Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/22443
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dc.contributor.authorAbidin, Ahmad Faisalen_UK
dc.contributor.authorKolberg, Marioen_UK
dc.contributor.authorHussain, Amiren_UK
dc.contributor.editorTrovati, Men_UK
dc.contributor.editorHill, Ren_UK
dc.contributor.editorAnjum, Aen_UK
dc.contributor.editorZhu, SYen_UK
dc.contributor.editorLiu, Len_UK
dc.date.accessioned2015-11-06T23:21:20Z-
dc.date.available2015-11-06T23:21:20Zen_UK
dc.date.issued2015-12en_UK
dc.identifier.urihttp://hdl.handle.net/1893/22443-
dc.description.abstractAccurate bus arrival time prediction is key for improving the attractiveness of public transport, as it helps users better manage their travel schedule. This paper proposes a model of bus arrival time prediction, which aims to improve arrival time accuracy. This model is intended to function as a pre-processing stage to handle real world input data in advance of further processing by a Kalman Filtering model; as such, the model is able to overcome the data processing limitations in existing models, and can improve accuracy of output information. The arrival time is predicted using a Kalman Filter (KF) model, by using information acquired from social network communication, especially Twitter. The KF Model predicts the arrival time by filtering the noise or disturbance during the journey. Twitter is one example of a Big Data source that offers a huge amount of unstructured data that can be analyzed and utilized for improving arrival time predictions. Twitter offers an API to retrieve live, real-time (road traffic) information, and offers semantic analysis of the retrieved twitter data. Twitter data, which has been processed, can be considered as a new input for route calculations and updates. This data will be fed into KF models for further processing to produce a new arrival time estimation.en_UK
dc.language.isoenen_UK
dc.publisherSpringeren_UK
dc.relationAbidin AF, Kolberg M & Hussain A (2015) Integrating Twitter Traffic Information with Kalman Filter Models for Public Transportation Vehicle Arrival Time Prediction. In: Trovati M, Hill R, Anjum A, Zhu S & Liu L (eds.) Big-Data Analytics and Cloud Computing: Theory, Algorithms and Applications. Cham, Switzerland: Springer, pp. 67-82. https://doi.org/10.1007/978-3-319-25313-8_5en_UK
dc.rightsThe publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.en_UK
dc.subjectKalman Filteren_UK
dc.subjectApplication Programming Interfaceen_UK
dc.subjectTwitter Useren_UK
dc.subjectLarge Spikeen_UK
dc.subjectTwitter Dataen_UK
dc.titleIntegrating Twitter Traffic Information with Kalman Filter Models for Public Transportation Vehicle Arrival Time Predictionen_UK
dc.typePart of book or chapter of booken_UK
dc.rights.embargodate3000-01-01en_UK
dc.rights.embargoreason[bookchapter-big-data-final (170715) (1).pdf] The publisher does not allow this work to be made publicly available in this Repository therefore there is an embargo on the full text of the work.en_UK
dc.identifier.doi10.1007/978-3-319-25313-8_5en_UK
dc.citation.spage67en_UK
dc.citation.epage82en_UK
dc.citation.publicationstatusPublisheden_UK
dc.type.statusAM - Accepted Manuscripten_UK
dc.author.emailmko@cs.stir.ac.uken_UK
dc.citation.btitleBig-Data Analytics and Cloud Computing: Theory, Algorithms and Applicationsen_UK
dc.citation.date13/01/2016en_UK
dc.citation.isbn978-3319253114en_UK
dc.citation.isbn978-3-319-25313-8en_UK
dc.publisher.addressCham, Switzerlanden_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.identifier.wtid585147en_UK
dc.contributor.orcid0000-0002-0930-2385en_UK
dc.contributor.orcid0000-0002-8080-082Xen_UK
dc.date.filedepositdate2015-11-06en_UK
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings

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