A need-based model of multi-day, multi-person activity generation
By T. Arentze, H. Timmermans. 2009
240 citations recorded by the source corpus.
Subfields: General Decision Sciences, Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/ecee081529c908ce05293f446aa0708a9f4e2f4e
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In General Decision Sciences
- Soora Rasouli
- Joseph Y. J. Chow
- W. Recker
- H. Timmermans
- Joan L. Walker
- Caspar G. Chorus
- Stephane Hess
In Sociology and Political Science
- Soora Rasouli
- Joseph Y. J. Chow
- W. Recker
- E. Ehlers
- Ipsita Banerjee
- Elenna R. Dugundji
- H. Timmermans
- Joan L. Walker
Citations in the corpus
- Activity-based models of travel demand: promises, progress and prospects (2014, unknown): “Work along these lines has started (e.g. Arentze, Ettema, & Timmermans, 2009, 2013; Arentze & Timmermans, 2007, 2009, 2011a, 2011b, 2012; Cenani, Arentze, & Timmermans, 2012; Gliebe, 2012; Habib, 2011; Han, Arentze, & Timmermans, 2013; Han, Arentze, Timmermans, Janssens, & Wets, 2011; Joh, Arentze,…”
- Correcting for endogeneity in behavioral choice models with social influence variables (2011, background): “Important avenues of research in this domain include capturing influences from tight social networks such as among household members (see, for example, Bradley and Vovsha, 2005; Gliebe and Koppelman, 2005; Srinivasan and Bhat, 2005; Arentze and Timmermans, 2009; Timmermans and Zhang, 2009) as well a”
- Uncertainty in travel demand forecasting models: literature review and research agenda (2012, methodology): “The international research agenda has moved to the next generation of activity-based models: dynamic activity based models (e.g. Han et al. 2008, 2009, 2010, 2011; Arentze and Timmermans, 2009, 2011; Habib and Miller, 2009).”
- Inverse optimization with endogenous arrival time constraints to calibrate the household activity pattern problem (2012, methodology): “The challenge in solving the inverse MILP is the duality gap in integer programming problems (Williams, 1996), which prevents using the formulation from Ahuja and Orlin (2001) directly. To solve the MILP version, Wang (2009) proposed a cutting plane algorithm along with an LP-relaxed form.”