Matches in SemOpenAlex for { <https://semopenalex.org/work/W4229016946> ?p ?o ?g. }
Showing items 1 to 96 of
96
with 100 items per page.
- W4229016946 endingPage "71" @default.
- W4229016946 startingPage "71" @default.
- W4229016946 abstract "Traffic and transportation forecasting is a key issue in urban planning aimed to provide a greener and more sustainable environment to residents. Their privacy is a second key issue that requires synthetic travel data. A possible solution is offered by generative models. Here, a variational autoencoder architecture has been trained on a floating car dataset in order to grasp the statistical features of the traffic demand in the city of Rome. The architecture is based on multilayer dense neural networks for encoding and decoding parts. A brief analysis of parameter influence is conducted. The generated trajectories are compared with those in the dataset. The resulting reconstructed synthetic data are employed to compute the traffic fluxes and geographic distribution of parked cars. Further work directions are provided." @default.
- W4229016946 created "2022-05-08" @default.
- W4229016946 creator A5016788120 @default.
- W4229016946 creator A5024062552 @default.
- W4229016946 date "2022-04-29" @default.
- W4229016946 modified "2023-09-25" @default.
- W4229016946 title "Traffic Request Generation through a Variational Auto Encoder Approach" @default.
- W4229016946 cites W1982300822 @default.
- W4229016946 cites W1987228002 @default.
- W4229016946 cites W2064675550 @default.
- W4229016946 cites W2112738128 @default.
- W4229016946 cites W2132646517 @default.
- W4229016946 cites W2617955281 @default.
- W4229016946 cites W2774941429 @default.
- W4229016946 cites W2775721950 @default.
- W4229016946 cites W2808478781 @default.
- W4229016946 cites W2915288435 @default.
- W4229016946 cites W2963000270 @default.
- W4229016946 cites W2979734730 @default.
- W4229016946 cites W3046095786 @default.
- W4229016946 cites W3090910518 @default.
- W4229016946 cites W3096831136 @default.
- W4229016946 cites W3133352370 @default.
- W4229016946 cites W3201057372 @default.
- W4229016946 doi "https://doi.org/10.3390/computers11050071" @default.
- W4229016946 hasPublicationYear "2022" @default.
- W4229016946 type Work @default.
- W4229016946 citedByCount "1" @default.
- W4229016946 countsByYear W42290169462023 @default.
- W4229016946 crossrefType "journal-article" @default.
- W4229016946 hasAuthorship W4229016946A5016788120 @default.
- W4229016946 hasAuthorship W4229016946A5024062552 @default.
- W4229016946 hasBestOaLocation W42290169461 @default.
- W4229016946 hasConcept C101738243 @default.
- W4229016946 hasConcept C108583219 @default.
- W4229016946 hasConcept C111919701 @default.
- W4229016946 hasConcept C11413529 @default.
- W4229016946 hasConcept C118505674 @default.
- W4229016946 hasConcept C119857082 @default.
- W4229016946 hasConcept C123657996 @default.
- W4229016946 hasConcept C124101348 @default.
- W4229016946 hasConcept C125411270 @default.
- W4229016946 hasConcept C154945302 @default.
- W4229016946 hasConcept C166957645 @default.
- W4229016946 hasConcept C167966045 @default.
- W4229016946 hasConcept C171268870 @default.
- W4229016946 hasConcept C199360897 @default.
- W4229016946 hasConcept C205649164 @default.
- W4229016946 hasConcept C26517878 @default.
- W4229016946 hasConcept C2780440489 @default.
- W4229016946 hasConcept C38652104 @default.
- W4229016946 hasConcept C39890363 @default.
- W4229016946 hasConcept C41008148 @default.
- W4229016946 hasConcept C57273362 @default.
- W4229016946 hasConceptScore W4229016946C101738243 @default.
- W4229016946 hasConceptScore W4229016946C108583219 @default.
- W4229016946 hasConceptScore W4229016946C111919701 @default.
- W4229016946 hasConceptScore W4229016946C11413529 @default.
- W4229016946 hasConceptScore W4229016946C118505674 @default.
- W4229016946 hasConceptScore W4229016946C119857082 @default.
- W4229016946 hasConceptScore W4229016946C123657996 @default.
- W4229016946 hasConceptScore W4229016946C124101348 @default.
- W4229016946 hasConceptScore W4229016946C125411270 @default.
- W4229016946 hasConceptScore W4229016946C154945302 @default.
- W4229016946 hasConceptScore W4229016946C166957645 @default.
- W4229016946 hasConceptScore W4229016946C167966045 @default.
- W4229016946 hasConceptScore W4229016946C171268870 @default.
- W4229016946 hasConceptScore W4229016946C199360897 @default.
- W4229016946 hasConceptScore W4229016946C205649164 @default.
- W4229016946 hasConceptScore W4229016946C26517878 @default.
- W4229016946 hasConceptScore W4229016946C2780440489 @default.
- W4229016946 hasConceptScore W4229016946C38652104 @default.
- W4229016946 hasConceptScore W4229016946C39890363 @default.
- W4229016946 hasConceptScore W4229016946C41008148 @default.
- W4229016946 hasConceptScore W4229016946C57273362 @default.
- W4229016946 hasIssue "5" @default.
- W4229016946 hasLocation W42290169461 @default.
- W4229016946 hasLocation W42290169462 @default.
- W4229016946 hasOpenAccess W4229016946 @default.
- W4229016946 hasPrimaryLocation W42290169461 @default.
- W4229016946 hasRelatedWork W1974618110 @default.
- W4229016946 hasRelatedWork W2557924869 @default.
- W4229016946 hasRelatedWork W2669956259 @default.
- W4229016946 hasRelatedWork W2903766720 @default.
- W4229016946 hasRelatedWork W2939353110 @default.
- W4229016946 hasRelatedWork W3214993561 @default.
- W4229016946 hasRelatedWork W4285107510 @default.
- W4229016946 hasRelatedWork W4311648049 @default.
- W4229016946 hasRelatedWork W4380075502 @default.
- W4229016946 hasRelatedWork W4386782329 @default.
- W4229016946 hasVolume "11" @default.
- W4229016946 isParatext "false" @default.
- W4229016946 isRetracted "false" @default.
- W4229016946 workType "article" @default.