dc.contributor.author | Menz, L | |
dc.contributor.author | Herberth, R | |
dc.contributor.author | Luo, C | |
dc.contributor.author | Gauterin, F | |
dc.contributor.author | Gerlicher, A | |
dc.contributor.author | Wang, Q | |
dc.date.accessioned | 2019-02-26T15:35:50Z | |
dc.date.issued | 2018-06-11 | |
dc.description.abstract | The prediction of an individual's future locations is a significant part of scientific researches. While a variety of solutions have been investigated for the prediction of future locations, predicting departure and arrival times at predicted locations is a task with higher complexity and less attention. While the challenges of combining spatial and temporal information have been stated in various works, the proposed solutions lack accuracy and robustness. This paper proposes a simple yet effective way to predict not only an individual's future location, but also most probable departure and arrival times as well as the most probable route from origin to destination. | en_GB |
dc.identifier.citation | 2018 IEEE Wireless Communications and Networking Conference (WCNC), 15-18 April 2018, Barcelona Spain | en_GB |
dc.identifier.doi | 10.1109/WCNC.2018.8377086 | |
dc.identifier.uri | http://hdl.handle.net/10871/36077 | |
dc.language.iso | en | en_GB |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_GB |
dc.rights | © 2018 IEEE. All rights reserved | en_GB |
dc.subject | Markov processes | en_GB |
dc.subject | Predictive models | en_GB |
dc.subject | Probability density function | en_GB |
dc.subject | Task analysis | en_GB |
dc.subject | Complexity theory | en_GB |
dc.subject | Conferences | en_GB |
dc.subject | Spatial databases | en_GB |
dc.subject | Mobility behaviour | en_GB |
dc.subject | mobility prediction | en_GB |
dc.subject | Markov model | en_GB |
dc.subject | probability density function | en_GB |
dc.title | An improved method for mobility prediction using a Markov model and density estimation | en_GB |
dc.type | Conference proceedings | en_GB |
dc.date.available | 2019-02-26T15:35:50Z | |
dc.identifier.isbn | 9781538617342 | |
dc.identifier.issn | 1525-3511 | |
dc.description | This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record | en_GB |
dc.rights.uri | http://www.rioxx.net/licenses/all-rights-reserved | en_GB |
dcterms.dateAccepted | 2017-12-13 | |
rioxxterms.version | NA | en_GB |
rioxxterms.licenseref.startdate | 2017-12-13 | |
rioxxterms.type | Conference Paper/Proceeding/Abstract | en_GB |
refterms.dateFOA | 2019-02-26T15:35:56Z | |
refterms.panel | B | en_GB |