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Analysis of cycling network evolution in OpenStreetMap through a data quality prism / Raphaël Bres (2023)  

Public Titre : Analysis of cycling network evolution in OpenStreetMap through a data quality prism Type de document : Article/Communication Auteurs : Raphaël Bres, Auteur ; Veronika Peralta, Auteur ; Arnaud Le Guilcher , Auteur ; Thomas Devogele , Auteur ; Ana-Maria Olteanu-Raimond , Auteur ; Cyril de Runz, Auteur Editeur : Göttingen : Copernicus publications Année de publication : 2023 Collection : AGILE GIScience Series num. 4 Conférence : AGILE 2023, 26th international AGILE Conference on Geographic Information Science, Spatial data for design 13/06/2023 16/06/2023 Delft Pays-Bas OA Proceedings Importance : n° 3 ; 9 p. Note générale : bibliographie voir aussi le rapport de reproductibilité : https://doi.org/10.17605/OSF.IO/9KP7U Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] bicyclette [Termes IGN] méthode de Monte-Carlo [Termes IGN] mobilité territoriale [Termes IGN] mobilité urbaine [Termes IGN] modèle de simulation [Termes IGN] OpenStreetMap [Termes IGN] qualité des données [Termes IGN] voie cyclable Résumé : (auteur) Cycling practice has been constantly increasing for several years and the COVID crisis has just accelerated the process. Indeed, more and more municipalities have developed new cycle paths to facilitate cycling. Considering this increasing interest for cycling, it makes sense to study how this recent evolution is reflected in the underlying representation of the cycling network in the geographic databases. Main studies analysing the evolution of the road network focus on the motor vehicle network in the major cities of the world. These studies do not seem applicable to cycling network specially to some low population density areas or even to smaller cities. This paper analyses the changes in the cycling network through OSM data from a data freshness perspective. These changes can be either updates from changes in the real-world network or upgrades to the network. To these end, we propose a method using a Monte Carlo simulation (MCS) to analyse the frequency of changes in cycling routes in several areas with different population density, all in the Loire Valley region in France. We also define the cycling network, which is a very complex concept and we explain how it is represented in OSM data and suffers from different data quality issues. Results show that the number of changes across time are similar in areas having a similar population density, while being lower in low population density areas. These phenomena is higher in the cycling network compared to other networks. Numéro de notice : C2023-011 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : Vers HAL Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/agile-giss-4-3-2023 Date de publication en ligne : 06/06/2023 En ligne : https://doi.org/10.5194/agile-giss-4-3-2023 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103308

Correlation of road network structure and urban mobility intensity: An exploratory study using geo-tagged tweets / Li Geng in ISPRS International journal of geo-information, vol 12 n° 1 (January 2023)  

Public [article]  inISPRS International journal of geo-information > vol 12 n° 1 (January 2023) . - n° 7 Titre : Correlation of road network structure and urban mobility intensity: An exploratory study using geo-tagged tweets Type de document : Article/Communication Auteurs : Li Geng, Auteur ; Ke Zhang, Auteur Année de publication : 2023 Article en page(s) : n° 7 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] corrélation automatique de points homologues [Termes IGN] données issues des réseaux sociaux [Termes IGN] Etats-Unis [Termes IGN] géobalise [Termes IGN] mobilité urbaine [Termes IGN] OpenStreetMap [Termes IGN] réseau routier [Termes IGN] Twitter Résumé : (auteur) Urban planners have been long interested in understanding how urban structure and activities are mutually influenced. Human mobility and economic activities naturally drive the formation of road network structure and the accessibility of the latter shapes the patterns of movement flow across urban space. In this paper, we perform an exploratory study on the relationship between the street network structure and the intensity of human movement in urban areas. We focus on two cities and we utilize a dataset of geo-tagged tweets that can form a proxy to urban mobility and the corresponding street networks as obtained from OpenStreetMap. We apply three network centrality measures, including closeness, betweenness and straightness centrality, calculated at a global or local scale, as well as under mixed or individual transportation mode (e.g., driving, biking and walking) with its directional accessibility, to uncover the structural properties of urban street networks. We further design an urban area transition network and apply PageRank to capture the intensity of human mobility. Our correlation analysis indicates different centrality metrics have different levels of correlation with the intensity of human movement. The closeness centrality consistently shows the highest correlation (with a coefficient around 0.6) with human movement intensity when calculated at a global scale, while straightness centrality often shows no correlation at the global scale or weaker correlation ρ≈0.4 at the local scale. The correlation levels further depend on the type of directional accessibility and of various types of transportation modes. Hence, the directionality and transportation mode, largely ignored in the analysis of road networks, are crucial. Furthermore, the strength of the correlation varies in the two cities examined, indicating potential differences in urban spatial structure and human mobility patterns. Numéro de notice : A2023-105 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueNat DOI : 10.3390/ijgi12010007 Date de publication en ligne : 28/12/2022 En ligne : https://doi.org/10.3390/ijgi12010007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102433 [article]

