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How do voice-assisted digital maps influence human wayfinding in pedestrian navigation? / Yawei Xu in Cartography and Geographic Information Science, vol 49 n° 3 (May 2022)  

Public [article]  inCartography and Geographic Information Science > vol 49 n° 3 (May 2022) . - pp 271 - 287 Titre : How do voice-assisted digital maps influence human wayfinding in pedestrian navigation? Type de document : Article/Communication Auteurs : Yawei Xu, Auteur ; Tong Qin, Auteur ; Yulin Wu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 271 - 287 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie numérique [Termes IGN] acquisition de connaissances [Termes IGN] cognition [Termes IGN] comportement [Termes IGN] itinéraire piétionnier [Termes IGN] navigation pédestre [Termes IGN] oculométrie [Termes IGN] orientation [Termes IGN] Pékin (Chine) [Termes IGN] questionnaire Résumé : (auteur) Voice-assisted digital maps have become mainstream navigation aids for pedestrian navigation. Although these maps are widely studied and applied, it is still unclear how they affect human behavior and spatial knowledge acquisition. In this study, we recruited thirty-three college students to carry out an outdoor wayfinding experiment. We compared the effects of voice-assisted digital maps with those of digital maps without voice instructions and paper maps by using eye tracking, sketch maps, questionnaires and interviews. The results show that, compared to the other map types, voice-assisted digital maps can help users reach their destinations more quickly and pay more attention to moving objects, thereby increasing the comfort levels of participants. However, the efficiency of voice-assisted maps on route memory tasks does not rival that of paper maps. Overall, the use of voice-assisted digital maps saves time but may reduce pedestrians’ spatial knowledge acquisition. The results of this study reveal the influence of voice on pedestrian wayfinding and deepen the scientific understanding of the multimedia navigation mode in shaping human spatial ability. Numéro de notice : A2022-295 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2021.2017798 Date de publication en ligne : 13/01/2022 En ligne : https://doi.org/10.1080/15230406.2021.2017798 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100347 [article]

Graph neural network based model for multi-behavior session-based recommendation / Bo Yu in Geoinformatica [en ligne], vol 26 n° 2 (April 2022)  

Public [article]  inGeoinformatica [en ligne] > vol 26 n° 2 (April 2022) . - pp 429 - 447 Titre : Graph neural network based model for multi-behavior session-based recommendation Type de document : Article/Communication Auteurs : Bo Yu, Auteur ; Ruoqian Zhang, Auteur ; Wei Chen, Auteur ; Junhua Fang, Auteur Année de publication : 2022 Article en page(s) : pp 429 - 447 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] attention (apprentissage automatique) [Termes IGN] comportement [Termes IGN] consommation [Termes IGN] modèle de simulation [Termes IGN] réseau neuronal de graphes [Termes IGN] réseau sémantique [Termes IGN] service fondé sur la position Mots-clés libres : session Résumé : (auteur) Multi-behavior session-based recommendation aims to predict the next item, such as a location-based service (LBS) or a product, to be interacted by a specific behavior type (e.g., buy or click) in a session involving multiple types of behaviors. State-of-the-art methods generally model multi-behavior dependencies in item-level, but ignore the potential of discovering useful patterns of multi-behavior transition through feature-level representation learning. Besides, sequential and non-sequential patterns should be properly fused in session modeling to capture dynamic interests within the session. To this end, this paper proposes a Graph Neural Network based Hybrid Model GNNH, which enables feature-level deeper representations of multi-behavior interaction sequences for session-based recommendation. Specifically, we first construct multi-relational item graph (MRIG) and feature graph (MRFG) based on session sequences. On top of the MRIG and MRFG, our model takes advantage of GNN to capture item and feature representations, such that global item-to-item and feature-to-feature relations are fully preserved. Afterwards, each multi-behavior session is modeled by a seamless fusion of interacted item and feature representations, where self-attention and mean-pooling are used to obtain sequential and non-sequential patterns simultaneously. Experiments on two real datasets show that the GNNH model significantly outperforms the state-of-the-art methods. Numéro de notice : A2022-326 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1007/s10707-021-00439-w Date de publication en ligne : 29/05/2021 En ligne : https://doi.org/10.1007/s10707-021-00439-w Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100489 [article]

