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Interactive visual analytics of moving passenger flocks using massive smart card data / Tong Zhang in Cartography and Geographic Information Science, Vol 49 n° 4 (July 2022)  

Public [article]  inCartography and Geographic Information Science > Vol 49 n° 4 (July 2022) . - pp 354 - 369 Titre : Interactive visual analytics of moving passenger flocks using massive smart card data Type de document : Article/Communication Auteurs : Tong Zhang, Auteur ; Wei He, Auteur ; Jing Huang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 354 - 369 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse spatiale [Termes IGN] analyse visuelle [Termes IGN] carte à puce [Termes IGN] données massives [Termes IGN] mobilité urbaine [Termes IGN] objet mobile [Termes IGN] Shenzhen [Termes IGN] trajet (mobilité) [Vedettes matières IGN] Géovisualisation Résumé : (auteur) Understanding urban mobility patterns is constrained by our limited capabilities to extract and visualize spatio-temporal regularities from large amounts of mobility data. Moving flocks, defined as groups of people traveling along over a pre-defined time duration, can reveal collective moving patterns at aggregated spatio-temporal scales, thereby facilitating the discovery of urban mobility structure and travel demand patterns. In this study, we extend classical trajectory-oriented flock mining algorithms to discover moving flocks of transit passengers, accounting for the constraints of multi-modal transit networks. We develop a map-centered visual analytics approach by integrating the flock mining algorithm with interactive visualization designs of discovered flocks. Novel interactive visualizations are designed and implemented to support the exploration and analyses of discovered moving flocks at different spatial and temporal scales. The visual analytics approach is evaluated using a real-world smart card dataset collected in Shenzhen City, China, validating its applicability in capturing and mapping dynamic mobility patterns over a large metropolitan area. Numéro de notice : A2022-480 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2022.2039775 Date de publication en ligne : 09/03/2022 En ligne : https://doi.org/10.1080/15230406.2022.2039775 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100886 [article]

Modeling human–human interaction with attention-based high-order GCN for trajectory prediction / Yanyan Fang in The Visual Computer, vol 38 n° 7 (July 2022)  

Public [article]  inThe Visual Computer > vol 38 n° 7 (July 2022) . - pp 2257 - 2269 Titre : Modeling human–human interaction with attention-based high-order GCN for trajectory prediction Type de document : Article/Communication Auteurs : Yanyan Fang, Auteur ; Zhiyu Jin, Auteur ; Zhenhua Cui, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2257 - 2269 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] attention (apprentissage automatique) [Termes IGN] détection de cible [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] interaction spatiale [Termes IGN] modèle de simulation [Termes IGN] objet mobile [Termes IGN] piéton [Termes IGN] réseau neuronal de graphes [Termes IGN] trajet (mobilité) Résumé : (auteur) This paper presents a novel high-order graph convolutional network (GCN) for pedestrian trajectory prediction. Specifically, the walking state of a target pedestrian depends on both its historical trajectory, which encodes its speed, walking direction and acceleration information, as well as the movement of its neighbors. Thus we propose to leverage GCNs to aggregate the trajectory features of the target pedestrian and its neighbors to predict the movement of the target pedestrian. Considering that the movement of the neighbors’ neighbors affects the movement of the target pedestrian’s neighbors, thus indirectly affecting the movement of the target pedestrian, we propose to use a high-order GCN for human–human interaction modelling. Such a high-order GCN considers the target pedestrian’s neighbors as well as its neighbors’ neighbors. Further, a pedestrian avoids collision with others by estimating its locations and its neighbors’ upcoming locations, and it slows down or changes direction if it believes a collision may occur, especially in very crowded scenes. In light of this, we propose to model such anticipation-based decision making behavior as attention and combine it with our high-order GCN. Thus we first roughly estimate the future trajectories of all pedestrians with a simple method. By using the coarse predicted future trajectory and GCN outputs, we calculate the attention in our attention-based high-order GCN and predict future trajectory. Extensive experiments validate the effectiveness of our approach. In addition, our model shows a higher data efficiency. On the ETH&UCY dataset, using only 5% of the training data for each training epoch, our model outperforms the state of the art. Numéro de notice : A2022-507 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00371-021-02109-2 Date de publication en ligne : 01/07/2021 En ligne : https://doi.org/10.1007/s00371-021-02109-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101040 [article]

