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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)
[article]
Titre : Machine‐learning prediction models for pedestrian traffic flow levels: Towards optimizing walking routes for blind pedestrians Type de document : Article/Communication Auteurs : Achituv Cohen, Auteur ; Sagi Dalyot, Auteur Année de publication : 2020 Article en page(s) : pp 1264-1279 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] apprentissage automatique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données localisées des bénévoles
[Termes IGN] données spatiotemporelles
[Termes IGN] gestion des itinéraires
[Termes IGN] handicap
[Termes IGN] itinéraire piétionnier
[Termes IGN] modèle de simulation
[Termes IGN] navigation pédestre
[Termes IGN] OpenStreetMap
[Termes IGN] personne non-voyante
[Termes IGN] point d'intérêt
[Termes IGN] trafic routierRésumé : (Auteur) Navigation and orientation while walking in urban spaces pose serious challenges for blind pedestrians, sometimes even on a daily basis. Research shows the practicability of computerized weighted network route planning algorithms based on OpenStreetMap mapping data for calculating customized routes for blind pedestrians. While data about pedestrians and vehicle traffic flow at different times throughout the day influence the route choices of blind pedestrians, such data do not exist in OpenStreetMap. Quantifying the correlation between spatial structure and traffic flow could be used to fill this gap. As such, we investigated machine‐learning methods to develop a computerized model for predicting pedestrian traffic flow levels, with the objective of enriching the OpenStreetMap database. This article presents prediction results by implementing six machine‐learning algorithms based on parameters relating to the geometrical and topological configuration of streets in OpenStreetMap, as well as points‐of‐interest such as public transportation and shops. The Random Forest algorithm produced the best results, whereby 95% of the testing data were successfully predicted. These results indicate that machine‐learning algorithms can accurately generate necessary temporal data, which when combined with the available crowdsourced open mapping data could augment the reliability of route planning algorithms for blind pedestrians. Numéro de notice : A2020-700 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12674 Date de publication en ligne : 04/08/2020 En ligne : https://doi.org/10.1111/tgis.12674 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96210
in Transactions in GIS > Vol 24 n° 5 (October 2020) . - pp 1264-1279[article]Comparing pedestrians’ gaze behavior in desktop and in real environments / Weihua Dong in Cartography and Geographic Information Science, Vol 47 n° 5 (September 2020)
[article]
Titre : Comparing pedestrians’ gaze behavior in desktop and in real environments Type de document : Article/Communication Auteurs : Weihua Dong, Auteur ; Hua Liao, Auteur ; Bing Liu, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 432 - 451 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse comparative
[Termes IGN] analyse visuelle
[Termes IGN] comportement
[Termes IGN] espace urbain
[Termes IGN] lecture de carte
[Termes IGN] monde virtuel
[Termes IGN] navigation pédestre
[Termes IGN] oculométrie
[Termes IGN] piéton
[Termes IGN] test statistique
[Termes IGN] travail
[Termes IGN] vision par ordinateur
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) This research is motivated by the widespread use of desktop environments in the lab and by the recent trend of conducting real-world eye-tracking experiments to investigate pedestrian navigation. Despite the existing significant differences between the real world and the desktop environments, how pedestrians’ visual behavior in real environments differs from that in desktop environments is still not well understood. Here, we report a study that recorded eye movements for a total of 82 participants while they were performing five common navigation tasks in an unfamiliar urban environment (N = 39) and in a desktop environment (N = 43). By analyzing where the participants allocated their visual attention, what objects they fixated on, and how they transferred their visual attention among objects during navigation, we found similarities and significant differences in the general fixation indicators, spatial fixation distributions and attention to the objects of interest. The results contribute to the ongoing debate over the validity of using desktop environments to investigate pedestrian navigation by providing insights into how pedestrians allocate their attention to visual stimuli to accomplish navigation tasks in the two environments. Numéro de notice : A2020-488 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2020.176251 Date de publication en ligne : 29/05/2020 En ligne : https://doi.org/10.1080/15230406.2020.1762513 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95658
