Full metadata record
DC FieldValueLanguage
dc.contributor.authorYang, Shun-Renen_US
dc.contributor.authorSu, Yu-Juen_US
dc.contributor.authorChang, Yao-Yuanen_US
dc.contributor.authorHung, Hui-Nienen_US
dc.date.accessioned2019-06-03T01:08:31Z-
dc.date.available2019-06-03T01:08:31Z-
dc.date.issued2019-04-01en_US
dc.identifier.issn0018-9545en_US
dc.identifier.urihttp://dx.doi.org/10.1109/TVT.2019.2899125en_US
dc.identifier.urihttp://hdl.handle.net/11536/151905-
dc.description.abstractAutonomous and connected cars (ACCs), together with edge computing (EC), have been recognized as a promising solution to achieve green intelligent transportation for smart cities. This paper aims to address short-term traffic prediction, a fundamental enabler for the success of ACC applications, under the European Telecommunications Standards Institute multiaccess EC (MEC) architecture that exhibits constraints different from conventional cloud computing. First, a data-centric experiment platform is designed and implemented to facilitate traffic prediction algorithm development. This paper further proposes a novel short-term traffic prediction model that integrates a traffic light model and a vehicle velocity model, considering limited computing resources of MEC servers. We note that the effects of traffic lights are complicated and have not been rigorously examined in most, if not all, of the related work. This work models the queueing time when a driver arrives at a road intersection and faces a red light. Moreover, to forecast the vehicle velocity, we propose a novel low-complexity semiparametric prediction model considering periodic features and spatial/temporal correlations of dynamic road events. The experiment results demonstrate that our vehicle-velocity prediction model achieves almost equivalent accuracy to the well-known Long Short-Term Memory Neural Network model, requiring much lower computational complexity. Our experiment results also confirm that since our integrated model pays attention to the traffic-light effects and the individual driver behavior, it can more effectively capture the real-time changes of traffic conditions.en_US
dc.language.isoen_USen_US
dc.subjectAutonomous and connected carsen_US
dc.subjectmulti-access edge computing (MEC)en_US
dc.subjecttraffic light modelen_US
dc.subjecttraffic predictionen_US
dc.subjectvehicle velocity modelen_US
dc.titleShort-Term Traffic Prediction for Edge Computing-Enhanced Autonomous and Connected Carsen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/TVT.2019.2899125en_US
dc.identifier.journalIEEE TRANSACTIONS ON VEHICULAR TECHNOLOGYen_US
dc.citation.volume68en_US
dc.citation.issue4en_US
dc.citation.spage3140en_US
dc.citation.epage3153en_US
dc.contributor.department統計學研究所zh_TW
dc.contributor.departmentInstitute of Statisticsen_US
dc.identifier.wosnumberWOS:000465241600008en_US
dc.citation.woscount0en_US
Appears in Collections:Articles