HiPerMovelets: high-performance movelet extraction for trajectory classification / Tarlis Tortelli Portela in International journal of geographical information science IJGIS, vol 36 n° 5 (May 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 5 (May 2022) . - pp 1012 - 1036 Titre : HiPerMovelets: high-performance movelet extraction for trajectory classification Type de document : Article/Communication Auteurs : Tarlis Tortelli Portela, Auteur ; Jonata Tyska Carvalho, Auteur ; Vania Bogorny, Auteur Année de publication : 2022 Article en page(s) : pp 1012 - 1036 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] classification [Termes IGN] exploration de données géographiques [Termes IGN] jeu de données localisées [Termes IGN] trace numérique [Termes IGN] trajet (mobilité) Résumé : (auteur) In the last decade, trajectory classification has received significant attention. The vast amount of data generated on social media, the use of sensor networks, IOT devices and other Internet-enabled sources allowed the semantic enrichment of mobility data, making the classification task more challenging. Existing trajectory classification methods have mainly considered space, time and numerical data, ignoring the semantic dimensions. Only recently proposed methods as Movelets and MASTERMovelets can handle all types of dimensions. MASTERMovelets is the only method that automatically discovers the best dimension combination and subtrajectory size for trajectory classification. However, although it outperformed the state-of-the-art in terms of accuracy, MASTERMovelets is computationally expensive and results in a high dimensionality problem, which makes it unfeasible for most real trajectory datasets that contain a big volume of data. To overcome this problem and enable the application of the movelets approach on large datasets, in this paper we propose a new high-performance method for extracting movelets and classifying trajectories, called HiPerMovelets (High-performance Movelets). Experimental results show that HiPerMovelets is 10 times faster than MASTERMovelets, reduces the high-dimensionality problem, is more scalable, and presents a high classification accuracy in all evaluated datasets with both raw and semantic trajectories. Numéro de notice : A2022-332 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658816.2021.2018593 Date de publication en ligne : 03/01/2022 En ligne : https://doi.org/10.1080/13658816.2021.2018593 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100608 [article]

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Navigation network derivation for QR code-based indoor pedestrian path planning / Jinjin Yan in Transactions in GIS, vol 26 n° 3 (May 2022)  

Public [article]  inTransactions in GIS > vol 26 n° 3 (May 2022) . - pp 1240 - 1255 Titre : Navigation network derivation for QR code-based indoor pedestrian path planning Type de document : Article/Communication Auteurs : Jinjin Yan, Auteur ; Jinwoo Lee, Auteur ; Sisi Zlatanova, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1240 - 1255 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] batiment commercial [Termes IGN] bâtiment public [Termes IGN] navigation pédestre [Termes IGN] noeud [Termes IGN] point d'intérêt [Termes IGN] positionnement en intérieur [Termes IGN] QR code [Termes IGN] scène intérieure [Termes IGN] trajet (mobilité) Résumé : (auteur) With the development of cities, the indoor structures of contemporary public or commercial buildings are becoming increasingly complex. Accordingly, the need for indoor navigation has arisen. Among the indoor positioning technologies, quick response (QR) code, a low-cost, easily deployable, flexible, and efficient approach, has been used for indoor positioning and navigation purposes. A navigation network (model) is a precondition for pedestrian navigation path planning. However, no thorough research has been completed to investigate the relationship between navigation networks and locations of QR codes, which may cause ambiguities when deciding the closest node from the network that should be used for path computation. Specifically, QR codes are generally placed according to preferences or certain specifications whereas current agreed navigation network derivation approaches do not consider that. This article presents a navigation network derivation approach to address the issue by integrating QR code locations as nodes in navigation networks. The present approach is demonstrated in a shopping mall case. The results show that the approach can overcome the above-mentioned issue for indoor pedestrian path planning based on the QR code localization. Numéro de notice : A2022-476 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1111/tgis.12912 Date de publication en ligne : 10/04/2022 En ligne : https://doi.org/10.1111/tgis.12912 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100823 [article]

