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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]

Exemplaires(1)

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032-2022031	RAB	Revue	Centre de documentation	En réserve L003	Disponible

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)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 1 (January 2022) . - pp 31 - 54 Titre : Attributing pedestrian networks with semantic information based on multi-source spatial data Type de document : Article/Communication Auteurs : Xue Yang, Auteur ; Kathleen Stewart, Auteur ; Mengyuan Fang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 31 - 54 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] données localisées des bénévoles [Termes IGN] données multisources [Termes IGN] extraction de données [Termes IGN] itinéraire piétionnier [Termes IGN] navigation pédestre [Termes IGN] ondelette [Termes IGN] réseau routier [Termes IGN] segmentation sémantique [Termes IGN] utilisation du sol [Termes IGN] Wuhan (Chine) Résumé : (auteur) The lack of associating pedestrian networks, i.e. the paths and roads used for non-vehicular travel, with information about semantic attribution is a major weakness for many applications, especially those supporting accurate pedestrian routing. Researchers have developed various algorithms to generate pedestrian walkways based on datasets, including high-resolution images, existing map databases, and GPS data; however, the semantic attribution of pedestrian walkways is often ignored. The objective of our study is to automatically extract semantic information including incline values and the different categories of pedestrian paths from multi-source spatial data, such as crowdsourced GPS tracking data, land use data, and motor vehicle road (MVR) networks. Incline values for each pedestrian path were derived from tracking data through elevation filtering using wavelet theory and a similarity-based map-matching method. To automatically categorize pedestrian paths into five classes including sidewalk, crosswalk, entrance walkway, indoor path, and greenway, we developed a hierarchical strategy of spatial analysis using land use data and MVR networks. The effectiveness of our proposed method is demonstrated using real datasets including GPS tracking data collected by volunteers, land use data acquired from OpenStreetMap, and MVR network data downloaded from Gaode Map. Numéro de notice : A2022-083 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1902530 En ligne : https://doi.org/10.1080/13658816.2021.1902530 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99480 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2022011	SL	Revue	Centre de documentation	Revues en salle	Disponible

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

Public [article]  inPattern recognition > vol 121 (January 2022) . - n° 108252 Titre : Pedestrian trajectory prediction with convolutional neural networks Type de document : Article/Communication Auteurs : Simone Zamboni, Auteur ; Zekarias Tilahun Kefato, Auteur ; Sarunas Girdzijauskas, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 108252 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] classification par réseau neuronal récurrent [Termes IGN] distance euclidienne [Termes IGN] filtre de Gauss [Termes IGN] itinéraire piétionnier [Termes IGN] modèle de simulation [Termes IGN] navigation pédestre [Termes IGN] piéton [Termes IGN] prévision à court terme [Termes IGN] réseau social [Termes IGN] trajet (mobilité) Résumé : (auteur) Predicting the future trajectories of pedestrians is a challenging problem that has a range of application, from crowd surveillance to autonomous driving. In literature, methods to approach pedestrian trajectory prediction have evolved, transitioning from physics-based models to data-driven models based on recurrent neural networks. In this work, we propose a new approach to pedestrian trajectory prediction, with the introduction of a novel 2D convolutional model. This new model outperforms recurrent models, and it achieves state-of-the-art results on the ETH and TrajNet datasets. We also present an effective system to represent pedestrian positions and powerful data augmentation techniques, such as the addition of Gaussian noise and the use of random rotations, which can be applied to any model. As an additional exploratory analysis, we present experimental results on the inclusion of occupancy methods to model social information, which empirically show that these methods are ineffective in capturing social interaction. Numéro de notice : A2022-109 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.patcog.2021.108252 Date de publication en ligne : 13/08/2021 En ligne : https://doi.org/10.1016/j.patcog.2021.108252 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99615 [article]

Simulating multi-exit evacuation using deep reinforcement learning / Dong Xu in Transactions in GIS, Vol 25 n° 3 (June 2021)  

