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Inverse optimization with endogenous arrival time constraints to calibrate the household activity pattern problem

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  • Chow, Joseph Y.J.
  • Recker, Will W.

Abstract

A parameter estimation method is proposed for calibrating the household activity pattern problem so that it can be used as a disaggregate, activity-based analog of the traffic assignment problem for activity-based travel forecasting. Inverse optimization is proposed for estimating parameters of the household activity pattern problem such that the observed behavior is optimal, the patterns can be replicated, and the distribution of the parameters is consistent. In order to fit the model to both the sequencing of activities and the arrival times to those activities, an inverse problem is formulated as a mixed integer linear programming problem such that coefficients of the objectives are jointly estimated along with the goal arrival times to the activities. The formulation is designed to be structurally similar to the equivalent problems defined by Ahuja and Orlin and can be solved exactly with a cutting plane algorithm. The concept of a unique invariant common prior is used to regularize the estimation method, and proven to converge using the Method of Successive Averages. The inverse model is tested on sample households from the 2001 California Household Travel Survey and results indicate a significant improvement over the standard inverse problem in the literature as well as baseline prescriptive models that do not make use of sample data for calibration. Although, not unexpectedly, the estimated optimization model by itself is a relatively poor forecasting model, it may be used in determining responses of a population to spatio-temporal scenarios where revealed preference data is absent.