SNN_flow: a shared nearest-neighbor-based clustering method for inhomogeneous origin-destination flows / Qiliang Liu in International journal of geographical information science IJGIS, vol 36 n° 2 (February 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 2 (February 2022) . - pp 253 - 279 Titre : SNN_flow: a shared nearest-neighbor-based clustering method for inhomogeneous origin-destination flows Type de document : Article/Communication Auteurs : Qiliang Liu, Auteur ; Jie Yang, Auteur ; Min Deng, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 253 - 279 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] classification ascendante hiérarchique [Termes IGN] classification barycentrique [Termes IGN] flux [Termes IGN] méthode de Monte-Carlo [Termes IGN] mobilité urbaine [Termes IGN] noeud [Termes IGN] origine - destination [Termes IGN] Pékin (Chine) [Termes IGN] réseau routier [Termes IGN] taxi [Termes IGN] trajet (mobilité) Résumé : (auteur) Identifying clusters from individual origin–destination (OD) flows is vital for investigating spatial interactions and flow mapping. However, detecting arbitrarily-shaped and non-uniform flow clusters from network-constrained OD flows continues to be a challenge. This study proposes a shared nearest-neighbor-based clustering method (SNN_flow) for inhomogeneous OD flows constrained by a road network. To reveal clusters of varying shapes and densities, a normalized density for each OD flow is defined based on the concept of shared nearest-neighbor, and flow clusters are constructed using the density-connectivity mechanism. To handle large amounts of disaggregated OD flows, an efficient method for searching the network-constrained k-nearest flows is developed based on a local road node distance matrix. The parameters of SNN_flow are statistically determined: the density threshold is modeled as a significance level of a significance test, and the number of nearest neighbors is estimated based on the variance of the kth nearest distance. SNN_flow is compared with three state-of-the-art methods using taxicab trip data in Beijing. The results show that SNN_flow outperforms existing methods in identifying flow clusters with irregular shapes and inhomogeneous distributions. The clusters identified by SNN_flow can reveal human mobility patterns in Beijing. Numéro de notice : A2022-163 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1899184 Date de publication en ligne : 16/03/2021 En ligne : https://doi.org/10.1080/13658816.2021.1899184 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99786 [article]

Création d’un indicateur de qualité de la desserte des transports pour des parcelles à une échelle locale / Nick Lin (2022)

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Pedestrian trajectory prediction with convolutional neural networks / Simone Zamboni in Pattern recognition, vol 121 (January 2022)  

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Mapping trajectories and flows: facilitating a human-centered approach to movement data analytics / Somayeh Dodge in Cartography and Geographic Information Science, vol 48 n° 4 (July 2021)  

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Using information entropy and a multi-layer neural network with trajectory data to identify transportation modes / Qingying Yu in International journal of geographical information science IJGIS, vol 35 n° 7 (July 2021)  

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Map matching for semi-restricted trajectories / Timon Behr (2021)  

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Stability of urban forms: modelling the emergence of collective behaviour in residential trajectories / Arthur Benichou (2021) 

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Context-aware similarity of GPS trajectories / Radu Mariescu-Istodor in Journal of location-based services, vol 14 n° 4 ([01/11/2020])  

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Unfolding spatial-temporal patterns of taxi trip based on an improved network kernel density estimation / Boxi Shen in ISPRS International journal of geo-information, vol 9 n° 11 (November 2020)  

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From small sets of GPS trajectories to detailed movement profiles: quantifying personalized trip-dependent movement diversity / Elham Naghizade in International journal of geographical information science IJGIS, vol 34 n° 10 (October 2020)  

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Prediction of RTK positioning integrity for journey planning / Ahmed El-Mowafy in Journal of applied geodesy, vol 14 n° 4 (October 2020)  

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