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Predicting the variability in pedestrian travel rates and times using crowdsourced GPS data / Michael J. Campbell in Computers, Environment and Urban Systems, vol 97 (October 2022)  

Public [article]  inComputers, Environment and Urban Systems > vol 97 (October 2022) . - n° 101866 Titre : Predicting the variability in pedestrian travel rates and times using crowdsourced GPS data Type de document : Article/Communication Auteurs : Michael J. Campbell, Auteur ; Philip E. Dennison, Auteur ; Matthew Thompson, Auteur Année de publication : 2022 Article en page(s) : n° 101866 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] base de données localisées [Termes IGN] Californie (Etats-Unis) [Termes IGN] chemin le moins coûteux, algorithme du [Termes IGN] données issues des réseaux sociaux [Termes IGN] durée de trajet [Termes IGN] mobilité urbaine [Termes IGN] navigation pédestre [Termes IGN] pente [Termes IGN] planification urbaine [Termes IGN] trace GPS [Termes IGN] Utah (Etas-Unis) Résumé : (auteur) Accurately predicting pedestrian travel times is critically valuable in emergency response, wildland firefighting, disaster management, law enforcement, and urban planning. However, the relationship between pedestrian movement and landscape conditions is highly variable between individuals, making it difficult to estimate how long it will take broad populations to get from one location to another on foot. Although functions exist for predicting travel rates, they typically oversimplify the inherent variability of pedestrian travel by assuming the effects of landscapes on movement are universal. In this study, we present an approach for predicting the variability in pedestrian travel rates and times using a large, crowdsourced database of GPS tracks. Acquired from the outdoor recreation website AllTrails, these tracks represent nearly 2000 hikes on a diverse range of trails in Utah and California, USA. We model travel rates as a function of the slope of the terrain by generating a series of non-linear percentile models from the 2.5 th to the 97.5 th by 2.5 percentiles. The 50 th percentile model, representing the hiking speed of the typical individual, demonstrates marked improvement over existing slope-travel rate functions when compared to an independent test dataset. Our results demonstrate novel capacity to estimate travel time variability, with modeled percentiles being able to predict actual percentiles with less than 10% error. Travel rate functions can also be applied to least cost path analysis to provide variability in travel times. Numéro de notice : A2022-599 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.compenvurbsys.2022.101866 Date de publication en ligne : 20/08/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101866 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101452 [article]

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]

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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]

Detecting individuals' spatial familiarity with urban environments using eye movement data / Hua Liao in Computers, Environment and Urban Systems, vol 93 (April 2022)  

Public [article]  inComputers, Environment and Urban Systems > vol 93 (April 2022) . - n° 101758 Titre : Detecting individuals' spatial familiarity with urban environments using eye movement data Type de document : Article/Communication Auteurs : Hua Liao, Auteur ; Wendi Zhao, Auteur ; Changbo Zhang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101758 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse visuelle [Termes IGN] apprentissage automatique [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] navigation pédestre [Termes IGN] oculométrie [Termes IGN] service fondé sur la position [Termes IGN] zone urbaine [Vedettes matières IGN] Géovisualisation Résumé : (auteur) The spatial familiarity of environments is an important high-level user context for location-based services (LBS). Knowing users' familiarity level of environments is helpful for enabling context-aware LBS that can automatically adapt information services according to users' familiarity with the environment. Unlike state-of-the-art studies that used questionnaires, sketch maps, mobile phone positioning (GPS) data, and social media data to measure spatial familiarity, this study explored the potential of a new type of sensory data - eye movement data - to infer users' spatial familiarity of environments using a machine learning approach. We collected 38 participants' eye movement data when they were performing map-based navigation tasks in familiar and unfamiliar urban environments. We trained and cross-validated a random forest classifier to infer whether the users were familiar or unfamiliar with the environments (i.e., binary classification). By combining basic statistical features and fixation semantic features, we achieved a best accuracy of 81% in a 10-fold classification and 70% in the leave-one-task-out (LOTO) classification. We found that the pupil diameter, fixation dispersion, saccade duration, fixation count and duration on the map were the most important features for detecting users' spatial familiarity. Our results indicate that detecting users' spatial familiarity from eye tracking data is feasible in map-based navigation and only a few seconds (e.g., 5 s) of eye movement data is sufficient for such detection. These results could be used to develop context-aware LBS that adapt their services to users' familiarity with the environments. Numéro de notice : A2022-121 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101758 Date de publication en ligne : 21/01/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101758 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99663 [article]

