou K. Time of day modeling in a tour-based context: The Tel-Aviv experience. Transportation Res. Board (2008) 2076:88–96CrossrefGoogle Scholar
  • Rossi P. E., Allenby G. M., McCulloch R.Bayesian Statistics and Marketing (2005) (John Wiley and Sons, Hoboken, NJ) CrossrefGoogle Scholar
  • Schofer J. L. Summary statement. Proc. USDOT Expert Forum on Road Pricing and Travel Demand Modeling (2005) Alexandria, VA:3–12Google Scholar
  • Small K. A. The scheduling of consumer activities: Work trips. Amer. Econom. Rev. (1982) 72(3):467–479Google Scholar
  • Small K. A. A discrete choice model for ordered alternatives. Econometrica (1987) 55(2):409–424CrossrefGoogle Scholar
  • Small K. A., Rosen H. S. Applied welfare economics with discrete choice models. Econometrica (1981) 49(1):105–130CrossrefGoogle Scholar
  • Small K. A., Noland R., Chu X., Lewis D. Valuation of travel-time savings and predictability in congested conditions for highway user-cost estimation. (1999) . National Cooperative Highway Research Program Report 431, National Academy Press, Washington, DCGoogle Scholar
  • Smith T. E., LeSage J. P., LeSage J. P., Pace R. K. A Bayesian probit model with spatial dependencies. Spatial and Spatiotemporal Econometrics (2004) (Elsevier, Amsterdam) 127–160CrossrefGoogle Scholar
  • Steed J., Bhat C. R. On modeling the departure time choice for home-based social/recreational and shopping trips. Transportation Res. Record (2000) 1706:152–159CrossrefGoogle Scholar
  • Transportation Research Board Metropolitan travel forecasting: Current practice and future direction. (2007) . TRB Special Report 288, Committee for Determination of the State of the Practice in Metropolitan Area Travel Forecasting, Washington, DCGoogle Scholar
  • Vovsha P., Bradley M. A hybrid discrete choice departure time and duration model for scheduling travel tours. Transportation Res. Record (2004) 1894:46–56CrossrefGoogle Scholar
  • Vovsha P., Davidson W., Donnelly R. Making the state of the art the state of the practice: advanced modeling techniques for road pricing. Proc. USDOT Expert Forum on Road Pricing and Travel Demand Modeling (2005) Alexandria, VA:95–122Google Scholar
  • Wang J. J. Timing utility of daily activities and its impact on travel. Transportation Res. Part A (1996) 30(3):189–206CrossrefGoogle Scholar
  • Yee J. L., Niemeier D. A. Analysis of activity duration using the Puget sound transportation panel. Transportation Res. Part A (2000) 34(8):607–624Google Scholar
  • Zhang X., Boscardin W. J., Belin T. R. Bayesian analysis of multivariate nominal measures using multivariate multinomial probit models. Computational Statist. Data Anal. (2008) 52(7):3697–3708CrossrefGoogle Scholar
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