Matches in SemOpenAlex for { <https://semopenalex.org/work/W3048248255> ?p ?o ?g. }
Showing items 1 to 87 of
87
with 100 items per page.
- W3048248255 abstract "Real time traffic navigation is an important capability in smart transportation technologies, which has been extensively studied these years. Due to the vast development of edge devices, collecting real time traffic data is no longer a problem. However, real traffic navigation is still considered to be a particularly challenging problem because of the time-varying patterns of the traffic flow and unpredictable accidents/congestion. To give accurate and reliable navigation results, predicting the future traffic flow(speed,congestion,volume,etc) in a fast and accurate way is of great importance. In this paper, we adopt the ideas of ensemble learning and develop a two-stage machine learning model to give accurate navigation results. We model the traffic flow as a time series and apply XGBoost algorithm to get accurate predictions on future traffic conditions(1st stage). We then apply the Top K Dijkstra algorithm to find a set of shortest paths from the give start point to the destination as the candidates of the output optimal path. With the prediction results in the 1st stage, we find one optimal path from the candidates as the output of the navigation algorithm. We show that our navigation algorithm can be greatly improved via EOPF(Enhanced Optimal Path Finding), which is based on neural network(2nd stage). We show that our method can be over 7% better than the method without EOPF in many situations, which indicates the effectiveness of our model." @default.
- W3048248255 created "2020-08-13" @default.
- W3048248255 creator A5015486951 @default.
- W3048248255 creator A5067465324 @default.
- W3048248255 creator A5082019982 @default.
- W3048248255 creator A5091143353 @default.
- W3048248255 date "2020-07-01" @default.
- W3048248255 modified "2023-09-25" @default.
- W3048248255 title "Enhance The Performance Of Navigation: A Two-Stage Machine Learning Approach" @default.
- W3048248255 cites W1915929007 @default.
- W3048248255 cites W2103610940 @default.
- W3048248255 cites W2132711183 @default.
- W3048248255 cites W2157825442 @default.
- W3048248255 cites W2792692674 @default.
- W3048248255 cites W2920582503 @default.
- W3048248255 cites W2953303875 @default.
- W3048248255 doi "https://doi.org/10.1109/bigcom51056.2020.00036" @default.
- W3048248255 hasPublicationYear "2020" @default.
- W3048248255 type Work @default.
- W3048248255 sameAs 3048248255 @default.
- W3048248255 citedByCount "0" @default.
- W3048248255 crossrefType "proceedings-article" @default.
- W3048248255 hasAuthorship W3048248255A5015486951 @default.
- W3048248255 hasAuthorship W3048248255A5067465324 @default.
- W3048248255 hasAuthorship W3048248255A5082019982 @default.
- W3048248255 hasAuthorship W3048248255A5091143353 @default.
- W3048248255 hasBestOaLocation W30482482552 @default.
- W3048248255 hasConcept C127413603 @default.
- W3048248255 hasConcept C132525143 @default.
- W3048248255 hasConcept C154945302 @default.
- W3048248255 hasConcept C162307627 @default.
- W3048248255 hasConcept C173870130 @default.
- W3048248255 hasConcept C177264268 @default.
- W3048248255 hasConcept C199360897 @default.
- W3048248255 hasConcept C207512268 @default.
- W3048248255 hasConcept C22212356 @default.
- W3048248255 hasConcept C22590252 @default.
- W3048248255 hasConcept C2524010 @default.
- W3048248255 hasConcept C2777735758 @default.
- W3048248255 hasConcept C2779888511 @default.
- W3048248255 hasConcept C28719098 @default.
- W3048248255 hasConcept C31258907 @default.
- W3048248255 hasConcept C33923547 @default.
- W3048248255 hasConcept C41008148 @default.
- W3048248255 hasConcept C50644808 @default.
- W3048248255 hasConcept C64093975 @default.
- W3048248255 hasConcept C79403827 @default.
- W3048248255 hasConcept C80444323 @default.
- W3048248255 hasConceptScore W3048248255C127413603 @default.
- W3048248255 hasConceptScore W3048248255C132525143 @default.
- W3048248255 hasConceptScore W3048248255C154945302 @default.
- W3048248255 hasConceptScore W3048248255C162307627 @default.
- W3048248255 hasConceptScore W3048248255C173870130 @default.
- W3048248255 hasConceptScore W3048248255C177264268 @default.
- W3048248255 hasConceptScore W3048248255C199360897 @default.
- W3048248255 hasConceptScore W3048248255C207512268 @default.
- W3048248255 hasConceptScore W3048248255C22212356 @default.
- W3048248255 hasConceptScore W3048248255C22590252 @default.
- W3048248255 hasConceptScore W3048248255C2524010 @default.
- W3048248255 hasConceptScore W3048248255C2777735758 @default.
- W3048248255 hasConceptScore W3048248255C2779888511 @default.
- W3048248255 hasConceptScore W3048248255C28719098 @default.
- W3048248255 hasConceptScore W3048248255C31258907 @default.
- W3048248255 hasConceptScore W3048248255C33923547 @default.
- W3048248255 hasConceptScore W3048248255C41008148 @default.
- W3048248255 hasConceptScore W3048248255C50644808 @default.
- W3048248255 hasConceptScore W3048248255C64093975 @default.
- W3048248255 hasConceptScore W3048248255C79403827 @default.
- W3048248255 hasConceptScore W3048248255C80444323 @default.
- W3048248255 hasLocation W30482482551 @default.
- W3048248255 hasLocation W30482482552 @default.
- W3048248255 hasOpenAccess W3048248255 @default.
- W3048248255 hasPrimaryLocation W30482482551 @default.
- W3048248255 hasRelatedWork W14632032 @default.
- W3048248255 hasRelatedWork W3537258 @default.
- W3048248255 hasRelatedWork W499851 @default.
- W3048248255 hasRelatedWork W5626169 @default.
- W3048248255 hasRelatedWork W6157708 @default.
- W3048248255 hasRelatedWork W6194079 @default.
- W3048248255 hasRelatedWork W7339520 @default.
- W3048248255 hasRelatedWork W7954853 @default.
- W3048248255 hasRelatedWork W8175827 @default.
- W3048248255 hasRelatedWork W9080967 @default.
- W3048248255 isParatext "false" @default.
- W3048248255 isRetracted "false" @default.
- W3048248255 magId "3048248255" @default.
- W3048248255 workType "article" @default.