dc.rights.license | CC-BY-NC-ND | |
dc.contributor.advisor | Kreveld, M. van | |
dc.contributor.author | Hoekstra, J.C.S. | |
dc.date.accessioned | 2016-02-17T18:01:15Z | |
dc.date.available | 2016-02-17T18:01:15Z | |
dc.date.issued | 2016 | |
dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/21870 | |
dc.description.abstract | This study aims to predict the next journey of travelers by train based on
smart card data. After preprocessing raw data into features describing jour-
neys, the problem is framed as a sequence prediction instance. Domain
modelling issues such as the choice of alphabet, representation of time and
the definition of a sequence are discussed. A base alphabet is constructed,
and closed frequent pattern mining is proposed as a method of algorithmi-
cally extending it. The resulting data encodings are tested against a range
of established sequence prediction algorithms. Results show the All-Kth-
Order-Markov algorithm outperforms other algorithms by a margin. With
regard to pattern encoding, the results are somewhat inconclusive. | |
dc.description.sponsorship | Utrecht University | |
dc.format.extent | 632721 | |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | |
dc.title | Predicting train journeys from smart card data: a real-world application of the sequence prediction problem | |
dc.type.content | Master Thesis | |
dc.rights.accessrights | Open Access | |
dc.subject.keywords | sequence prediction, smart card, ov-chipkaart, pattern mining, sequence mining, domain modelling | |
dc.subject.courseuu | Computing Science | |