On the challenges of mobility prediction in smart cities

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dc.contributor.authorBoukhedouma, H.
dc.contributor.authorMeziane, Abdelkrim
dc.contributor.authorHammoudi, S.
dc.contributor.authorBenna, Amel
dc.date.accessioned2024-02-14T09:28:08Z
dc.date.available2024-02-14T09:28:08Z
dc.date.issued2020
dc.description.abstractThe mass of data generated from people’s mobility in smart cities is constantly increasing, thus making a new business for large companies. These data are often used for mobility prediction in order to improve services or even systems such as the development of location-based services, personalized recommendation systems, and mobile communication systems. In this paper, we identify the mobility prediction issues and challenges serving as guideline for researchers and developers in mobility prediction. To this end, we first identify the key concepts and classifications related to mobility prediction. We then, focus on challenges in mobility prediction from a deep literature study. These classifications and challenges are for serving further understanding, development and enhancement of the mobility prediction vision.
dc.identifier.doihttps://doi.org/10.5194/isprs-archives-XLIV-4-W2-2020-17-2020
dc.identifier.urihttps://dl.cerist.dz/handle/CERIST/1019
dc.publisherCopernicus Publications
dc.relation.ispartofseriesThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences; 5th International Conference on Smart Data and Smart Cities
dc.relation.pages17-24
dc.relation.placeNice - France
dc.structureSystèmes d'Information et Image en Santé S2IS
dc.subjectMovement type
dc.subjectSmart city
dc.subjectContext
dc.subjectPrediction
dc.subjectMobility
dc.titleOn the challenges of mobility prediction in smart cities
dc.typeConference paper
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