Evaluating a data-driven approach for choice set identification

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The specification of the choice set for travel behaviour analysis is a non-trivial task, as its size and composition are known to influence the results of model estimation and prediction. Most studies specify the choice set using choice set generation algorithms. These methods can introduce two severe errors to the specified choice set: false negative (not generating observed routes) and false positive (including irrelevant alternatives) errors. Due to increased availability of revealed preference data, like GPS, it is possible to identify the choice set in different way: data-driven. The data-driven path identification approach (DDPI), introduced in this paper, combines all unique routes that are observed for one origin-destination pair into the choice set. This paper evaluates this DDPI approach, by comparing it to two choice set generation methods (breadth-first search on link elimination and labelling). The evaluation is based on three main purposes of choice sets: analysis of alternatives, model estimation and prediction. The conclusion is that the DDPI approach is a useful alternative for choice set identification. The findings indicate that in analysing alternatives, the DDPI approach is most suitable, as it is equal to the observed behaviour. For model estimation the DDPI approach provides a useful alternative to choice set generation methods, as it provides insights into the preferences of individuals. In terms of prediction, the DDPI approach is suitable on a network level, but not on the individual level. The average performance over all alternatives is similar for all choice sets, but on individual level the DDPI method does not predict well.
Original languageEnglish
Title of host publicationProceedings of the International Choice Modelling Conference 2017
Number of pages17
Publication statusPublished - 2017
EventInternational Choice Modelling Conference 2017 - Vineyard Hotel, Cape Town, South Africa
Duration: 3 Apr 20175 Apr 2017


ConferenceInternational Choice Modelling Conference 2017
Abbreviated titleICMC 2017
Country/TerritorySouth Africa
CityCape Town
Internet address


  • data-driven choice set generation
  • BFS-LE approach
  • abelling approach
  • cyclists’ route choice
  • travel behaviour
  • analysis comparison

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