Mining crowdsourced trajectory and geo-tagged data for spatial-semantic road map construction / Jincai Huang in Transactions in GIS, vol 26 n° 2 (April 2022)  

Public [article]  inTransactions in GIS > vol 26 n° 2 (April 2022) . - pp 735 - 754 Titre : Mining crowdsourced trajectory and geo-tagged data for spatial-semantic road map construction Type de document : Article/Communication Auteurs : Jincai Huang, Auteur ; Yunfei Zhang, Auteur ; Min Deng, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 735 - 754 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Vedettes matières IGN] Géomatique [Termes IGN] base de données routières [Termes IGN] carrefour [Termes IGN] carte routière [Termes IGN] contenu généré par les utilisateurs [Termes IGN] données localisées des bénévoles [Termes IGN] données routières [Termes IGN] extraction de traits caractéristiques [Termes IGN] information sémantique [Termes IGN] intégration de données [Termes IGN] navigation automobile [Termes IGN] vitesse [Termes IGN] Wuhan (Chine) Résumé : (auteur) The road map is a fundamental part of a spatial data infrastructure (SDI), and is widely applied in navigation, smart transportation, and mobile location services. Recently, with the ubiquity of positioning devices, crowdsourced trajectories have become a significant data resource for road map construction and updating. However, existing trajectory-based methods mainly place emphasis on extracting road geometry features and may ignore continuous updating of road semantic information. Hence, we propose a divide-and-conquer method to construct a spatial-semantic road map by incorporating multiple data sources (e.g., crowdsourced trajectories and geo-tagged data). The proposed method divides road map construction into two sub-tasks, road structure reconstruction and road attributes inference. The road structure reconstruction process starts to partition raw trajectory data into different cliques of roadways and road intersections, and then extracts various targeted road structures by analyzing the turning modes in different trajectory cliques. The road attributes inference process aims to infer three pieces of crucial semantic information about road speeds, turning rules, and road names from crowdsourced trajectories and geo-tagged data. The case studies in Wuhan were examined to illustrate that the proposed method can construct a routable road map with enhanced geometric structures and rich semantic information, providing a beneficial data solution for car navigation and SDI update. Numéro de notice : A2022-364 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1111/tgis.12879 Date de publication en ligne : 17/12/2021 En ligne : https://doi.org/10.1111/tgis.12879 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100583 [article]

Mapping global flying aircraft activities using Landsat 8 and cloud computing / Fen Zhao in ISPRS Journal of photogrammetry and remote sensing, vol 184 (February 2022)  

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ATLANTIS : Une ontologie pour représenter les Instructions nautiques / Helen Mair Rawsthorne (2022)    

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Attributing pedestrian networks with semantic information based on multi-source spatial data / Xue Yang in International journal of geographical information science IJGIS, vol 36 n° 1 (January 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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Predicting AIS reception using tropospheric propagation forecast and machine learning / Zackary Vanche (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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Relevés d’obstacles à la navigation aérienne au service de l’information aéronautique / Olivier de Joinville in XYZ, n° 169 (décembre 2021)

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Impact of travel time uncertainties on modeling of spatial accessibility: a comparison of street data sources / Yan Lin in Cartography and Geographic Information Science, vol 48 n° 6 (October 2021)  

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Pedestrian fowl prediction in open public places using graph convolutional network / Menghang Liu in ISPRS International journal of geo-information, vol 10 n° 7 (July 2021)  

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Multi-GNSS PPP/INS tightly coupled integration with atmospheric augmentation and its application in urban vehicle navigation / Shengfeng Gu in Journal of geodesy, vol 95 n° 6 (June 2021)  

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