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A methodology with a distributed algorithm for large-scale trajectory distribution prediction / QiuLei Guo in International journal of geographical information science IJGIS, Vol 33 n° 3-4 (March - April 2019)  

Public [article]  inInternational journal of geographical information science IJGIS > Vol 33 n° 3-4 (March - April 2019) . - pp 833 - 854 Titre : A methodology with a distributed algorithm for large-scale trajectory distribution prediction Type de document : Article/Communication Auteurs : QiuLei Guo, Auteur ; Hassan A. Karimi, Auteur Année de publication : 2019 Article en page(s) : pp 833 - 854 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] distribution spatiale [Termes IGN] données massives [Termes IGN] données spatiotemporelles [Termes IGN] gestion de trafic [Termes IGN] migration pendulaire [Termes IGN] mobilité urbaine [Termes IGN] New York (Etats-Unis ; ville) [Termes IGN] Pékin (Chine) [Termes IGN] population urbaine [Termes IGN] prédiction [Termes IGN] trafic urbain [Termes IGN] trajet (mobilité) Résumé : (Auteur) In this paper, we propose a method for predicting the distributions of people’s trajectories on the road network throughout a city. Specifically, we predict the number of people who will move from one area to another, their probable trajectories, and the corresponding likelihoods of those trajectories in the near future, such as within an hour. With this prediction, we will identify the hot road segments where potential traffic jams might occur and reveal the formation of those traffic jams. Accurate predictions of human trajectories at a city level in real time is challenging due to the uncertainty of people’s spatial and temporal mobility patterns, the complexity of a city level’s road network, and the scale of the data. To address these challenges, this paper proposes a method which includes several major components: (1) a model for predicting movements between neighboring areas, which combines both latent and explicit features that may influence the movements; (2) different methods to estimate corresponding flow trajectory distributions in the road network; (3) a MapReduce-based distributed algorithm to simulate large-scale trajectory distributions under real-time constraints. We conducted two case studies with taxi data collected from Beijing and New York City and systematically evaluated our method. Numéro de notice : A2019-218 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1536981 Date de publication en ligne : 31/10/2018 En ligne : https://doi.org/10.1080/13658816.2018.1536981 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92690 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2019031	RAB	Revue	Centre de documentation	En réserve L003	Disponible
079-2019032	RAB	Revue	Centre de documentation	En réserve L003	Disponible

A Topology-inferred graph-based heuristic algorithm for map simplification / QiuLei Guo in Transactions in GIS, vol 20 n° 5 (October 2016)  

Public [article]  inTransactions in GIS > vol 20 n° 5 (October 2016) . - pp 775 – 789 Titre : A Topology-inferred graph-based heuristic algorithm for map simplification Type de document : Article/Communication Auteurs : QiuLei Guo, Auteur ; Hassan A. Karimi, Auteur Année de publication : 2016 Article en page(s) : pp 775 – 789 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] algorithme de généralisation [Termes IGN] carte heuristique [Termes IGN] graphe [Termes IGN] méthode heuristique [Termes IGN] polyligne [Termes IGN] relation topologique [Termes IGN] temps réel [Termes IGN] voisinage (relation topologique) [Vedettes matières IGN] Généralisation Résumé : (auteur) In this article, we present a heuristic map simplification algorithm based on a novel topology-inferred graph model. Compared with the existing algorithms, which only focus either on geometry simplification or on topological consistency, our algorithm simplifies the map composed of series of polylines and constraint points while maintaining the topological relationships in the map, maximizing the number of removal points, and minimizing error distance efficiently. Unlike some traditional geometry simplification algorithms, such as Douglas and Peucker's, which add points incrementally, we remove points sequentially based on a priority determined by heuristic functions. In the first stage, we build a graph to model the topology of points in the map from which we determine whether a point is removable or not. As map generalization is needed in different applications with different requirements, we present two heuristic functions to determine the priority of points removal for two different purposes: to save storage space and to reduce computation time. The time complexity of our algorithm is math formula which is efficient enough to be considered for real-time applications. Experiments on real maps were conducted and the results indicate that our algorithm produces high quality results; one heuristic function results in higher removal points saving storage space and the other improves the time performance significantly. Numéro de notice : A2016-999 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12188 En ligne : http://dx.doi.org/10.1111/tgis.12188 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83779 [article]