Please use this identifier to cite or link to this item: http://hdl.handle.net/11718/20225
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dc.contributor.authorLaha, Arnab Kumar
dc.contributor.authorPutatunda, Sayan
dc.date.accessioned2018-02-06T04:28:10Z
dc.date.available2018-02-06T04:28:10Z
dc.date.issued2018-07
dc.identifier.urihttp://hdl.handle.net/11718/20225
dc.description.abstractThe prediction of the destination location at the time of pickup is an important problem with potential for substantial impact on the efficiency of a GPS enabled taxi service. While this problem has been explored earlier in the batch data set-up, we propose in this paper new solutions in the streaming data set-up. We examine four incremental learning methods using a Damped window model namely, Multivariate multiple regression, spherical-spherical regression, Randomized spherical K-NN regression and an Ensemble of these methods for their effectiveness in solving the destination prediction problem. The performance of these methods on several large datasets are evaluated using suitably chosen metrics and they were also compared with some other existing methods. The Multivariate multiple regression method and the Ensemble of the three methods are found to be the two best performers. The next pickup location problem is also considered and the aforementioned methods are examined for their suitability using real world datasets. As in the case of destination prediction problem, here also we find that the Multivariate multiple regression method and the Ensemble of the three methods gives better performance than the rest.en_US
dc.language.isoen_USen_US
dc.publisherIndian Institute of Management Ahmedabaden_US
dc.relation.ispartofseriesW.P.;2017-03-02
dc.subjectDirectional Data Analysisen_US
dc.subjectIncremental Learningen_US
dc.subjectIntelligent Transportation Systemsen_US
dc.subjectMultivariate Multiple Regressionen_US
dc.subjectSliding Windowsen_US
dc.subjectStreaming Dataen_US
dc.titleReal time location prediction with taxi - Gps data streamsen_US
dc.typeWorking Paperen_US
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