Print Email Facebook Twitter Evaluating a data-driven approach for choice set identification Title Evaluating a data-driven approach for choice set identification Author Ton, D. (TU Delft Transport and Planning) Duives, D.C. (TU Delft Transport and Planning) Cats, O. (TU Delft Transport and Planning) Hoogendoorn, S.P. (TU Delft Transport and Planning; TU Delft Transport and Planning) Department Transport and Planning Date 2017 Abstract 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. Subject data-driven choice set generationBFS-LE approachabelling approachcyclists’ route choicetravel behaviouranalysis comparison To reference this document use: http://resolver.tudelft.nl/uuid:1144ffaa-7eb4-4d65-812f-fd786a06ccfd Source Proceedings of the International Choice Modelling Conference 2017 Event International Choice Modelling Conference 2017, 2017-04-03 → 2017-04-05, Vineyard Hotel, Cape Town, South Africa Part of collection Institutional Repository Document type conference paper Rights © 2017 D. Ton, D.C. Duives, O. Cats, S.P. Hoogendoorn Files PDF 1191_1837_1_PB.pdf 4.84 MB Close viewer /islandora/object/uuid:1144ffaa-7eb4-4d65-812f-fd786a06ccfd/datastream/OBJ/view