The interaction between activity choice and duration: Application of copula-based and nested-logit models

Document Type : Article

Authors

Department of Civil and Environmental Engineering, Tarbiat Modares University, Tehran, P.O. Box 14115-111, Iran

Abstract

There is a relationship between choosing an activity and duration of that activity, especially for non-mandatory ones. Some previous studies have analyzed the decisions about an activity type and duration independently, though some others have used joint models. This paper contributes to the body of knowledge through using Nested-logit and Copula-based models for assessing the existence of interdependency or a hierarchy between non-mandatory activity choice and the relative duration. In the Nested-logit model, it is assumed that error terms of these decisions are interrelated, though one is influenced by another. In contrast, the Copula-based model can accommodate spatial error correlation across observational units without imposing a restrictive distribution assumption on the dependency structures between the error components. The data from Qazvin, a city in Iran, are used for estimating both Nested-logit and Copula-based models and the best variables explaining both choices for each model have been selected. The final models were compared in terms of log-likelihood at convergence and adjusted likelihood ratio index. The results indicated that there are some common influential observed and unobserved factors between these decisions. Also, Copula-based joint model with ρ_0^2 equals to 0.134 outperforms Nested-logit models and provides a better explanatory power.

Keywords

References

REFERENCES
[1] Bowman, J.L. and Ben-Akiva, M.E., “Activity-based disaggregate travel demand model system with activity schedules”, Transportation research part a: policy and practice, 35(1), pp.1-28 (2001). https://doi.org/10.1016/S0965-8564(99)00043-9.
[2] Ettema, D., Bastin, F., Polak, J. at al., “Modelling the joint choice of activity timing and duration”, Transportation Research Part A: Policy and Practice, 41(9), pp.827-841 (2007). https://doi.org/10.1016/j.tra.2007.03.001.
[3] Amirgholy, M. and Gonzales, E.J.,  “Analytical equilibrium of bicriterion choices with heterogeneous user preferences: application to the morning commute problem”, Transportmetrica B: Transport Dynamics, 5(4), pp.450-482 (2017). https://doi.org/10.1080/21680566.2017.1279087.
[4] Shabanpour, R., Golshani, N., Auld, J. at al., A., “Dynamics of activity time-of-day choice”, Transportation Research Record, 2665(1), pp.51-59 (2017). https://doi.org/10.3141%2F2665-06.
[5] Bhat, C.R., “A multiple discrete–continuous extreme value model: formulation and application to discretionary time-use decisions”, Transportation Research Part B: Methodological, 39(8), pp.679-707 (2005). https://doi.org/10.1016/j.trb.2004.08.003.
[6] Bhat, C.R. and Gossen, R., “A mixed multinomial logit model analysis of weekend recreational episode type choice”, Transportation Research Part B: Methodological, 38(9), pp.767-787 (2004). https://doi.org/10.1016/j.trb.2003.10.003.
[7] Meloni, I., Guala, L. and Loddo, A., “Time allocation to discretionary in-home, out-of-home activities and to trips”, Transportation, 31(1), pp.69-96 (2004). https://doi.org/10.1023/B:PORT.0000007228.44861.ae.
[8] Ding, C., Mishra, S., Lin, Y. at al., “Cross-nested joint model of travel mode and departure time choice for urban commuting trips: Case study in Maryland–Washington, DC Region”, Journal of Urban Planning and Development, 141(4), p.04014036 (2015). https://doi.org/10.1061/(ASCE)UP.1943-5444.0000238.
[9] Yang, L., Zheng, G. and Zhu, X., “Cross-nested logit model for the joint choice of residential location, travel mode, and departure time”, Habitat International, 38, pp.157-166 (2013). https://doi.org/10.1016/j.habitatint.2012.06.002.
