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A rich-dictionary markov predictor for vehicular trajectory forecasting
dc.creator | Papakostas D., Katsaros D. | en |
dc.date.accessioned | 2023-01-31T09:43:41Z | |
dc.date.available | 2023-01-31T09:43:41Z | |
dc.date.issued | 2018 | |
dc.identifier | 10.1109/ICTAI.2018.00091 | |
dc.identifier.isbn | 9781538674499 | |
dc.identifier.issn | 10823409 | |
dc.identifier.uri | http://hdl.handle.net/11615/77745 | |
dc.description.abstract | Next-location prediction in a VANET system, where each vehicle acts as a network node, is of great importance in intelligent transport systems (ITS) as this property could have a direct and positive effect on network connectivity, traffic management and hence, improve overall ITS safety. In the last few years, the widespread use of GPS navigation systems and wireless communication technology-enabled vehicles has resulted in huge volumes of trajectory data. The task of utilizing this data employing pattern-matching techniques for next-location prediction in an efficient and accurate manner is an ongoing research problem. This paper presents the Rich-Dictionary Markov Predictor (RDM), a protocol for producing online these forecasts by using a pattern matching technique. RDM is fast, accurate and fully parameterized presenting different trade-offs as regards efficiency versus prediction accuracy. We evaluated the effectiveness of RDM via simulation and the results attest that it achieves on the average more than 35% better prediction accuracy and competitive to faster prediction times than other model independent and highly accurate prediction algorithms. © 2018 IEEE. | en |
dc.language.iso | en | en |
dc.source | Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060776949&doi=10.1109%2fICTAI.2018.00091&partnerID=40&md5=6c502f6c1e8388fd731aa6e1e60667eb | |
dc.subject | Advanced traffic management systems | en |
dc.subject | Economic and social effects | en |
dc.subject | Intelligent systems | en |
dc.subject | Location | en |
dc.subject | Navigation systems | en |
dc.subject | Pattern matching | en |
dc.subject | Traffic control | en |
dc.subject | Vehicular ad hoc networks | en |
dc.subject | Intelligent transport systems | en |
dc.subject | Location forecasting | en |
dc.subject | Markov predictors | en |
dc.subject | Next location predictions | en |
dc.subject | Pattern-matching technique | en |
dc.subject | VANETs | en |
dc.subject | Vehicular trajectories | en |
dc.subject | Wireless communication technology | en |
dc.subject | Forecasting | en |
dc.subject | IEEE Computer Society | en |
dc.title | A rich-dictionary markov predictor for vehicular trajectory forecasting | en |
dc.type | conferenceItem | en |
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