in Cartography and Geographic Information Science > Vol 47 n° 5 (September 2020) . - pp 432 - 451[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 032-2020051 RAB Revue Centre de documentation En réserve L003 Disponible Developing shopping and dining walking indices using POIs and remote sensing data / Yingbin Deng in ISPRS International journal of geo-information, vol 9 n° 6 (June 2020)
[article]
Titre : Developing shopping and dining walking indices using POIs and remote sensing data Type de document : Article/Communication Auteurs : Yingbin Deng, Auteur ; Yingwei Yan, Auteur ; Yichun Xie, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : 22 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] achat
[Termes IGN] couvert végétal
[Termes IGN] distance
[Termes IGN] données environnementales
[Termes IGN] loisir
[Termes IGN] navigation pédestre
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] OpenStreetMap
[Termes IGN] point d'intérêt
[Termes IGN] sport
[Termes IGN] température au sol
[Termes IGN] trajet (mobilité)Résumé : (auteur) Walking is one of the most commonly promoted traveling methods and is garnering increasing attention. Many indices/scores have been developed by scholars to measure the walkability in a local community. However, most existing walking indices/scores involve urban planning-oriented, local service-oriented, regional accessibility-oriented, and physical activity-oriented walkability assessments. Since shopping and dining are two major leisure activities in our daily lives, more attention should be given to the shopping or dining-oriented walking environment. Therefore, we developed two additional walking indices that focus on shopping or dining. The point of interest (POI), vegetation coverage, water coverage, distance to bus/subway station, and land surface temperature were employed to construct walking indices based on 50-meter street segments. Then, walking index values were categorized into seven recommendation levels. The field verification illustrates that the proposed walking indices can accurately represent the walking environment for shopping and dining. The results in this study could provide references for citizens seeking to engage in activities of shopping and dining with a good walking environment. Numéro de notice : A2020-310 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9060366 Date de publication en ligne : 02/06/2020 En ligne : https://doi.org/10.3390/ijgi9060366 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95157
in ISPRS International journal of geo-information > vol 9 n° 6 (June 2020) . - 22 p.[article]Pedestrian network generation based on crowdsourced tracking data / Xue Yang in International journal of geographical information science IJGIS, vol 34 n° 5 (May 2020)
[article]
Titre : Pedestrian network generation based on crowdsourced tracking data Type de document : Article/Communication Auteurs : Xue Yang, Auteur ; Luliang Tang, Auteur ; Chang Ren, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1051 - 1074 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] approche participative
[Termes IGN] base de données multi-représentation
[Termes IGN] correction géométrique
[Termes IGN] correction topographique
[Termes IGN] dimension fractale
[Termes IGN] données localisées des bénévoles
[Termes IGN] estimation par noyau
[Termes IGN] mobilité urbaine
[Termes IGN] navigation pédestre
[Termes IGN] regroupement de pointsRésumé : (auteur) Pedestrian networks play an important role in various applications, such as pedestrian navigation services and mobility modeling. This paper presents a novel method to extract pedestrian networks from crowdsourced tracking data based on a two-layer framework. This framework includes a walking pattern classification layer and a pedestrian network generation layer. In the first layer, we propose a multi-scale fractal dimension (MFD) algorithm in order to recognize the two different types of walking patterns: walking with a clear destination (WCD) or walking without a clear destination (WOCD). In the second layer, we generate the pedestrian network by combining the pedestrian regions and pedestrian paths. The pedestrian regions are extracted based on a modified connected component analysis (CCA) algorithm from the WOCD traces. We generate the pedestrian paths using a kernel density estimation (KDE)-based point clustering algorithm from the WCD traces. The pedestrian network generation results using two actual crowdsourced datasets show that the proposed method has good performance in both geometrical correctness and topological correctness. Numéro de notice : A2020-207 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1702197 Date de publication en ligne : 09/12/2019 En ligne : https://doi.org/10.1080/13658816.2019.1702197 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94888