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]

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]

Coupling graph deep learning and spatial-temporal influence of built environment for short-term bus travel demand prediction / Tianhong Zhao in Computers, Environment and Urban Systems, vol 94 (June 2022)  

Public [article]  inComputers, Environment and Urban Systems > vol 94 (June 2022) . - n° 101776 Titre : Coupling graph deep learning and spatial-temporal influence of built environment for short-term bus travel demand prediction Type de document : Article/Communication Auteurs : Tianhong Zhao, Auteur ; Zhengdong Huang, Auteur ; Wei Tu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101776 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] apprentissage profond [Termes IGN] bati [Termes IGN] données spatiotemporelles [Termes IGN] gestion de trafic [Termes IGN] graphe [Termes IGN] logement [Termes IGN] migration pendulaire [Termes IGN] modèle de simulation [Termes IGN] régression géographiquement pondérée [Termes IGN] service public [Termes IGN] Shenzhen [Termes IGN] système de transport intelligent [Termes IGN] transport public [Termes IGN] transport urbain Résumé : (auteur) Accurate and robust short-term bus travel prediction facilitates operating the bus fleet to provide comfortable and flexible bus services. The built environment, including land use, buildings, and public facilities, has an important influence on bus travel demand prediction. However, previous studies regarded the built environment as a static feature thus even ignored its influence on bus travel in deep learning framework. To fill this gap, we propose a graph deep learning-based approach coupling with spatiotemporal influence of built environment (GDLBE) to enhance short-term bus travel demand prediction. A time-dependent geographically weighted regression method is used to resolve the dynamic influence of the built environment on bus travel demand at different times of the day. A graph deep learning module is used to capture the comprehensive spatial and temporal dependency behind massive bus travel demand. The short-term bus travel demand is predicted by fusing the dynamic built environment influences and spatiotemporal dependency. An experiment in Shenzhen is conducted to evaluate the performance of the proposed approach. Baseline methods are compared, and the results demonstrate that the proposed approach outperforms the baselines. These results will help bus fleet dispatch for smart transportation. Numéro de notice : A2022-245 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101776 Date de publication en ligne : 12/03/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101776 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100185 [article]

The effect of intra-urban mobility flows on the spatial heterogeneity of social media activity: investigating the response to rainfall events / Sidgley Camargo de Andrade in International journal of geographical information science IJGIS, vol 36 n° 6 (June 2022)  

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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)  

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Discovering co-location patterns in multivariate spatial flow data / Jiannan Cai in International journal of geographical information science IJGIS, vol 36 n° 4 (April 2022)  

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Identification and classification of routine locations using anonymized mobile communication data / Gonçalo Ferreira in ISPRS International journal of geo-information, vol 11 n° 4 (April 2022)  

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Early warning of COVID-19 hotspots using human mobility and web search query data / Takahiro Yabe in Computers, Environment and Urban Systems, vol 92 (March 2022)  

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SNN_flow: a shared nearest-neighbor-based clustering method for inhomogeneous origin-destination flows / Qiliang Liu in International journal of geographical information science IJGIS, vol 36 n° 2 (February 2022)  

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Apport de l’intelligence artificielle au domaine des villes intelligentes : application à l’assistance des déplacements des personnes à mobilité réduite / Nathan Aky (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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Understanding the modifiable areal unit problem in dockless bike sharing usage and exploring the interactive effects of built environment factors / Feng Gao in International journal of geographical information science IJGIS, vol 35 n° 9 (September 2021)  

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