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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]

Assessing the cognition of movement trajectory visualizations: interpreting speed and direction / Crystal J. Bae in Cartography and Geographic Information Science, Vol 50 n° 2 (March 2023)  

Public [article]  inCartography and Geographic Information Science > Vol 50 n° 2 (March 2023) . - pp 143 - 161 Titre : Assessing the cognition of movement trajectory visualizations: interpreting speed and direction Type de document : Article/Communication Auteurs : Crystal J. Bae, Auteur ; Somayeh Dodge, Auteur Année de publication : 2023 Article en page(s) : pp 143 - 161 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse visuelle [Termes IGN] cognition [Termes IGN] compréhension de l'image [Termes IGN] données spatiotemporelles [Termes IGN] objet mobile [Termes IGN] visualisation cartographique [Vedettes matières IGN] Géovisualisation Résumé : (auteur) This paper evaluates cognitively plausible geovisualization techniques for mapping movement data. With the widespread increase in the availability and quality of space-time data capturing movement trajectories of individuals, meaningful representations are needed to properly visualize and communicate trajectory data and complex movement patterns using geographic displays. Many visualization and visual analytics approaches have been proposed to map movement trajectories (e.g. space-time paths, animations, trajectory lines, etc.). However, little is known about how effective these complex visualizations are in capturing important aspects of movement data. Given the complexity of movement data which involves space, time, and context dimensions, it is essential to evaluate the communicative efficiency and efficacy of various visualization forms in helping people understand movement data. This study assesses the effectiveness of static and dynamic movement displays as well as visual variables in communicating movement parameters along trajectories, such as speed and direction. To do so, a web-based survey is conducted to evaluate the understanding of movement visualizations by a nonspecialist audience. This and future studies contribute fundamental insights into the cognition of movement visualizations and inspire new methods for the empirical evaluation of geovisualizations. Numéro de notice : A2023-221 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2022.2157879 Date de publication en ligne : 23/01/2023 En ligne : https://doi.org/10.1080/15230406.2022.2157879 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103167 [article]

An improved optimization model for crowd evacuation considering individual exit choice preference / Fei Gao in Transactions in GIS, vol 26 n° 7 (November 2022)  

Public [article]  inTransactions in GIS > vol 26 n° 7 (November 2022) . - pp 2850 - 2873 Titre : An improved optimization model for crowd evacuation considering individual exit choice preference Type de document : Article/Communication Auteurs : Fei Gao, Auteur ; Zhiqiang Du, Auteur ; Martin Werner, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2850 - 2873 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] comportement [Termes IGN] événement [Termes IGN] gestion de crise [Termes IGN] optimisation (mathématiques) [Termes IGN] optimisation par essaim de particules [Termes IGN] planification [Termes IGN] secours d'urgence Résumé : (auteur) Guidance-assisted crowd evacuation is a process of combining individual exit choice behavior with managers'exit assignment control. The knowledge of individual exit choice preference is of great significance for optimizing global exit assignment planning. This study proposes an improved optimization model for crowd evacuation by integrating the individual-level exit choice preference analysis with system-level exit assignment optimization to represent more realistic crowd evacuation decisions. First, the impact factors of individual exit choice behavior are considered in a mixed logit model to predict the probability of each individual choosing each exit in specific situations. Second, a preference-based exit filtering strategy is designed to analyze the sensible alternative exits for individuals or groups in multi-scale evacuation cells. Finally, to pursue optimal exit assignment planning, a multi-objective particle swarm optimization algorithm and an improved social force model are adopted to simulate the process of crowd evacuation and evaluate the performance of the specific exit assignment plans. The case study of an outdoor multiple-exit scenario in Xi'an, China, indicates that the proposed model can help managers to understand the heterogeneity of individual evacuation behaviors. Furthermore, it will support more reliable and realistic evacuation decisions in real-life situations than conventional plans that typically implement the top-n strategy. Numéro de notice : A2022-833 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1111/tgis.12984 Date de publication en ligne : 04/09/2022 En ligne : https://doi.org/10.1111/tgis.12984 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102216 [article]

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]

Detecting spatiotemporal traffic events using geosocial media data / Shishuo Xu in Computers, Environment and Urban Systems, vol 94 (June 2022)  

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The point-descriptor-precedence representation for point configurations and movements / Amna Qayyum in International journal of geographical information science IJGIS, vol 35 n° 7 (July 2021)  

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Trajectory and image-based detection and identification of UAV / Yicheng Liu in The Visual Computer, vol 37 n° 7 (July 2021)  

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Understanding collective human movement dynamics during large-scale events using big geosocial data analytics / Junchuan Fan in Computers, Environment and Urban Systems, vol 87 (May 2021)  

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Dynamic human body reconstruction and motion tracking with low-cost depth cameras / Kangkan Wang in The Visual Computer, vol 37 n° 3 (March 2021)  

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Lightweight convolutional neural network-based pedestrian detection and re-identification in multiple scenarios / Xiao Ke in Machine Vision and Applications, vol 32 n° 2 (March 2021)  

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Passive radar imaging of ship targets with GNSS signals of opportunity / Debora Pastina in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 3 (March 2021)  

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Pyramidal framework: guidance for the next generation of GIS spatial-temporal models / Cyril Carré in ISPRS International journal of geo-information, vol 10 n° 3 (March 2021)  

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Activity recognition in residential spaces with Internet of things devices and thermal imaging / Kshirasagar Naik in Sensors, vol 21 n° 3 (February 2021)  

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Unsupervised deep representation learning for real-time tracking / Ning Wang in International journal of computer vision, vol 129 n° 2 (February 2021)  

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