FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4321502816> ?p ?o ?g. }

• W4321502816 abstract "The conditional generative adversarial network (CGAN) is used in this paper for empirical Bayes (EB) analysis of road crash hotspots. EB is a well-known method for estimating the expected crash frequency of sites (e.g. road segments, intersections) and then prioritising these sites to identify a subset of high priority sites (e.g. hotspots) for additional safety audits/improvements. In contrast to the conventional EB approach, which employs a statistical model such as the negative binomial model (NB-EB) to model crash frequency data, the recently developed CGAN-EB approach uses a conditional generative adversarial network, a form of deep neural network, that can model any form of distributions of the crash frequency data. Previous research has shown that the CGAN-EB performs as well as or better than NB-EB, however that work considered only a small range of crash data characteristics and did not examine the spatial and temporal transferability. In this paper a series of simulation experiments are devised and carried out to assess the CGAN-EB performance across a wide range of conditions and compares it to the NB-EB. The simulation results show that CGAN-EB performs as well as NB-EB when conditions favor the NB-EB model (i.e. data conform to the assumptions of the NB model) and outperforms NB-EB in experiments reflecting conditions frequently encountered in practice (i.e. low sample mean crash rates, and when crash frequency does not follow a log-linear relationship with covariates). Also, temporal and spatial transferability of both approaches were evaluated using field data and both CGAN-EB and NB-EB approaches were found to have similar performance." @default.
• W4321502816 created "2023-02-23" @default.
• W4321502816 creator A5006139866 @default.
• W4321502816 creator A5015875908 @default.
• W4321502816 creator A5054001710 @default.
• W4321502816 date "2023-02-01" @default.
• W4321502816 modified "2023-09-30" @default.
• W4321502816 title "Application of Conditional Deep Generative Networks (CGAN) in empirical bayes estimation of road crash risk and identifying crash hotspots" @default.
• W4321502816 cites W1927248280 @default.
• W4321502816 cites W1943688421 @default.
• W4321502816 cites W1972707698 @default.
• W4321502816 cites W1998428978 @default.
• W4321502816 cites W2003226215 @default.
• W4321502816 cites W2009472238 @default.
• W4321502816 cites W2010816666 @default.
• W4321502816 cites W2015605078 @default.
• W4321502816 cites W2080093988 @default.
• W4321502816 cites W2080403838 @default.
• W4321502816 cites W2105807315 @default.
• W4321502816 cites W2114856138 @default.
• W4321502816 cites W2123763935 @default.
• W4321502816 cites W2127248997 @default.
• W4321502816 cites W2181523240 @default.
• W4321502816 cites W2300247416 @default.
• W4321502816 cites W2331955455 @default.
• W4321502816 cites W2743180505 @default.
• W4321502816 cites W2765811365 @default.
• W4321502816 cites W2777409296 @default.
• W4321502816 cites W2803931329 @default.
• W4321502816 cites W2904042868 @default.
• W4321502816 cites W2910166160 @default.
• W4321502816 cites W2921467240 @default.
• W4321502816 cites W2936522960 @default.
• W4321502816 cites W2941466173 @default.
• W4321502816 cites W2991137082 @default.
• W4321502816 cites W2999641182 @default.
• W4321502816 cites W3035530851 @default.
• W4321502816 cites W3096411517 @default.
• W4321502816 cites W3112197387 @default.
• W4321502816 cites W3117629641 @default.
• W4321502816 cites W3154694233 @default.
• W4321502816 cites W4237197562 @default.
• W4321502816 cites W4282587657 @default.
• W4321502816 cites W4284669228 @default.
• W4321502816 doi "https://doi.org/10.1016/j.ijtst.2023.02.005" @default.
• W4321502816 hasPublicationYear "2023" @default.
• W4321502816 type Work @default.
• W4321502816 citedByCount "0" @default.
• W4321502816 crossrefType "journal-article" @default.
• W4321502816 hasAuthorship W4321502816A5006139866 @default.
• W4321502816 hasAuthorship W4321502816A5015875908 @default.
• W4321502816 hasAuthorship W4321502816A5054001710 @default.
• W4321502816 hasBestOaLocation W43215028161 @default.
• W4321502816 hasConcept C105795698 @default.
• W4321502816 hasConcept C107673813 @default.
• W4321502816 hasConcept C119857082 @default.
• W4321502816 hasConcept C127413603 @default.
• W4321502816 hasConcept C140331021 @default.
• W4321502816 hasConcept C146978453 @default.
• W4321502816 hasConcept C154945302 @default.
• W4321502816 hasConcept C183469790 @default.
• W4321502816 hasConcept C199360897 @default.
• W4321502816 hasConcept C204323151 @default.
• W4321502816 hasConcept C207201462 @default.
• W4321502816 hasConcept C33923547 @default.
• W4321502816 hasConcept C41008148 @default.
• W4321502816 hasConcept C61272859 @default.
• W4321502816 hasConceptScore W4321502816C105795698 @default.
• W4321502816 hasConceptScore W4321502816C107673813 @default.
• W4321502816 hasConceptScore W4321502816C119857082 @default.
• W4321502816 hasConceptScore W4321502816C127413603 @default.
• W4321502816 hasConceptScore W4321502816C140331021 @default.
• W4321502816 hasConceptScore W4321502816C146978453 @default.
• W4321502816 hasConceptScore W4321502816C154945302 @default.
• W4321502816 hasConceptScore W4321502816C183469790 @default.
• W4321502816 hasConceptScore W4321502816C199360897 @default.
• W4321502816 hasConceptScore W4321502816C204323151 @default.
• W4321502816 hasConceptScore W4321502816C207201462 @default.
• W4321502816 hasConceptScore W4321502816C33923547 @default.
• W4321502816 hasConceptScore W4321502816C41008148 @default.
• W4321502816 hasConceptScore W4321502816C61272859 @default.
• W4321502816 hasFunder F4320315159 @default.
• W4321502816 hasLocation W43215028161 @default.
• W4321502816 hasOpenAccess W4321502816 @default.
• W4321502816 hasPrimaryLocation W43215028161 @default.
• W4321502816 hasRelatedWork W2004502153 @default.
• W4321502816 hasRelatedWork W2961085424 @default.
• W4321502816 hasRelatedWork W3046775127 @default.
• W4321502816 hasRelatedWork W3170094116 @default.
• W4321502816 hasRelatedWork W4285260836 @default.
• W4321502816 hasRelatedWork W4286629047 @default.
• W4321502816 hasRelatedWork W4306321456 @default.
• W4321502816 hasRelatedWork W4306674287 @default.
• W4321502816 hasRelatedWork W4321502816 @default.
• W4321502816 hasRelatedWork W4224009465 @default.
• W4321502816 isParatext "false" @default.
• W4321502816 isRetracted "false" @default.
• W4321502816 workType "article" @default.