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dc.contributor.authorHerrmann, Peter
dc.contributor.authorPuka, Ergys
dc.contributor.authorSkoglund, Tor Rune
dc.date.accessioned2021-10-25T09:12:40Z
dc.date.available2021-10-25T09:12:40Z
dc.date.created2021-06-03T16:43:46Z
dc.date.issued2020
dc.identifier.isbn978-1-6654-0394-8
dc.identifier.urihttps://hdl.handle.net/11250/2825242
dc.description.abstractPersonal Information Displays (PID) at bus stops help making the usage of public transport more attractive. If no electric grid is nearby, however, the installation of PIDs is very expensive due to the high wiring costs. To resolve this issue, the partners of the R&D project IoT-STOP develop a novel PID system that will be independent from the access to power lines. The system uses e-papers as displays that can be accessed using a cellular network. To prevent long, energyintensive idle listening, the network receiver operates only when the passenger information, in particular, the Expected Times of Arrival (ETA) of the buses, is updated. Between two updates, the receiver is switched off such that adjustments after sudden events are not possible. Therefore, the update periods have to be carefully selected. In this paper, we introduce a predictor that estimates time intervals between updates. Our method is based on linear regression using samples of previous bus rides to forecast arrival times. Its predictions are applied by an algorithm to detect areas during the journey of a bus at which its ETA at a later stop changes with a certain probability. The forecasted times for passing such areas are then selected to update the PID at this stop. In addition, we present a number of tests of the predictor carried out at some bus stops in Bergen, Norway. The results show that the proposed method indeed predicts sensible update times of the PID systems.en_US
dc.language.isoengen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.ispartofIEEE International Conference on Smart City and Informatization (iSCI)
dc.relation.urihttps://ieeexplore.ieee.org/document/9346327
dc.titleMachine Learning-based Uptime-Prediction for Battery-friendly Passenger Information Displaysen_US
dc.typeChapteren_US
dc.description.versionacceptedVersionen_US
dc.rights.holder© IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.source.pagenumber49-59en_US
dc.identifier.doi10.1109/iSCI50694.2020.00016
dc.identifier.cristin1913621
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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