in International journal of geographical information science IJGIS > vol 34 n° 5 (May 2020) . - pp 1051 - 1074[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2020051 RAB Revue Centre de documentation En réserve L003 Disponible Indoor localization for pedestrians with real-time capability using multi-sensor smartphones / Catia Real Ehrlich in Geo-spatial Information Science, vol 22 n° 2 (June 2019)
[article]
Titre : Indoor localization for pedestrians with real-time capability using multi-sensor smartphones Type de document : Article/Communication Auteurs : Catia Real Ehrlich, Auteur ; Jörg Blankenbach, Auteur Année de publication : 2019 Article en page(s) : pp 73 - 88 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement
[Termes IGN] altitude barométrique
[Termes IGN] Bluetooth
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] microsystème électromécanique
[Termes IGN] navigation à l'estime
[Termes IGN] navigation pédestre
[Termes IGN] positionnement en intérieur
[Termes IGN] téléphone intelligentRésumé : (Auteur) The localization of persons or objects usually refers to a position determined in a spatial reference system. Outdoors, this is usually accomplished with Global Navigation Satellite Systems (GNSS). However, the automatic positioning of people in GNSS-free environments, especially inside of buildings (indoors) poses a huge challenge. Indoors, satellite signals are attenuated, shielded or reflected by building components (e.g. walls or ceilings). For selected applications, the automatic indoor positioning is possible based on different technologies (e.g. WiFi, RFID, or UWB). However, a standard solution is still not available. Many indoor positioning systems are only suitable for specific applications or are deployed under certain conditions, e.g. additional infrastructures or sensor technologies. Smartphones, as popular cost-effective multi-sensor systems, is a promising indoor localization platform for the mass-market and is increasingly coming into focus. Today’s devices are equipped with a variety of sensors that can be used for indoor positioning. In this contribution, an approach to smartphone-based pedestrian indoor localization is presented. The novelty of this approach refers to a holistic, real-time pedestrian localization inside of buildings based on multi-sensor smartphones and easy-to-install local positioning systems. For this purpose, the barometric altitude is estimated in order to derive the floor on which the user is located. The 2D position is determined subsequently using the principle of pedestrian dead reckoning based on user's movements extracted from the smartphone sensors. In order to minimize the strong error accumulation in the localization caused by various sensor errors, additional information is integrated into the position estimation. The building model is used to identify permissible (e.g. rooms, passageways) and impermissible (e.g. walls) building areas for the pedestrian. Several technologies contributing to higher precision and robustness are also included. For the fusion of different linear and non-linear data, an advanced algorithm based on the Sequential Monte Carlo method is presented. Numéro de notice : A2019-325 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10095020.2019.1613778 Date de publication en ligne : 21/05/2019 En ligne : https://doi.org/10.1080/10095020.2019.1613778 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93327
in Geo-spatial Information Science > vol 22 n° 2 (June 2019) . - pp 73 - 88[article]Low-complexity online correction and calibration of pedestrian dead reckoning using map matching and GPS / Fabian Hölzke in Geo-spatial Information Science, vol 22 n° 2 (June 2019)PermalinkInferring user tasks in pedestrian navigation from eye movement data in real-world environments / Hua Liao in International journal of geographical information science IJGIS, Vol 33 n° 3-4 (March - April 2019)PermalinkPoint clouds for direct pedestrian pathfinding in urban environments / Jesus Balado in ISPRS Journal of photogrammetry and remote sensing, vol 148 (February 2019)PermalinkMéthodes d'apprentissage statistique pour la détection de la signalisation routière à partir de véhicules traceurs / Yann Méneroux (2019)PermalinkMultimodal scene understanding: algorithms, applications and deep learning, ch. 8. Multimodal localization for embedded systems: a survey / Imane Salhi (2019)PermalinkAdéquation algorithme architecture pour la localisation basée image sur système embarqué / David Vandergucht (2018)PermalinkI’m walking here! Checking the accuracy of an inertial-based pedestrian navigation system with a drone / Marcin Uradzinski in GPS world, vol 28 n° 6 (June 2017)PermalinkThe differentiation of point symbols using selected visual variables in the mobile augmented reality system / Łukasz Halik in Cartographic journal (the), Vol 54 n° 2 (May 2017)PermalinkPermalinkUnderstanding the bias of call detail records in human mobility research / Ziliang Zhao in International journal of geographical information science IJGIS, vol 30 n° 9-10 (September - October 2016)Permalink