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  • Chow, Joseph Y.J. & Recker, Will W., 2012. "Inverse optimization with endogenous arrival time constraints to calibrate the household activity pattern problem," Transportation Research Part B: Methodological, Elsevier, vol. 46(3), pages 463-479.
    • Handle: RePEc:eee:transb:v:46:y:2012:i:3:p:463-479
    DOI: 10.1016/j.trb.2011.11.005
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    1. Lee, Ming S. & McNally, Michael G., 2003. "On the structure of weekly activity/travel patterns," Transportation Research Part A: Policy and Practice, Elsevier, vol. 37(10), pages 823-839, December.
    2. Lee, Ming S. & McNally, Michael G., 2003. "On the Structure of Weekly Activity/Travel Patterns," University of California Transportation Center, Working Papers qt15w464vp, University of California Transportation Center.
    3. Lam, William H. K. & Yin, Yafeng, 2001. "An activity-based time-dependent traffic assignment model," Transportation Research Part B: Methodological, Elsevier, vol. 35(6), pages 549-574, July.
    4. Evans, Alan W, 1972. "On the Theory of the Valuation and Allocation of Time," Scottish Journal of Political Economy, Scottish Economic Society, vol. 19(1), pages 1-17, February.
    5. Maher, M. J., 1983. "Inferences on trip matrices from observations on link volumes: A Bayesian statistical approach," Transportation Research Part B: Methodological, Elsevier, vol. 17(6), pages 435-447, December.
    6. Recker, W. W., 2001. "A bridge between travel demand modeling and activity-based travel analysis," Transportation Research Part B: Methodological, Elsevier, vol. 35(5), pages 481-506, June.
    7. Bar-Gera, Hillel & Boyce, David, 2006. "Solving a non-convex combined travel forecasting model by the method of successive averages with constant step sizes," Transportation Research Part B: Methodological, Elsevier, vol. 40(5), pages 351-367, June.
    8. John C. Harsanyi, 1967. "Games with Incomplete Information Played by "Bayesian" Players, I-III Part I. The Basic Model," Management Science, INFORMS, vol. 14(3), pages 159-182, November.
    9. Recker, W. W., 1995. "The household activity pattern problem: General formulation and solution," Transportation Research Part B: Methodological, Elsevier, vol. 29(1), pages 61-77, February.
    10. Stephan Dempe & Sebastian Lohse, 2006. "Inverse Linear Programming," Lecture Notes in Economics and Mathematical Systems, in: Alberto Seeger (ed.), Recent Advances in Optimization, pages 19-28, Springer.
    11. Recker, Will W & Duan, J. & Wang, H., 2008. "Development of an estimation procedure for an activity-based travel demand model," University of California Transportation Center, Working Papers qt0rz778v6, University of California Transportation Center.
    12. Kim, Hee-Kyung, 2008. "Activity-based Travel Demand Model with Time-use and Microsimulation incorporating Intra-household Interactions," University of California Transportation Center, Working Papers qt4913331c, University of California Transportation Center.
    13. Feinberg, Yossi, 2000. "Characterizing Common Priors in the Form of Posteriors," Journal of Economic Theory, Elsevier, vol. 91(2), pages 127-179, April.
    14. Bhat, Chandra R., 2008. "The multiple discrete-continuous extreme value (MDCEV) model: Role of utility function parameters, identification considerations, and model extensions," Transportation Research Part B: Methodological, Elsevier, vol. 42(3), pages 274-303, March.
    15. Calvete, Herminia I. & Gale, Carmen & Oliveros, Maria-Jose & Sanchez-Valverde, Belen, 2007. "A goal programming approach to vehicle routing problems with soft time windows," European Journal of Operational Research, Elsevier, vol. 177(3), pages 1720-1733, March.
    16. Bhat, Chandra R., 2005. "A multiple discrete-continuous extreme value model: formulation and application to discretionary time-use decisions," Transportation Research Part B: Methodological, Elsevier, vol. 39(8), pages 679-707, September.
    17. Small, Kenneth A, 1982. "The Scheduling of Consumer Activities: Work Trips," American Economic Review, American Economic Association, vol. 72(3), pages 467-479, June.
    18. Gitakrishnan Ramadurai & Satish Ukkusuri, 2010. "Dynamic User Equilibrium Model for Combined Activity-Travel Choices Using Activity-Travel Supernetwork Representation," Networks and Spatial Economics, Springer, vol. 10(2), pages 273-292, June.
    19. Dominique Feillet & Pierre Dejax & Michel Gendreau, 2005. "Traveling Salesman Problems with Profits," Transportation Science, INFORMS, vol. 39(2), pages 188-205, May.
    20. Hunt, J.D. & Stefan, K.J., 2007. "Tour-based microsimulation of urban commercial movements," Transportation Research Part B: Methodological, Elsevier, vol. 41(9), pages 981-1013, November.
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    2. Chan, Timothy C.Y. & Lee, Taewoo, 2018. "Trade-off preservation in inverse multi-objective convex optimization," European Journal of Operational Research, Elsevier, vol. 270(1), pages 25-39.
    3. Hong, Sung-Pil & Kim, Kyung min & Byeon, Geunyeong & Min, Yun-Hong, 2017. "A method to directly derive taste heterogeneity of travellers’ route choice in public transport from observed routes," Transportation Research Part B: Methodological, Elsevier, vol. 95(C), pages 41-52.
    4. Ghobadi, Kimia & Mahmoudzadeh, Houra, 2021. "Inferring linear feasible regions using inverse optimization," European Journal of Operational Research, Elsevier, vol. 290(3), pages 829-843.
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    6. Xiao Fu & William H. K. Lam, 2018. "Modelling joint activity-travel pattern scheduling problem in multi-modal transit networks," Transportation, Springer, vol. 45(1), pages 23-49, January.
    7. Xu, Zhiheng & Kang, Jee Eun & Chen, Roger, 2018. "A random utility based estimation framework for the household activity pattern problem," Transportation Research Part A: Policy and Practice, Elsevier, vol. 114(PB), pages 321-337.
    8. He, Brian Yueshuai & Zhou, Jinkai & Ma, Ziyi & Wang, Ding & Sha, Di & Lee, Mina & Chow, Joseph Y.J. & Ozbay, Kaan, 2021. "A validated multi-agent simulation test bed to evaluate congestion pricing policies on population segments by time-of-day in New York City," Transport Policy, Elsevier, vol. 101(C), pages 145-161.
    9. Mahmoud Javanmardi & Mehran Fasihozaman Langerudi & Ramin Shabanpour & Abolfazl Mohammadian, 2016. "An optimization approach to resolve activity scheduling conflicts in ADAPTS activity-based model," Transportation, Springer, vol. 43(6), pages 1023-1039, November.
    10. Yashar Khayati & Jee Eun Kang & Mark Karwan & Chase Murray, 2021. "Household Activity Pattern Problem with Autonomous Vehicles," Networks and Spatial Economics, Springer, vol. 21(3), pages 609-637, September.
    11. Jee Eun Kang & Will Recker, 2015. "Strategic Hydrogen Refueling Station Locations with Scheduling and Routing Considerations of Individual Vehicles," Transportation Science, INFORMS, vol. 49(4), pages 767-783, November.
    12. Kang, Jee Eun & Chow, Joseph Y.J. & Recker, Will W., 2013. "On activity-based network design problems," Transportation Research Part B: Methodological, Elsevier, vol. 57(C), pages 398-418.
    13. Li Ping Gan & Will Recker, 2013. "Stochastic Preplanned Household Activity Pattern Problem with Uncertain Activity Participation (SHAPP)," Transportation Science, INFORMS, vol. 47(3), pages 439-454, August.
    14. Liu, Xintao & Yan, Wai Yeung & Chow, Joseph Y.J., 2015. "Time-geographic relationships between vector fields of activity patterns and transport systems," Journal of Transport Geography, Elsevier, vol. 42(C), pages 22-33.
    15. Susan Jia Xu & Mehdi Nourinejad & Xuebo Lai & Joseph Y. J. Chow, 2018. "Network Learning via Multiagent Inverse Transportation Problems," Service Science, INFORMS, vol. 52(6), pages 1347-1364, December.
    16. Chow, Joseph Y.J. & Ritchie, Stephen G. & Jeong, Kyungsoo, 2014. "Nonlinear inverse optimization for parameter estimation of commodity-vehicle-decoupled freight assignment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 67(C), pages 71-91.
    17. Xiyuan Ren & Joseph Y. J. Chow, 2023. "Nonparametric estimation of k-modal taste heterogeneity for group level agent-based mixed logit," Papers 2309.13159, arXiv.org.
    18. Chan, Timothy C.Y. & Kaw, Neal, 2020. "Inverse optimization for the recovery of constraint parameters," European Journal of Operational Research, Elsevier, vol. 282(2), pages 415-427.
    19. Ren, Xiyuan & Chow, Joseph Y.J., 2022. "A random-utility-consistent machine learning method to estimate agents’ joint activity scheduling choice from a ubiquitous data set," Transportation Research Part B: Methodological, Elsevier, vol. 166(C), pages 396-418.
    20. Timothy C. Y. Chan & Taewoo Lee & Daria Terekhov, 2019. "Inverse Optimization: Closed-Form Solutions, Geometry, and Goodness of Fit," Management Science, INFORMS, vol. 65(3), pages 1115-1135, March.

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