IDEAS home Printed from https://ideas.repec.org/a/eee/transa/v104y2017icp1-20.html
   My bibliography  Save this article

Does the social context help with understanding and predicting the choice of activity type and duration? An application of the Multiple Discrete-Continuous Nested Extreme Value model to activity diary data

Author

Listed:
  • Calastri, Chiara
  • Hess, Stephane
  • Daly, Andrew
  • Carrasco, Juan Antonio

Abstract

An understanding of activity choices and duration is a key requirement for better policy making, in transport and beyond. Previous studies have failed to make the important link with individuals’ social context. In this paper, the Multiple Discrete-Continuous Nested Extreme Value (MDCNEV) model is applied to the choice of activity type and duration over the course of two days, using data from the Chilean city of Concepción. In common with other studies, heterogeneity across decision makers is accommodated in the model by analysing the impact of different socio-demographic, mobility and residential location variables on both the activity choice and the time allocation decision. In addition, different social network and social capital measures are found to be significantly correlated with the choice and duration of different activities, and we show how these relationships seem to differ from the effects of socio-demographic variables. Finally, we perform a forecasting exercise using the MDCNEV model, highlighting the differences in substitution patterns from a standard MDCEV model.

Suggested Citation

  • Calastri, Chiara & Hess, Stephane & Daly, Andrew & Carrasco, Juan Antonio, 2017. "Does the social context help with understanding and predicting the choice of activity type and duration? An application of the Multiple Discrete-Continuous Nested Extreme Value model to activity diary," Transportation Research Part A: Policy and Practice, Elsevier, vol. 104(C), pages 1-20.
    • Handle: RePEc:eee:transa:v:104:y:2017:i:c:p:1-20
    DOI: 10.1016/j.tra.2017.07.003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0965856417300848
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tra.2017.07.003?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Bhat, Chandra R. & Sen, Sudeshna, 2006. "Household vehicle type holdings and usage: an application of the multiple discrete-continuous extreme value (MDCEV) model," Transportation Research Part B: Methodological, Elsevier, vol. 40(1), pages 35-53, January.
    2. James J. Heckman, 1977. "Sample Selection Bias As a Specification Error (with an Application to the Estimation of Labor Supply Functions)," NBER Working Papers 0172, National Bureau of Economic Research, Inc.
    3. Bhat, Chandra R. & Srinivasan, Sivaramakrishnan & Sen, Sudeshna, 2006. "A joint model for the perfect and imperfect substitute goods case: Application to activity time-use decisions," Transportation Research Part B: Methodological, Elsevier, vol. 40(10), pages 827-850, December.
    4. Pinjari, Abdul Rawoof & Bhat, Chandra, 2010. "A multiple discrete-continuous nested extreme value (MDCNEV) model: Formulation and application to non-worker activity time-use and timing behavior on weekdays," Transportation Research Part B: Methodological, Elsevier, vol. 44(4), pages 562-583, May.
    5. Chandra Bhat & Konstadinos Goulias & Ram Pendyala & Rajesh Paleti & Raghuprasad Sidharthan & Laura Schmitt & Hsi-Hwa Hu, 2013. "A household-level activity pattern generation model with an application for Southern California," Transportation, Springer, vol. 40(5), pages 1063-1086, September.
    6. José Moore & Juan-Antonio Carrasco & Alejandro Tudela, 2013. "Exploring the links between personal networks, time use, and the spatial distribution of social contacts," Transportation, Springer, vol. 40(4), pages 773-788, July.
    7. Ettema, Dick & Bastin, Fabian & Polak, John & Ashiru, Olu, 2007. "Modelling the joint choice of activity timing and duration," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(9), pages 827-841, November.
    8. Chandra Bhat, 2001. "Modeling the Commute Activity-Travel Pattern of Workers: Formulation and Empirical Analysis," Transportation Science, INFORMS, vol. 35(1), pages 61-79, February.
    9. Dubin, Jeffrey A & McFadden, Daniel L, 1984. "An Econometric Analysis of Residential Electric Appliance Holdings and Consumption," Econometrica, Econometric Society, vol. 52(2), pages 345-362, March.
    10. Bowman, J. L. & Ben-Akiva, M. E., 2001. "Activity-based disaggregate travel demand model system with activity schedules," Transportation Research Part A: Policy and Practice, Elsevier, vol. 35(1), pages 1-28, January.
    11. Sean T. Doherty & Eric J. Miller & Kay W. Axhausen, 2002. "A conceptual model of the weekly household activity/travel scheduling process," Chapters, in: Eliahu Stern & IIan Salomon & Piet H.L. Bovy (ed.), Travel Behaviour, chapter 12, Edward Elgar Publishing.
