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Measuring accessibility of bus system based on multi-source traffic data / Yufan Zuo in Geo-spatial Information Science, vol 23 n° 3 (September 2020)  

Public [article]  inGeo-spatial Information Science > vol 23 n° 3 (September 2020) . - pp 248 - 257 Titre : Measuring accessibility of bus system based on multi-source traffic data Type de document : Article/Communication Auteurs : Yufan Zuo, Auteur ; Zhiyuan Liu, Auteur ; Xiao Fu, Auteur Année de publication : 2020 Article en page(s) : pp 248 - 257 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] accessibilité [Termes IGN] approche holistique [Termes IGN] données multisources [Termes IGN] données spatiotemporelles [Termes IGN] modélisation spatio-temporelle [Termes IGN] planification urbaine [Termes IGN] point d'intérêt [Termes IGN] Shenzhen [Termes IGN] trafic routier [Termes IGN] transport collectif Résumé : (auteur) Accessibility is a representative indicator for evaluating the supply of bus system. Traditional studies have evaluated the accessibility from different aspects. Considering the interaction among land use, bus timetable arrangement and individual factors, a more holistic accessibility measurement is proposed to combine static and dynamic characteristics from multisource traffic data. The rationale of the proposed model is verified by a case study of bus system in Shenzhen, China, which is carried out to find the spatial and temporal discrepancy of service of bus system. It is found that the adjustment of bus schedule to time-varying travel demand can affect accessibility of bus system and that Land-use development, average bus speed and bus facilities all have positive effects on accessibility of bus system. These findings provide significant reference for transport planning and policy-making. The proposed model is not limited to accessibility measuring of bus system, but also applicable to other travel modes. Numéro de notice : A2020-564 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10095020.2020.1783189 Date de publication en ligne : 24/07/2020 En ligne : https://doi.org/10.1080/10095020.2020.1783189 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95881 [article]

Semiblind hyperspectral unmixing in the presence of spectral library mismatches / Xiao Fu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 9 (September 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 9 (September 2016) . - pp 5171 - 5184 Titre : Semiblind hyperspectral unmixing in the presence of spectral library mismatches Type de document : Article/Communication Auteurs : Xiao Fu, Auteur ; Wing-Kin Ma, Auteur ; José M. Bioucas-Dias, Auteur ; Tsung-Han Chan, Auteur Année de publication : 2016 Article en page(s) : pp 5171 - 5184 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des mélanges spectraux [Termes IGN] données clairsemées [Termes IGN] image hyperspectrale [Termes IGN] itération [Termes IGN] régression Résumé : (Auteur) The dictionary-aided sparse regression (SR) approach has recently emerged as a promising alternative to hyperspectral unmixing in remote sensing. By using an available spectral library as a dictionary, the SR approach identifies the underlying materials in a given hyperspectral image by selecting a small subset of spectral samples in the dictionary to represent the whole image. A drawback with the current SR developments is that an actual spectral signature in the scene is often assumed to have zero mismatch with its corresponding dictionary sample, and such an assumption is considered too ideal in practice. In this paper, we tackle the spectral signature mismatch problem by proposing a dictionary-adjusted nonconvex sparsity-encouraging regression (DANSER) framework. The main idea is to incorporate dictionary-correcting variables in an SR formulation. A simple and low per-iteration complexity algorithm is tailor-designed for practical realization of DANSER. Using the same dictionary-correcting idea, we also propose a robust subspace solution for dictionary pruning. Extensive simulations and real-data experiments show that the proposed method is effective in mitigating the undesirable spectral signature mismatch effects. Numéro de notice : A2016-896 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2557340 En ligne : https://doi.org/10.1109/TGRS.2016.2557340 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83087 [article]