Assessing COVID-induced changes in spatiotemporal structure of mobility in the United States in 2020: a multi-source analytical framework / Evgeny Noi in International journal of geographical information science IJGIS, vol 36 n° 3 (March 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 3 (March 2022) . - pp 585 - 616 Titre : Assessing COVID-induced changes in spatiotemporal structure of mobility in the United States in 2020: a multi-source analytical framework Type de document : Article/Communication Auteurs : Evgeny Noi, Auteur ; Alexander Rudolph, Auteur ; Somayeh Dodge, Auteur Année de publication : 2022 Article en page(s) : pp 585 - 616 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse spatio-temporelle [Termes IGN] autocorrélation spatiale [Termes IGN] comportement [Termes IGN] données multisources [Termes IGN] données spatiotemporelles [Termes IGN] épidémie [Termes IGN] Etats-Unis [Termes IGN] hétérogénéité spatiale [Termes IGN] maladie virale [Termes IGN] mobilité [Termes IGN] mobilité territoriale Résumé : (auteur) The COVID-19 pandemic resulted in profound changes in mobility patterns and altered travel behaviors locally and globally. As a result, movement metrics have widely been used by researchers and policy makers as indicators to study, model, and mitigate the impacts of the COVID-19 pandemic. However, the veracity and variability of these mobility metrics have not been studied. This paper provides a systematic review of mobility and social distancing metrics available to researchers during the pandemic in 2020 in the United States. Twenty-six indices across nine different sources are analyzed and assessed with respect to their spatial and temporal coverage as well as sample representativeness at the county-level. Finally global and local indicators of spatial association are computed to explore spatial and temporal heterogeneity in mobility patterns. The structure of underlying changes in mobility and social distancing is examined in different US counties and across different data sets. We argue that a single measure might not describe all aspects of mobility perfectly. Numéro de notice : A2022-207 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.2005796 Date de publication en ligne : 21/12/2021 En ligne : https://doi.org/10.1080/13658816.2021.2005796 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100023 [article]

Changing mobility patterns in the Netherlands during COVID-19 outbreak / Sander Van Der Drift in Journal of location-based services, vol 16 n° 1 (March 2022)  

Public [article]  inJournal of location-based services > vol 16 n° 1 (March 2022) . - pp 1 - 24 Titre : Changing mobility patterns in the Netherlands during COVID-19 outbreak Type de document : Article/Communication Auteurs : Sander Van Der Drift, Auteur ; Luc Wismans, Auteur ; Marie-José Olde-Kalter, Auteur Année de publication : 2022 Article en page(s) : pp 1 - 24 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] bicyclette [Termes IGN] comportement [Termes IGN] épidémie [Termes IGN] estimation bayesienne [Termes IGN] mobilité territoriale [Termes IGN] Pays-Bas [Termes IGN] téléphone intelligent [Termes IGN] transport [Termes IGN] transport public [Termes IGN] travail à domicile [Termes IGN] véhicule automobile Résumé : (auteur) The COVID-19 outbreak and associated measures taken had an enormous impact on society as well as a disruptive, but not necessarily negative, impact on mobility. The Ministry of Infrastructure and Water Management received the most recent insights from the Dutch Mobility Panel (DMP) on a weekly basis. These insights were used to monitor the travel behaviour and to analyse changes in the behaviour of different groups and usage of modes of transport during COVID-19. The analysis shows an enormous decrease in travel at the beginning of the implementation of the so-called ‘intelligent’ lockdown and gradual increase again towards comparable levels as before this ‘intelligent lockdown, although the distribution over time, motives and used modes has changed. It becomes clear that not everyone needs to travel during peak hours and commuter travel is also not the main reason for the increase in car usage. Furthermore, cycling has shown to be an alternative option for travellers and public transport is hardly used anymore. If it is possible to sustain the lower level of car usage and integrate public transport as an important alternative for travel again, the COVID-19 impact on mobility could have a substantial remaining positive impact on mobility. Numéro de notice : A2022-391 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/17489725.2021.1876259 Date de publication en ligne : 11/03/2021 En ligne : https://doi.org/10.1080/17489725.2021.1876259 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100682 [article]