    12. Juan Antonio Carrasco & Bernie Hogan & Barry Wellman & Eric J Miller, 2008. "Collecting Social Network Data to Study Social Activity-Travel Behavior: An Egocentric Approach," Environment and Planning B, , vol. 35(6), pages 961-980, December.
    13. 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.
    14. Bernardo, Christina & Paleti, Rajesh & Hoklas, Megan & Bhat, Chandra, 2015. "An empirical investigation into the time-use and activity patterns of dual-earner couples with and without young children," Transportation Research Part A: Policy and Practice, Elsevier, vol. 76(C), pages 71-91.
    15. 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.
    16. T Gärling & T Kalén & J Romanus & M Selart & B Vilhelmson, 1998. "Computer Simulation of Household Activity Scheduling," Environment and Planning A, , vol. 30(4), pages 665-679, April.
    17. De Jong, G. C., 1990. "An indirect utility model of car ownership and private car use," European Economic Review, Elsevier, vol. 34(5), pages 971-985, July.
    18. Kowald, Matthias & van den Berg, Pauline & Frei, Andreas & Carrasco, Juan-Antonio & Arentze, Theo & Axhausen, Kay & Mok, Diana & Timmermans, Harry & Wellman, Barry, 2013. "Distance patterns of personal networks in four countries: a comparative study," Journal of Transport Geography, Elsevier, vol. 31(C), pages 236-248.
    19. Castro, Marisol & Bhat, Chandra R. & Pendyala, Ram M. & Jara-Díaz, Sergio R., 2012. "Accommodating multiple constraints in the multiple discrete–continuous extreme value (MDCEV) choice model," Transportation Research Part B: Methodological, Elsevier, vol. 46(6), pages 729-743.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tapia, Rodrigo J. & de Jong, Gerard & Larranaga, Ana M. & Bettella Cybis, Helena B., 2020. "Application of MDCEV to infrastructure planning in regional freight transport," Transportation Research Part A: Policy and Practice, Elsevier, vol. 133(C), pages 255-271.
    2. Bhat, Chandra R., 2018. "A new flexible multiple discrete–continuous extreme value (MDCEV) choice model," Transportation Research Part B: Methodological, Elsevier, vol. 110(C), pages 261-279.
    3. Pellegrini, Andrea & Pinjari, Abdul Rawoof & Maggi, Rico, 2021. "A multiple discrete continuous model of time use that accommodates non-additively separable utility functions along with time and monetary budget constraints," Transportation Research Part A: Policy and Practice, Elsevier, vol. 144(C), pages 37-53.
    4. Palma, David & Hess, Stephane, 2022. "Extending the Multiple Discrete Continuous (MDC) modelling framework to consider complementarity, substitution, and an unobserved budget," Transportation Research Part B: Methodological, Elsevier, vol. 161(C), pages 13-35.
    5. Saxena, Shobhit & Pinjari, Abdul Rawoof & Paleti, Rajesh, 2022. "A multiple discrete-continuous extreme value model with ordered preferences (MDCEV-OP): Modelling framework for episode-level activity participation and time-use analysis," Transportation Research Part B: Methodological, Elsevier, vol. 166(C), pages 259-283.
    6. Chunguang Liu & Xinyu Zuo & Xiaoning Gu & Mengru Shao & Chao Chen, 2023. "Activity Duration under the COVID-19 Pandemic: A Comparative Analysis among Different Urbanized Areas Using a Hazard-Based Duration Model," Sustainability, MDPI, vol. 15(12), pages 1-28, June.
    7. Jara-Díaz, Sergio & Candia, Diego, 2020. "A new look at the value of leisure in two-worker households," Economics of Transportation, Elsevier, vol. 24(C).
    8. La Paix Puello, Lissy & Chowdhury, Saidul & Geurs, Karst, 2019. "Using panel data for modelling duration dynamics of outdoor leisure activities," Journal of choice modelling, Elsevier, vol. 31(C), pages 141-155.
    9. Jiatian Bu & Jie Yin & Yifan Yu & Ye Zhan, 2021. "Identifying the Daily Activity Spaces of Older Adults Living in a High-Density Urban Area: A Study Using the Smartphone-Based Global Positioning System Trajectory in Shanghai," Sustainability, MDPI, vol. 13(9), pages 1-17, April.
    10. Calastri, Chiara & Hess, Stephane & Daly, Andrew & Carrasco, Juan Antonio & Choudhury, Charisma, 2018. "Modelling the loss and retention of contacts in social networks: The role of dyad-level heterogeneity and tie strength," Journal of choice modelling, Elsevier, vol. 29(C), pages 63-77.
    11. Pudāne, Baiba & van Cranenburgh, Sander & Chorus, Caspar G., 2021. "A day in the life with an automated vehicle: Empirical analysis of data from an interactive stated activity-travel survey," Journal of choice modelling, Elsevier, vol. 39(C).
    12. Pan, Xiaofeng & Rasouli, Soora & Timmermans, Harry, 2019. "Modeling social influence using sequential stated adaptation experiments: A study of city trip itinerary choice," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 652-672.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rodrigo J. Tapia & Gerard Jong & Ana M. Larranaga & Helena B. Bettella Cybis, 2021. "Exploring Multiple‐discreteness in Freight Transport. A Multiple Discrete Extreme Value Model Application for Grain Consolidators in Argentina," Networks and Spatial Economics, Springer, vol. 21(3), pages 581-608, September.
