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<p>Destination choice models are a type of trip distribution or [[Spatial Interaction Models|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.  Destination choice models provide a better behavioral basis for trip distribution by allowing for a wider range of explanatory variables than gravity models.  They have been consistently demonstrated better able to reproduce observed travel patterns than gravity models (see, for example, <ref name = Bernardinetal09>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.</ref><ref name = “Chowetal05”>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</ref><ref name = Jonnalagaddaetal01>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.</ref><ref name = Bhatetal98>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</ref><ref name = BorgersTimmermans87>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</ref><ref name = Fotheringham83>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</ref>).  </p>
 
<p>Destination choice models are a type of trip distribution or [[Spatial Interaction Models|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.  Destination choice models provide a better behavioral basis for trip distribution by allowing for a wider range of explanatory variables than gravity models.  They have been consistently demonstrated better able to reproduce observed travel patterns than gravity models (see, for example, <ref name = Bernardinetal09>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.</ref><ref name = “Chowetal05”>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</ref><ref name = Jonnalagaddaetal01>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.</ref><ref name = Bhatetal98>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</ref><ref name = BorgersTimmermans87>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</ref><ref name = Fotheringham83>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</ref>).  </p>
  
<p>Although technically, gravity models can be considered a subset or special case of destination choice models,<ref name = "Daly82">Daly, A. (1982) 'Estimating Choice Models Containing Attraction Variables', "Transportation Research, Part B: Methodological" Vol. 16, No. 1, pp. 5-15</ref> the term “destination choice models” typically is used to identify models that incorporate additional variables in addition to [[Attractions and Size Variables|size]], [[Impedance|impedance]] and constants or k-factors.  </p>
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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). </p> 
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<p>Although technically, gravity models can be considered a subset or special case of destination choice models,<ref name = "Daly82">Daly, A. (1982) 'Estimating Choice Models Containing Attraction Variables', "Transportation Research, Part B: Methodological" Vol. 16, No. 1, pp. 5-15</ref> the term “destination choice models” typically is used to identify models that incorporate additional variables beyond [[Attractions and Size Variables|size]], [[Impedance|impedance]] and constants or k-factors.  </p>
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<p>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.    </p>
 
  
 
<h1>Use in Practice</h1>
 
<h1>Use in Practice</h1>
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<p>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.    </p>
 
<p>As of 2005, 5% of MPOs were using destination choice models, <ref name = "SR288"> [[SR 288-Metropolitan Travel Forecasting Current Practice and Future Direction]] </ref> mostly for trip distribution in aggregate trip-based models.  As of 2014, based on a survey by [https://www.fhwa.dot.gov/planning/tmip/ 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. </p>  
 
<p>As of 2005, 5% of MPOs were using destination choice models, <ref name = "SR288"> [[SR 288-Metropolitan Travel Forecasting Current Practice and Future Direction]] </ref> mostly for trip distribution in aggregate trip-based models.  As of 2014, based on a survey by [https://www.fhwa.dot.gov/planning/tmip/ 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. </p>  
  

Revision as of 17:49, 24 May 2017


Summary

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. Destination choice models provide a better behavioral basis for trip distribution by allowing for a wider range of explanatory variables than gravity models. They have been consistently demonstrated better able to reproduce observed travel patterns than gravity models (see, for example, [1][2][3][4][5][6]).

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).

Although technically, gravity models can be considered a subset or special case of destination choice models,[7] the term “destination choice models” typically is used to identify models that incorporate additional variables beyond size, impedance and constants or k-factors.


Use 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, [8] 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.

Advantages over Gravity and Remaining Limitations

It is important to recognize, on the one hand, a number of key advantages that destination choice models offer over gravity (and other simpler spatial interaction) models through their ability to consider additional factors. At the same time and on the other hand, it is also important to recognize that current destination choice models still struggle to explain the spatial distribution of travel largely due to lack of data and the importance of unobserved factors. In many cases, for instance, a destination choice model may be able to double the goodness-of-fit or explain twice as much of observed travel patterns than a gravity model and still explain less than half 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 in reality, those that the models can incorporate and reflect and those that they cannot. (Add Table)


References

  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. Daly, A. (1982) 'Estimating Choice Models Containing Attraction Variables', "Transportation Research, Part B: Methodological" Vol. 16, No. 1, pp. 5-15
  8. SR 288-Metropolitan Travel Forecasting Current Practice and Future Direction

Subcategories

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