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Detecting spatiotemporal propagation patterns of traffic congestion from fine-grained vehicle trajectory data / Haoyi Xiong in International journal of geographical information science IJGIS, vol 37 n° 5 (May 2023)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 37 n° 5 (May 2023) . - pp 1157-1179 Titre : Detecting spatiotemporal propagation patterns of traffic congestion from fine-grained vehicle trajectory data Type de document : Article/Communication Auteurs : Haoyi Xiong, Auteur ; Xun Zhou, Auteur ; David A. Bennett, Auteur Année de publication : 2023 Article en page(s) : pp 1157-1179 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] déformation temporelle dynamique (algorithme) [Termes IGN] détection d'anomalie [Termes IGN] données spatiotemporelles [Termes IGN] événement [Termes IGN] flux [Termes IGN] gestion de trafic [Termes IGN] réseau routier [Termes IGN] trafic routier Résumé : (auteur) Traffic congestion on a road segment typically begins as a small-scale spatiotemporal event that can then propagate throughout a road network and produce large-scale disruptions to a transportation system. In current techniques for the analysis of network flow, data is often aggregated to relatively large (e.g. 5 min) discrete time steps that obscure the small-scale spatiotemporal interactions that drive larger-scale dynamics. We propose a new method that handles fine-grained data to better capture those dynamics. Propagation patterns of traffic congestion are represented as spatiotemporally connected events. Each event is captured as a time series at the temporal resolution of the available trajectory data and at the spatial resolution of the network edge. The spatiotemporal propagation patterns of traffic congestion are captured using Dynamic Time Warping and represented as a set of directed acyclic graphs of spatiotemporal events. Results from this method are compared to an existing method using fine-grained data derived from an agent-based model of traffic simulation. Our method outperforms the existing method. Our method also successfully detects congestion propagation patterns that were reported by media news using sparse real-world data derived from taxis. Numéro de notice : A2023-225 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2023.2178653 Date de publication en ligne : 22/02/2023 En ligne : https://doi.org/10.1080/13658816.2023.2178653 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103177 [article]

Generic and efficient framework for search trees on flash memory storage systems / Mohamed Sarwat in Geoinformatica, vol 17 n° 3 (July 2013)  

Public [article]  inGeoinformatica > vol 17 n° 3 (July 2013) . - pp 489 - 519 Titre : Generic and efficient framework for search trees on flash memory storage systems Type de document : Article/Communication Auteurs : Mohamed Sarwat, Auteur ; Mohamed F. Mokbel, Auteur ; Xun Zhou, Auteur ; Suman Nath, Auteur Année de publication : 2013 Article en page(s) : pp 489 - 519 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Informatique [Termes IGN] arbre (mathématique) [Termes IGN] arbre-R [Termes IGN] indexation spatiale [Termes IGN] mémoire d'ordinateur [Termes IGN] mémoire flash Résumé : (Auteur) Tree index structures are crucial components in data management systems. Existing tree index structure are designed with the implicit assumption that the underlying external memory storage is the conventional magnetic hard disk drives. This assumption is going to be invalid soon, as flash memory storage is increasingly adopted as the main storage media in mobile devices, digital cameras, embedded sensors, and notebooks. Though it is direct and simple to port existing tree index structures on the flash memory storage, that direct approach does not consider the unique characteristics of flash memory, i.e., slow write operations, and erase-before-update property, which would result in a sub optimal performance. In this paper, we introduce FAST (i.e., Flash-Aware Search Trees) as a generic framework for flash-aware tree index structures. FAST distinguishes itself from all previous attempts of flash memory indexing in two aspects: (1) FAST is a generic framework that can be applied to a wide class of data partitioning tree structures including R-tree and its variants, and (2) FAST achieves both efficiency and durability of read and write flash operations through memory flushing and crash recovery techniques. Extensive experimental results, based on an actual implementation of FAST inside the GiST index structure in PostgreSQL, show that FAST achieves better performance than its competitors. Numéro de notice : A2013-381 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Article DOI : 10.1007/s10707-012-0164-9 Date de publication en ligne : 30/08/2012 En ligne : https://doi.org/10.1007/s10707-012-0164-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32519 [article]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
057-2013031	RAB	Revue	Centre de documentation	En réserve L003	Disponible