Raw GIS to 3D road modeling for real-time traffic simulation / Yacine Amara in The Visual Computer, vol 38 n° 1 (January 2022)  

Public [article]  inThe Visual Computer > vol 38 n° 1 (January 2022) . - pp 239 - 256 Titre : Raw GIS to 3D road modeling for real-time traffic simulation Type de document : Article/Communication Auteurs : Yacine Amara, Auteur ; Abdenour Amamra, Auteur ; Salim Khemis, Auteur Année de publication : 2022 Article en page(s) : pp 239 - 256 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] comportement [Termes IGN] graphe topologique [Termes IGN] intersection spatiale [Termes IGN] modèle de simulation [Termes IGN] modélisation 3D [Termes IGN] navigation virtuelle [Termes IGN] planification urbaine [Termes IGN] système d'information géographique [Termes IGN] système multi-agents [Termes IGN] temps réel [Termes IGN] trafic routier [Termes IGN] trajectoire (véhicule non spatial) Résumé : (auteur) In this work, we propose a new approach to road modeling and 3D traffic simulation. Based on the raw geographic information system (GIS) data laid out as sparse polylines with attributes, we compute a more adequate functional description for real-time simulation of on-road vehicle animation. The proposed approach begins with a filtering/subdivision module where the raw polylines are transformed into a graph of functional road segments as arcs and the nodes as intersections. Then, the vehicle speed profile is computed based on its dynamics, its neighborhood and the curvature profile of the road. Afterward, a multi-agent system is proposed in order to handle a large number of simulated vehicle/driver couples. Finally, we deploy a 3D rendering engine to display the computed 3D simulation on screen. The resulting model satisfies most of the real road features for traffic simulation including road interchanges, roundabouts, intersections, lanes, etc. More importantly, the simulated driving qualitatively mimics the real behavior of the drivers/vehicles on the road as can be seen in the accompanying video (RTSP video). We also validate our findings with a technical assessment based on macroscopic and microscopic traffic simulation metrics in several road traffic scenarios. Numéro de notice : A2022-160 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s00371-020-02013-1 Date de publication en ligne : 01/01/2022 En ligne : https://doi.org/10.1007/s00371-020-02013-1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99777 [article]

Contextual location recommendation for location-based social networks by learning user intentions and contextual triggers / Seyyed Mohammadreza Rahimi in Geoinformatica [en ligne], vol 26 n° 1 (January 2022)  

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Identifying map users with eye movement data from map-based spatial tasks: user privacy concerns / Hua Liao in Cartography and Geographic Information Science, vol 49 n° 1 (January 2022)  

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Modelling spatial processes in quantitative human geography / A. Stewart Fotheringham in Annals of GIS, vol 28 n° 1 (January 2022)  

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Simulation of dispersion effects by considering interactions of pedestrians and bicyclists using an agent space model / Mingwei Liu in Computers, Environment and Urban Systems, vol 91 (January 2022)  

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Modeling transit-assisted hurricane evacuation through socio-spatial networks / Yan Yang in International journal of geographical information science IJGIS, vol 35 n° 12 (December 2021)  

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Evaluating the effectiveness of different cartographic design variants for influencing route choice / Stefan Fuest in Cartography and Geographic Information Science, vol 48 n° 2 (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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Emotional habitat: mapping the global geographic distribution of human emotion with physical environmental factors using a species distribution model / Yizhuo Li in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)  

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Caractérisation de l’occupation spatiale des étagnes au printemps dans le Champsaur (Parc National des Écrins) : approche géomatique et biogéographique / Lucie Doudoux (2021)

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Chinese tourists in Nordic countries: An analysis of spatio-temporal behavior using geo-located travel blog data / Yunhao Zheng in Computers, Environment and Urban Systems, vol 85 (January 2021)  

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