Comprehensive Econometric Microsimulator for Daily Activity-Travel Patterns

By Chandra R. Bhat, Jessica Y. Guo, Sivaramakrishnan Srinivasan, Aruna Sivakumar. 2004

413 citations recorded by the source corpus.

Subfields: Automotive Engineering, Transportation

View Paper: https://www.semanticscholar.org/paper/6322d51ca119b2ee7179a50ec19b075e3f8c6981

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In Automotive Engineering

In Transportation

Citations in the corpus

SACSIM: An applied activity-based model system with fine-level spatial and temporal resolution (2010, methodology): “Activity-based model systems have been applied in Portland (Bradley et al. 1998; Bradley et al. 1999), San Francisco (Bradley et al. 2001; Jonnalagadda et al. 2001), New York (Vovsha et al. 2002), Columbus (Vovsha et al. 2003), Dallas (Bhat et al. 2004), and Sacramento.”
Uncertainty in travel demand forecasting models: literature review and research agenda (2012, methodology): “Activity-based models such as CEMDAP (Bhat et al., 2004), FAMOS (Pendyala et al., 2005), TASHA (Miller and Roorda, 2003) and typical activity-based models applied in the United States (e.g. Vovsha et al., 2004) add activity generation to these facets.”
Human mobility: Models and applications (2017, background): “Early quantitative studies of the movements of people and vehicles were held in large US metropolitan areas [2] (see the first chapters of [3] for details), and initial studies of human travel were of scales ranging from international migrations [4, 5, 6, 7] to journey-to-work commuting [8, 9, 10, 1”
A multiple discrete–continuous extreme value model: formulation and application to discretionary time-use decisions (2005, background): “These decisions determine the generation and scheduling of trips (see Bhat et al., 2004 for details of an implementation of an activity-based approach to travel demand modeling).”
A conceptual framework for agent-based modelling of logistics services (2010, background): “…approaches are appropriate to explicitly model various aspects of choice behaviour, to provide explicit representation of tours, and to include specific constraints acting at the individual level (e.g. Arentze and Timmermans, 2005; Bhat et al., 2004; Davidson et al., 2007; Miller and Roorda, 2003).”
Inverse optimization with endogenous arrival time constraints to calibrate the household activity pattern problem (2012, methodology): “For example, the CEMDAP microsimulation framework (Bhat et al., 2004) shown in Pinjari and Bhat (2011) can be modified into a hybrid framework, which we here call Comprehensive Econometric Microsimulator with Household Activity Pattern Problem (CEMHAPP), and shown in Fig.”
Activity-based Travel Demand Analysis (2011, background): “The model systems include: (1) The models developed (or under development) for various planning agencies such as Portland METRO (Bradley, et al., 1998), San Francisco SFCTA (Bradley, et al.; 2001), New York NYMTC (Vovsha, et al., 2002), Columbus MORPC (PB Consult 2005), Sacramento SACOG (Bowman and ”
Residential self-selection effects in an activity time-use behavior model (2009, background): “The parameter on this variable is between 0 and 1, as should theoretically be the case on the size measure (see Bhat et al., 1998 and Daly, 1982).”