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Aggregation of alternatives and its influence on prediction
KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Transport Studies, CTS. KTH, School of Architecture and the Built Environment (ABE), Transport Science, System Analysis and Economics.ORCID iD: 0000-0002-6839-8540
KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport and Location Analysis. KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Transport Studies, CTS.
(English)Manuscript (preprint) (Other academic)
Abstract [en]

In car type choice models, alternatives are usually grouped into categories by some of their main characteristics such as make, model, vintage, body type and/or fuel type. Each of these categories contains di erent versions of the cars that are usually not recognized in the applied literature. In this study we empirically investigate whether including the heterogeneity of these versions in the modeling does matter in estimation and prediction or not. We use detailed data on alternatives available on the market down to the versions level of each model, which enables us to account for heterogeneity in the model. We also have Swedish car registry data to represent demand. We estimate separate discrete choice models with diferent methods of correction for alternative aggregation, including nesting structure. These models are estimated based on year 2006 Swedish registry data for new cars, and predict for 2007. The results show that including heterogeneity of cars' versions in the model improves model tness but it does not necessarily improve prediction results.

 

Keyword [en]
Aggregate alternatives, prediction, car type choice, discrete choice modeling, clean vehicles
National Category
Social Sciences
Identifiers
URN: urn:nbn:se:kth:diva-180340OAI: oai:DiVA.org:kth-180340DiVA: diva2:892908
Note

QS 2016

Available from: 2016-01-11 Created: 2016-01-11 Last updated: 2016-01-15Bibliographically approved
In thesis
1. Prediction-driven approaches to discrete choicemodels with application to forecasting car typedemand
Open this publication in new window or tab >>Prediction-driven approaches to discrete choicemodels with application to forecasting car typedemand
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Models that can predict consumer choices are essential technical support fordecision makers in many contexts. The focus of this thesis is to address predictionproblems in discrete choice models and to develop methods to increase the predictivepower of these models with application to car type choice. In this thesis we challengethe common practice of prediction that is using statistical inference to estimateand select the ‘best’ model and project the results to a future situation. We showthat while the inference approaches are powerful explanatory tools in validating theexisting theories, their restrictive theory-driven assumptions make them not tailormadefor predictions. We further explore how modeling considerations for inferenceand prediction are different.Different papers of this thesis present various aspects of the prediction problemand suggest approaches and solutions to each of them.In paper 1, the problem of aggregation over alternatives, and its effects on bothestimation and prediction, is discussed. The focus of paper 2 is the model selectionfor the purpose of improving the predictive power of discrete choice models. Inpaper 3, the problem of consistency when using disaggregate logit models for anaggregate prediction question is discussed, and a model combination is proposedas tool. In paper 4, an updated version of the Swedish car fleet model is appliedto assess a Bonus-Malus policy package. Finally, in the last paper, we present thereal world applications of the Swedish car fleet model where the sensitivity of logitmodels to the specification of choice set affects prediction accuracy.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2016. xv, 27 p.
Series
TRITA-TSC-PHD, 16:002
National Category
Social Sciences
Research subject
Transport Science
Identifiers
urn:nbn:se:kth:diva-180347 (URN)978-91-87353-82-6 (ISBN)
Public defence
2016-02-03, Kollegiesalen, Brinellvägen 8, KTH, Stockholm, 09:00 (English)
Opponent
Supervisors
Note

QC 20160115

Available from: 2016-01-15 Created: 2016-01-11 Last updated: 2016-01-15Bibliographically approved

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Habibi, HabibiSundberg, Marcus
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