[10] Huang, A. and Levinson, D., “Axis of travel: Modeling non-work destination choice with GPS data”, Transportation Research Part C: Emerging Technologies, 58, pp.208-223 (2015). https://doi.org/10.1016/j.trc.2015.03.022.
[11] Newman, J.P. and Bernardin, V.L., “Hierarchical ordering of nests in a joint mode and destination choice model”, Transportation, 37(4), pp.677-688 (2010). https://doi.org/10.1007/s11116-010-9277-x.
[12] Born, K., Yasmin, S., You, D., et al., and Pendyala, R.M., “Joint model of weekend discretionary activity participation and episode duration”, Transportation Research Record, 2413(1), pp.34-44 (2014). https://doi.org/10.3141%2F2413-04.
[13] Ettema, D.F. and Timmermans, H.J., “Activity-based approaches to travel analysis. Pergamon (1997).
[14] Ettema, D. and Timmermans, H., “Modeling departure time choice in the context of activity scheduling behavior”, Transportation Research Record, 1831(1), pp.39-46 (2003). https://doi.org/10.3141%2F1831-05
[15] Axhausen, K.W., “Social networks and travel: Some hypotheses”, Social dimensions of sustainable transport: transatlantic perspectives, pp.90-108 (2005).
[16] Farber, S. and Páez, A., “My car, my friends, and me: a preliminary analysis of automobility and social activity participation”, Journal of Transport Geography, 17(3), pp.216-225 (2009). https://doi.org/10.1016/j.jtrangeo.2008.07.008.
[17] van den Berg, P., Arentze, T. and Timmermans, H., “A latent class accelerated hazard model of social activity duration”, Transportation Research Part A: Policy and Practice, 46(1), pp.12-21 (2012). https://doi.org/10.1016/j.tra.2011.09.015.
[18] Bhat, C.R., “A hazard-based duration model of shopping activity with nonparametric baseline specification and nonparametric control for unobserved heterogeneity”, Transportation Research Part B: Methodological, 30(3), pp.189-207 (1996). https://doi.org/10.1016/0191-2615(95)00029-1.
[19] Ettema, D., Borgers, A.L.O.Y.S. and Timmermans, H.A.R.R.Y., “Competing risk hazard model of activity choice, timing, sequencing, and duration”, Transportation Research Record, 1493, p.101 (1995).
[20] Polak, J., Jones, P., Vythoulkas, P., et al., “Travellers'choice of time to travel under road pricing”, paper presented to the 21st Ptrc summer annual meeting 13-16 September, University Of Manchester institute of science and technology. Tsu Ref, (766) (1993).
[21] Bhat, C.R. and Steed, J.L., “A continuous-time model of departure time choice for urban shopping trips”, Transportation Research Part B: Methodological, 36(3), pp.207-224 (2002). https://doi.org/10.1016/S0191-2615(00)00047-3.
[22] Habib, K.M.N., Day, N. and Miller, E.J., “An investigation of commuting trip timing and mode choice in the Greater Toronto Area: Application of a joint discrete-continuous model”, Transportation Research Part A: Policy and Practice, 43(7), pp.639-653 (2009). https://doi.org/10.1016/j.tra.2009.05.001.
[23] Habib, K.M.N., “Modeling commuting mode choice jointly with work start time and work duration”, Transportation research part A: Policy and practice, 46(1), pp.33-47 (2012). https://doi.org/10.1016/j.tra.2011.09.012.
[24] Popkowski Leszczyc, P.T. and Timmermans, H.J., “Unconditional and conditional competing risk models of activity duration and activity sequencing decisions: An empirical comparison”, Journal of Geographical systems, 4(2), pp.157-170 (2002). https://doi.org/10.3390/ijgi8120560.
[25] Chen, C. and McKnight, C.E., “Does the built environment make a difference? Additional evidence from the daily activity and travel behavior of homemakers living in New York City and suburbs” Journal of Transport Geography, 15(5), pp.380-395 (2007). https://doi.org/10.1016/j.jtrangeo.2006.11.010.
