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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

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  • 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
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    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    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.
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    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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).
    10. 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.
    11. 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.
    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.

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