Public [article]  inTransactions in GIS > Vol 25 n° 3 (June 2021) . - pp 1542-1564 Titre : Simulating multi-exit evacuation using deep reinforcement learning Type de document : Article/Communication Auteurs : Dong Xu, Auteur ; Xiao Huang, Auteur ; Joseph Mango, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1542-1564 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse comparative [Termes IGN] apprentissage par renforcement [Termes IGN] distribution spatiale [Termes IGN] itinéraire piétionnier [Termes IGN] modèle de simulation [Termes IGN] réseau neuronal profond Résumé : (Auteur) Conventional simulations on multi-exit indoor evacuation focus primarily on how to determine a reasonable exit based on numerous factors in a changing environment. Results commonly include some congested and other under-utilized exits, especially with large numbers of pedestrians. We propose a multi-exit evacuation simulation based on deep reinforcement learning (DRL), referred to as the MultiExit-DRL, which involves a deep neural network (DNN) framework to facilitate state-to-action mapping. The DNN framework applies Rainbow Deep Q-Network (DQN), a DRL algorithm that integrates several advanced DQN methods, to improve data utilization and algorithm stability and further divides the action space into eight isometric directions for possible pedestrian choices. We compare MultiExit-DRL with two conventional multi-exit evacuation simulation models in three separate scenarios: varying pedestrian distribution ratios; varying exit width ratios; and varying open schedules for an exit. The results show that MultiExit-DRL presents great learning efficiency while reducing the total number of evacuation frames in all designed experiments. In addition, the integration of DRL allows pedestrians to explore other potential exits and helps determine optimal directions, leading to a high efficiency of exit utilization. Numéro de notice : A2021-466 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Numéro de périodique nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12738 Date de publication en ligne : 11/03/2021 En ligne : https://doi.org/10.1111/tgis.12738 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98085 [article]

What is the difference between augmented reality and 2D navigation electronic maps in pedestrian wayfinding? / Weihua Dong in Cartography and Geographic Information Science, vol 48 n° 3 (May 2021)  

Public [article]  inCartography and Geographic Information Science > vol 48 n° 3 (May 2021) . - pp 225 - 240 Titre : What is the difference between augmented reality and 2D navigation electronic maps in pedestrian wayfinding? Type de document : Article/Communication Auteurs : Weihua Dong, Auteur ; Yulin Wu, Auteur ; Tong Qin, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 225 - 240 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse comparative [Termes IGN] analyse géovisuelle [Termes IGN] carte électronique [Termes IGN] cognition [Termes IGN] itinéraire piétionnier [Termes IGN] navigation pédestre [Termes IGN] oculométrie [Termes IGN] réalité augmentée [Termes IGN] visualisation 2D [Vedettes matières IGN] Cartologie Résumé : (auteur) Augmented reality (AR) navigation aids have become widely used in pedestrian navigation, yet few studies have verified their usability from the perspective of human spatial cognition, such as visual attention, cognitive processing, and spatial memory. We conducted an empirical study in which smartphone-based AR aids were compared with a common two-dimensional (2D) electronic map. We conducted eye-tracking wayfinding experiments, in which 73 participants used either a 2D electronic map or AR navigation aids. We statistically compared participants’ wayfinding performance, visual attention, and route memory between two groups (AR and 2D map navigation aids). The results showed their wayfinding performance did not differ significantly. Regarding visual attention, the participants using AR tended to have significantly shorter fixation durations, greater saccade amplitudes, and smaller pupil sizes on average than the 2D map participants, which indicates lower average cognitive workloads throughout the wayfinding process. Considering attention on environmental objects, the participants using AR paid less visual attention to buildings but more to persons than the participants using 2D maps. Sketched routes results revealed that it was more difficult for AR participants to form a clear memory of the route. The aim of this study is to inspire more usability research on AR navigation. Numéro de notice : A2021-510 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2021.1871646 Date de publication en ligne : 02/02/2021 En ligne : https://doi.org/10.1080/15230406.2021.1871646 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97532 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
032-2021031	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Modelling the effect of landmarks on pedestrian dynamics in urban environments / Gabriele Filomena in Computers, Environment and Urban Systems, vol 86 (March 2021)  

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Using multi-agent simulation to predict natural crossing points for pedestrians and choose locations for mid-block crosswalks / Egor Smirrnov in Geo-spatial Information Science, vol 23 n° 4 (December 2020)  

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Machine‐learning prediction models for pedestrian traffic flow levels: Towards optimizing walking routes for blind pedestrians / Achituv Cohen in Transactions in GIS, Vol 24 n° 5 (October 2020)  

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Point clouds for direct pedestrian pathfinding in urban environments / Jesus Balado in ISPRS Journal of photogrammetry and remote sensing, vol 148 (February 2019)  

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Journées ESRI transports et infrastructure / Anonyme in Géomatique expert, n° 111 (juillet- août 2016)

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Multidimensional Similarity Measuring for Semantic Trajectories / Andre Salvaro Furtado in Transactions in GIS, vol 20 n° 2 (April 2016)  

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The impact of planning on pedestrian movement: contrasting pedestrian movement models in pre-modern and modern neighborhoods in Israel / Itzhak Omer in International journal of geographical information science IJGIS, vol 29 n° 12 (December 2015)  

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Cas d'utilisation des cartes de randonnées avec représentation à symboles multiples / Olivia Gautrais (2015)

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Pedestrian navigation services: Challenges and current trends / Hassan A. Karimi in Geomatica, vol 67 n° 4 (December 2013)  

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Location-based illustration mapping applications and editing tools / Min Lu in Cartographica, vol 48 n° 2 (June 2013)  

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