Multiple discrete-continuous choice models with bounds on consumptions

Shobhit Saxena, Abdul Rawoof Pinjari, Ananya Roy, Rajesh Paleti

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


This paper derives a multiple discrete–continuous (MDC) choice model formulation with constraints that specify upper bounds on consumption. To do so, considering the conventional utility maximization problem of a consumer, the Karush-Kuhn-Tucker (KKT) conditions are laid out for the MDC model with a general set of linear constraints that include inequalities. Subsequently, we derive a model with constraints that accommodate upper bounds on consumptions and an additive utility structure that accommodates lower bounds on consumptions. The likelihood expression for the proposed model takes a closed form. Furthermore, we extend the formulation to impose bounds on an MDC choice model with activity episode-level choice alternatives that accommodates a logical ordering among different episodes of an activity. The proposed models are derived for two different specifications of the outside good utility – (1) nonlinear utility with respect to consumption and (2) linear utility with respect to consumption. The proposed models are applied to an empirical context to analyze activity-level as well as episode-level activity participation and time allocation while considering bounds on time allocations. Empirical results suggest that the models that consider upper bounds on consumption offer a better fit to data, avoid predictions of unrealistically large time allocations, and result in overall better predictions than those from models without bounds. The proposed models are useful in situations, such as microsimulation models of travel demand, where it is crucial to avoid unrealistically large predictions.

Original languageEnglish (US)
Pages (from-to)237-265
Number of pages29
JournalTransportation Research Part A: Policy and Practice
StatePublished - Jul 2021

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Transportation
  • Management Science and Operations Research


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