Trip distribution has been demonstrated to be the largest source of error in traditional travel models (need reference-Zhao and Kockelman, 2002). For that reason, logit-based destination choice models have become an increasingly common replacement for gravity models to improve the accuracy of the trip distribution step. Destination choice models are potentially even more advantageous over gravity models for longer distance travel and multinucleated travel regions, and have therefore been widely incorporated in statewide travel models (e.g., Wisconsin, Ohio, Maryland, New Hampshire, Arizona, Oregon, Idaho, California, etc.) and larger metropolitan area travel models (e.g., XXXX).

Destination choice models are a type of trip distribution or spatial interaction model which are formulated as discrete choice models, typically logit models. They can be thought of as a generalization of the traditional and widely used gravity model. In practice, this flexible and extensible formulation allows destination choice models to provide a better behavioral basis for trip distribution than the traditional gravity models, by allowing for a wider range of explanatory variables. Thus, although technically gravity models can be considered a subset or special case of destination choice models, the term “destination choice models” typically is used to identify models that incorporate additional variables beyond size/attractions, impedance/friction factors and constants or k-factors. (see, for example, [1][2][3][4][5][6]).

Destination choice models have consistently demonstrated the ability to better reproduce observed travel patterns than gravity models, both through the incorporation of additional variables, as well as by reflecting more complex statistical assumptions, such as capturing spatial autocorrelation. (ref bernardin et al, 2009)

Advantages and Limitations of Destination Choice Models

It is important to recognize a key advantage offered by destination choice models when compared to the more traditional gravity model with their ability to consider additional factors. At the same time, it is also important to recognize destination choice models in practice today still struggle to explain the spatial distribution of travel. This is due in large measure to the lack of data and the importance of unobserved attributes. In many cases, a destination choice model may be able to double the goodness-of-fit, or explain twice as much of the observed travel patterns than a gravity model, but in the end still explain less than half of the variation in the observed patterns.

Both the advantages and limitations of destination choice models can be understood in terms of the factors that affect travelers' destination choices based on those the models can incorporate and reflect and those they cannot. The table below describes some of these advantages and limitations.

Destination Choice Models in Practice

Destination choice models can be used in aggregate trip-based models as an alternative to gravity models or other spatial interaction models. Destination choice models are standard and ubiquitous in tour-based and activity-based models.

As of 2005, 5% of MPOs were using destination choice models, [7] mostly for trip distribution in aggregate trip-based models. As of 2014, based on a survey by TMIP, 9% of MPOs & DOTs were using a tour-based or activity-based model and an additional 17% were in the process of developing them. Destination choice models are therefore likely currently in use in approximately 15% of travel models and likely to be used in roughly a third of models in the relatively near term future.


  1. Bernardin, V. L., F. Koppelman, and D. Boyce. Enhanced Destination Choice Models Incorporating Agglomeration Related to Trip Chaining While Controlling for Spatial Competition. In Transportation Research Record: Journal of the Transportation Research Board of the National Academies, Washington, D.C., 2009, pp. 143-151.
  2. Chow, L.-F.,, F. Zhao, M.-T. Li, and S.-C. Li. Development and Evaluation of Aggregate Destination Choice Models for Trip Distribution in Florida. In Transportation Research Record: Journal of the Transportation Research Board of the National Academies, Washington, D.C., 2005, pp. 18-27
  3. Jonnalagadda, N., J. Freedman, W. A. Davidison, and J. D. Hunt. Development of Microsimulation Activity-Based Model for San Francisco: Destination and Mode Choice Models. In Transportation Research Record: Journal of the Transportation Research Board of the National Academies, Washington, D.C., 2001, pp. 25-35.
  4. Bhat, C., A. Govindarajan, and V. Pulugata. Disaggregate Attraction-End Choice Modeling: Formulation and Empirical Analysis. In Transportation Research Record: Journal of the Transportation Research Board of the National Academies, Washington, D.C., 1998, pp. 0-68
  5. Borgers, A., and H. Timmermans. Choice Model Specification, Substitution and Spatial Structure Effects: A Simulation Experiment. Regional Science and Urban Economics, Vol. 17, 1987, pp. 29-47
  6. Fotheringham, A. S. Some Theoretical Aspects of Destination Choice and Their Relevance to Production-Constrained Gravity Models. Environment and Planning, Vol. 15A, 1983, pp. 464-488
  7. SR 288-Metropolitan Travel Forecasting Current Practice and Future Direction


This category has the following 2 subcategories, out of 2 total.