    2. Pudāne, Baiba & van Cranenburgh, Sander & Chorus, Caspar G., 2021. "A day in the life with an automated vehicle: Empirical analysis of data from an interactive stated activity-travel survey," Journal of choice modelling, Elsevier, vol. 39(C).
    3. Lai, Xinjun & Lam, William H.K. & Su, Junbiao & Fu, Hui, 2019. "Modelling intra-household interactions in time-use and activity patterns of retired and dual-earner couples," Transportation Research Part A: Policy and Practice, Elsevier, vol. 126(C), pages 172-194.
    4. Lu, Hui & Hess, Stephane & Daly, Andrew & Rohr, Charlene, 2017. "Measuring the impact of alcohol multi-buy promotions on consumers' purchase behaviour," Journal of choice modelling, Elsevier, vol. 24(C), pages 75-95.
    5. Calastri, Chiara & Giergiczny, Marek & Zedrosser, Andreas & Hess, Stephane, 2023. "Modelling activity patterns of wild animals - An application of the multiple discrete-continuous extreme value (MDCEV) model," Journal of choice modelling, Elsevier, vol. 47(C).
    6. Bhat, Chandra R. & Sen, Sudeshna & Eluru, Naveen, 2009. "The impact of demographics, built environment attributes, vehicle characteristics, and gasoline prices on household vehicle holdings and use," Transportation Research Part B: Methodological, Elsevier, vol. 43(1), pages 1-18, January.
    7. Pinjari, Abdul Rawoof & Bhat, Chandra, 2021. "Computationally efficient forecasting procedures for Kuhn-Tucker consumer demand model systems: Application to residential energy consumption analysis," Journal of choice modelling, Elsevier, vol. 39(C).
    8. Tanner, Reto & Bolduc, Denis, 2012. "The Multiple Discrete-Continuous Extreme Value Model (MDCEV) with fixed costs," MPRA Paper 41452, University Library of Munich, Germany.
    9. Hanemann, Michael & Labandeira, Xavier & Labeaga, José M. & Vásquez-Lavín, Felipe, 2024. "Discrete-continuous models of residential energy demand: A comprehensive review," Resource and Energy Economics, Elsevier, vol. 77(C).
    10. Chandra Bhat & Abdul Pinjari, 2014. "Multiple discrete-continuous choice models: a reflective analysis and a prospective view," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 19, pages 427-454, Edward Elgar Publishing.
    11. Jian, Sisi & Rashidi, Taha Hossein & Dixit, Vinayak, 2017. "An analysis of carsharing vehicle choice and utilization patterns using multiple discrete-continuous extreme value (MDCEV) models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 103(C), pages 362-376.
    12. Pinjari, Abdul Rawoof & Augustin, Bertho & Sivaraman, Vijayaraghavan & Faghih Imani, Ahmadreza & Eluru, Naveen & Pendyala, Ram M., 2016. "Stochastic frontier estimation of budgets for Kuhn–Tucker demand systems: Application to activity time-use analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 88(C), pages 117-133.
    13. Bhat, Chandra R., 2018. "A new flexible multiple discrete–continuous extreme value (MDCEV) choice model," Transportation Research Part B: Methodological, Elsevier, vol. 110(C), pages 261-279.
    14. Sikder, Sujan & Pinjari, Abdul Rawoof, 2013. "The benefits of allowing heteroscedastic stochastic distributions in multiple discrete-continuous choice models," Journal of choice modelling, Elsevier, vol. 9(C), pages 39-56.
    15. Md Sami Hasnine & Khandker Nurul Habib, 2020. "Modelling the dynamics between tour-based mode choices and tour-timing choices in daily activity scheduling," Transportation, Springer, vol. 47(5), pages 2635-2669, October.
    16. Jara-Díaz, Sergio & Rosales-Salas, Jorge, 2017. "Beyond transport time: A review of time use modeling," Transportation Research Part A: Policy and Practice, Elsevier, vol. 97(C), pages 209-230.
    17. Ozonder, Gozde & Miller, Eric J., 2021. "Longitudinal investigation of skeletal activity episode timing decisions – A copula approach," Journal of choice modelling, Elsevier, vol. 40(C).
    18. Rajesh Paleti & Rachel Copperman & Chandra Bhat, 2011. "An empirical analysis of children’s after school out-of-home activity-location engagement patterns and time allocation," Transportation, Springer, vol. 38(2), pages 273-303, March.
    19. Xuemei Fu & Zhicai Juan, 2017. "An integrated framework to jointly model decisions of activity time allocation and work-related travel," Transportation Planning and Technology, Taylor & Francis Journals, vol. 40(6), pages 689-705, August.
    20. Saxena, Shobhit & Pinjari, Abdul Rawoof & Paleti, Rajesh, 2022. "A multiple discrete-continuous extreme value model with ordered preferences (MDCEV-OP): Modelling framework for episode-level activity participation and time-use analysis," Transportation Research Part B: Methodological, Elsevier, vol. 166(C), pages 259-283.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:transa:v:104:y:2017:i:c:p:1-20. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/547/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.