[26] Kapur, A. and Bhat, C.R., “Modeling adults' weekend day-time use by activity purpose and accompaniment arrangement”, Transportation Research Record, 2021(1), pp.18-27 (2007). https://doi.org/10.3141%2F2021-03.
[27] Xu, Z., Kang, J.E. and Chen, R., “A random utility based estimation framework for the household activity pattern problem”, Transportation research procedia, 23, pp.809-826 (2017). https://doi.org/10.1016/j.trpro.2017.05.045.
[28] Frei, C., Mahmassani, H.S. and Frei, A., “Making time count: Traveler activity engagement on urban transit”, Transportation Research Part A: Policy and Practice, 76, pp.58-70 (2015). https://doi.org/10.1016/j.tra.2014.12.007.
[29] Bhat, C.R. and Sen, S., “Household vehicle type holdings and usage: an application of the multiple discrete-continuous extreme value (MDCEV) model”, Transportation Research Part B: Methodological, 40(1), pp.35-53 (2006). https://doi.org/10.1016/j.trb.2005.01.003.
[30] Pawlak, J., Polak, J.W. and Sivakumar, A., “A framework for joint modelling of activity choice, duration, and productivity while travelling”, Transportation Research Part B: Methodological, 106, pp.153-172 (2017). https://doi.org/10.1016/j.trb.2017.10.010.
[31] Ermagun, A., Rashidi, T.H., Arian, A. and Samimi, A., “Mode Choice and Escort Decisions in School Trips: Application of a Copula-Based Model” (No. 14-2560) (2014).
[32] Rasaizadi, A. and Kermanshah, M., “Mode choice and number of non-work stops during the commute: Application of a copula-based joint model”, Scientia Iranica, 25(3), pp.1039-1047 (2018). https://dx.doi.org/10.24200/sci.2017.4194.
[33] Bhat, C.R. and Eluru, N., “A copula-based approach to accommodate residential self-selection effects in travel behavior modeling”, Transportation Research Part B: Methodological, 43(7), pp.749-765 (2009). https://doi.org/10.1016/j.trb.2009.02.001.
[34] Adjei, N. K, T. Brand, and H. Zeeb, “Gender inequality in self-reported health among the elderly in contemporary welfare countries: a cross-country analysis of time use activities, socioeconomic positions and family characteristics” PloS one 12, no. 9 (2017). https://doi.org/10.1371/journal.pone.0184676.
[35] Liu, X. D., Pan, F., Cai, W. L., et al., Correlation and risk measurement modeling: A Markov-switching mixed Clayton copula approach. Reliability Engineering & System Safety, 197, 106808 (2020). https://doi.org/10.1016/j.ress.2020.106808
[36] Pan, Y., S. Ou, L. Zhang, et al., "Modeling risks in dependent systems: A Copula-Bayesian approach." Reliability Engineering & System Safety 188 416-431 (2019). https://doi.org/10.1016/j.ress.2019.03.048
[37] Seyedabrishami, S., A. Rasaizadi, H. S. Rayaprolu, et al., "Car ownership: A joint model for number of cars and fuel types." Transportation Research Procedia 41 (2019). https://doi.org/10.1016/j.trpro.2019.09.101
[38] Train, K.E., “Discrete choice methods with simulation”, Cambridge university press (2009).
[39] Iran, S.C.O. 2012.  (http://www.amar.org.ir in Persian).
[40] Rasaizadi, A. and M. Askari, Effect of family structure on urban areas modal split by using the life cycle concept. Int. J. Hum. Capital Urban Manage, 5(2): p. 165-174 (2020).
[41] Ewing, R., Schroeer, W. and Greene, W., “School location and student travel analysis of factors affecting mode choice”, Transportation research record, 1895(1), pp.55-63 (2004). https://doi.org/10.3141%2F1895-08.
Scientia Iranica
Volume 28, Issue 4
Transactions on Civil Engineering (A)
July and August 2021
Pages 2037-2052

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History

  • Receive Date: 16 February 2020
  • Revise Date: 27 June 2020
  • Accept Date: 03 October 2020

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