Matches in SemOpenAlex for { <https://semopenalex.org/work/W3048607697> ?p ?o ?g. }
Showing items 1 to 88 of
88
with 100 items per page.
- W3048607697 endingPage "9789" @default.
- W3048607697 startingPage "9773" @default.
- W3048607697 abstract "Intelligent Transportation System (ITS) intends to provide progressive services linking different modes of transportation and related service system. Road transportation is one of the major and complex entity of the traffic management system. Traffic flow prediction (TFP) contributessignificantrole inpredicting various parameters for road transportation thatgenerates stochastic and nonlineardata through sensors. Traffic flow is demarcated as the average number of vehicles present in a specific region given the historical flow data.Accurate forecasting of macroscopic parameters such as volume, density, speed and flowof traffic can improve the efficiency of traffic management system.In order to predict traffic flow, spatial and temporal traffic features act as a raw data input for the predicting models. Most of the shallow models are incapable to reveal bothspatiotemporal information in big data. Deep neural networks (DNNs) have recently highlighted the potentiality of capturing andextracting important features for various application frommassive dataset.Our work depictsthe current state-of-the-art deep learning techniques (DL) and itsinspirations in TFP incorporating various contextual factorssuch as construction zones, weather conditions, special events, traffic incidents, weekdays and holidays apart from spatiotemporalfeatures for predicting the flow.Finally, provides open challengesyet to be exploredfurther for enhancing deep learning techniques and approachesin forecasting accurate flow." @default.
- W3048607697 created "2020-08-18" @default.
- W3048607697 creator A5006161125 @default.
- W3048607697 creator A5027042757 @default.
- W3048607697 date "2020-02-14" @default.
- W3048607697 modified "2023-09-24" @default.
- W3048607697 title "Deep Learning Techniques for Traffic Flow Prediction in Intelligent Transportation System: A Survey" @default.
- W3048607697 cites W2036785686 @default.
- W3048607697 cites W2059128538 @default.
- W3048607697 cites W2076063813 @default.
- W3048607697 cites W2141125852 @default.
- W3048607697 cites W2149600041 @default.
- W3048607697 cites W2165991108 @default.
- W3048607697 cites W2460404912 @default.
- W3048607697 cites W2504266609 @default.
- W3048607697 cites W2888956734 @default.
- W3048607697 cites W2899443545 @default.
- W3048607697 cites W2940640769 @default.
- W3048607697 cites W2947327518 @default.
- W3048607697 hasPublicationYear "2020" @default.
- W3048607697 type Work @default.
- W3048607697 sameAs 3048607697 @default.
- W3048607697 citedByCount "0" @default.
- W3048607697 crossrefType "journal-article" @default.
- W3048607697 hasAuthorship W3048607697A5006161125 @default.
- W3048607697 hasAuthorship W3048607697A5027042757 @default.
- W3048607697 hasConcept C108583219 @default.
- W3048607697 hasConcept C114809511 @default.
- W3048607697 hasConcept C119857082 @default.
- W3048607697 hasConcept C124101348 @default.
- W3048607697 hasConcept C126255220 @default.
- W3048607697 hasConcept C127413603 @default.
- W3048607697 hasConcept C154945302 @default.
- W3048607697 hasConcept C207512268 @default.
- W3048607697 hasConcept C22212356 @default.
- W3048607697 hasConcept C33923547 @default.
- W3048607697 hasConcept C38652104 @default.
- W3048607697 hasConcept C41008148 @default.
- W3048607697 hasConcept C42693407 @default.
- W3048607697 hasConcept C47796450 @default.
- W3048607697 hasConcept C50644808 @default.
- W3048607697 hasConcept C75684735 @default.
- W3048607697 hasConceptScore W3048607697C108583219 @default.
- W3048607697 hasConceptScore W3048607697C114809511 @default.
- W3048607697 hasConceptScore W3048607697C119857082 @default.
- W3048607697 hasConceptScore W3048607697C124101348 @default.
- W3048607697 hasConceptScore W3048607697C126255220 @default.
- W3048607697 hasConceptScore W3048607697C127413603 @default.
- W3048607697 hasConceptScore W3048607697C154945302 @default.
- W3048607697 hasConceptScore W3048607697C207512268 @default.
- W3048607697 hasConceptScore W3048607697C22212356 @default.
- W3048607697 hasConceptScore W3048607697C33923547 @default.
- W3048607697 hasConceptScore W3048607697C38652104 @default.
- W3048607697 hasConceptScore W3048607697C41008148 @default.
- W3048607697 hasConceptScore W3048607697C42693407 @default.
- W3048607697 hasConceptScore W3048607697C47796450 @default.
- W3048607697 hasConceptScore W3048607697C50644808 @default.
- W3048607697 hasConceptScore W3048607697C75684735 @default.
- W3048607697 hasLocation W30486076971 @default.
- W3048607697 hasOpenAccess W3048607697 @default.
- W3048607697 hasPrimaryLocation W30486076971 @default.
- W3048607697 hasRelatedWork W2048968079 @default.
- W3048607697 hasRelatedWork W2101978968 @default.
- W3048607697 hasRelatedWork W2361368645 @default.
- W3048607697 hasRelatedWork W2528428342 @default.
- W3048607697 hasRelatedWork W2579027196 @default.
- W3048607697 hasRelatedWork W2593566730 @default.
- W3048607697 hasRelatedWork W2764195644 @default.
- W3048607697 hasRelatedWork W2802835993 @default.
- W3048607697 hasRelatedWork W2897410508 @default.
- W3048607697 hasRelatedWork W2903775874 @default.
- W3048607697 hasRelatedWork W2904346290 @default.
- W3048607697 hasRelatedWork W2952355076 @default.
- W3048607697 hasRelatedWork W3045599474 @default.
- W3048607697 hasRelatedWork W3092194021 @default.
- W3048607697 hasRelatedWork W3094951126 @default.
- W3048607697 hasRelatedWork W3131310266 @default.
- W3048607697 hasRelatedWork W3139828765 @default.
- W3048607697 hasRelatedWork W3153201098 @default.
- W3048607697 hasRelatedWork W316528581 @default.
- W3048607697 hasRelatedWork W3193812480 @default.
- W3048607697 hasVolume "82" @default.
- W3048607697 isParatext "false" @default.
- W3048607697 isRetracted "false" @default.
- W3048607697 magId "3048607697" @default.
- W3048607697 workType "article" @default.