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Pedestrian network generation based on crowdsourced tracking data / Xue Yang in International journal of geographical information science IJGIS, vol 34 n° 5 (May 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 5 (May 2020) . - pp 1051 - 1074 Titre : Pedestrian network generation based on crowdsourced tracking data Type de document : Article/Communication Auteurs : Xue Yang, Auteur ; Luliang Tang, Auteur ; Chang Ren, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1051 - 1074 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes descripteurs IGN] approche participative [Termes descripteurs IGN] base de données multi-représentation [Termes descripteurs IGN] correction géométrique [Termes descripteurs IGN] correction topographique [Termes descripteurs IGN] dimension fractale [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] estimation par noyau [Termes descripteurs IGN] mobilité urbaine [Termes descripteurs IGN] navigation pédestre [Termes descripteurs IGN] regroupement de points Résumé : (auteur) Pedestrian networks play an important role in various applications, such as pedestrian navigation services and mobility modeling. This paper presents a novel method to extract pedestrian networks from crowdsourced tracking data based on a two-layer framework. This framework includes a walking pattern classification layer and a pedestrian network generation layer. In the first layer, we propose a multi-scale fractal dimension (MFD) algorithm in order to recognize the two different types of walking patterns: walking with a clear destination (WCD) or walking without a clear destination (WOCD). In the second layer, we generate the pedestrian network by combining the pedestrian regions and pedestrian paths. The pedestrian regions are extracted based on a modified connected component analysis (CCA) algorithm from the WOCD traces. We generate the pedestrian paths using a kernel density estimation (KDE)-based point clustering algorithm from the WCD traces. The pedestrian network generation results using two actual crowdsourced datasets show that the proposed method has good performance in both geometrical correctness and topological correctness. Numéro de notice : A2020-207 Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1702197 date de publication en ligne : 09/12/2019 En ligne : https://doi.org/10.1080/13658816.2019.1702197 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94888 [article]

Travel time estimation at intersections based on low-frequency spatial-temporal GPS trajectory big data / Luliang Tang in Cartography and Geographic Information Science, vol 43 n° 5 (November 2016)  

Public [article]  inCartography and Geographic Information Science > vol 43 n° 5 (November 2016) . - pp 417 - 426 Titre : Travel time estimation at intersections based on low-frequency spatial-temporal GPS trajectory big data Type de document : Article/Communication Auteurs : Luliang Tang, Auteur ; Zihan Kan, Auteur ; Xia Zhang, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 417 - 426 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] carrefour [Termes descripteurs IGN] coordonnées GPS [Termes descripteurs IGN] dimension temporelle [Termes descripteurs IGN] données massives [Termes descripteurs IGN] fréquence [Termes descripteurs IGN] itinéraire [Termes descripteurs IGN] logique floue [Termes descripteurs IGN] taxi [Termes descripteurs IGN] temps de trajet [Termes descripteurs IGN] trafic routier [Termes descripteurs IGN] Wuhan (Chine) Résumé : (Auteur) Intersections are the critical parts where different traffic flows converge and change directions, forming “bottlenecks” and “clog points” in urban traffic. Intersection travel time is an important parameter for public route planning, traffic management, and engineering optimization. Based on low-frequency spatial-temporal Global Positioning System (GPS) trace data, this article presents a novel method for estimating intersection travel time. The proposed method first analyzes the different travel patterns of vehicles through an intersection, then determines the range of an intersection dynamically and reasonably, and obtains traffic flow speed and delay at the intersection under different travel patterns using a fuzzy fitting approach. Finally, the average intersection travel time is estimated from traffic flow speed and delay and intersection range in different travel patterns. Wuhan road network data and GPS trace data from taxicabs were tested in the experiments and the results show that the proposed method can improve the accuracy of travel time estimation at city intersections. Numéro de notice : A2016-692 Thématique : GEOMATIQUE/POSITIONNEMENT Nature : Article DOI : 10.1080/15230406.2015.1130649 En ligne : https://doi.org/10.1080/15230406.2015.1130649 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82029 [article]

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