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Detecting spatiotemporal propagation patterns of traffic congestion from fine-grained vehicle trajectory data / Haoyi Xiong in International journal of geographical information science IJGIS, vol 37 n° 5 (May 2023)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 37 n° 5 (May 2023) . - pp 1157-1179 Titre : Detecting spatiotemporal propagation patterns of traffic congestion from fine-grained vehicle trajectory data Type de document : Article/Communication Auteurs : Haoyi Xiong, Auteur ; Xun Zhou, Auteur ; David A. Bennett, Auteur Année de publication : 2023 Article en page(s) : pp 1157-1179 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] déformation temporelle dynamique (algorithme) [Termes IGN] détection d'anomalie [Termes IGN] données spatiotemporelles [Termes IGN] événement [Termes IGN] flux [Termes IGN] gestion de trafic [Termes IGN] réseau routier [Termes IGN] trafic routier Résumé : (auteur) Traffic congestion on a road segment typically begins as a small-scale spatiotemporal event that can then propagate throughout a road network and produce large-scale disruptions to a transportation system. In current techniques for the analysis of network flow, data is often aggregated to relatively large (e.g. 5 min) discrete time steps that obscure the small-scale spatiotemporal interactions that drive larger-scale dynamics. We propose a new method that handles fine-grained data to better capture those dynamics. Propagation patterns of traffic congestion are represented as spatiotemporally connected events. Each event is captured as a time series at the temporal resolution of the available trajectory data and at the spatial resolution of the network edge. The spatiotemporal propagation patterns of traffic congestion are captured using Dynamic Time Warping and represented as a set of directed acyclic graphs of spatiotemporal events. Results from this method are compared to an existing method using fine-grained data derived from an agent-based model of traffic simulation. Our method outperforms the existing method. Our method also successfully detects congestion propagation patterns that were reported by media news using sparse real-world data derived from taxis. Numéro de notice : A2023-225 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2023.2178653 Date de publication en ligne : 22/02/2023 En ligne : https://doi.org/10.1080/13658816.2023.2178653 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103177 [article]

MTMGNN: Multi-time multi-graph neural network for metro passenger flow prediction / Du Yin in Geoinformatica, vol 27 n° 1 (January 2023)  

Public [article]  inGeoinformatica > vol 27 n° 1 (January 2023) . - pp 77 - 105 Titre : MTMGNN: Multi-time multi-graph neural network for metro passenger flow prediction Type de document : Article/Communication Auteurs : Du Yin, Auteur ; Renhe Jiang, Auteur ; Jiewen Deng, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 77 - 105 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] attention (apprentissage automatique) [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] déformation temporelle dynamique (algorithme) [Termes IGN] données multitemporelles [Termes IGN] données spatiotemporelles [Termes IGN] flux [Termes IGN] gestion de trafic [Termes IGN] origine - destination [Termes IGN] réseau neuronal de graphes [Termes IGN] système de transport intelligent [Termes IGN] trafic urbain [Termes IGN] transport public [Termes IGN] utilisateur Résumé : (auteur) The passenger flow prediction of the public metro system is a core and critical part of the intelligent transportation system, and is essential for traffic management, metro planning, and emergency safety measures. Most methods chose the recent segment from historical data as input to predict the future traffic flow; however, this would lead to the loss of the inherent characteristic information of the metro passenger flow’s daily morning and evening peak. Therefore, this study aggregates the recent-term and long-term information and use a long-term Gated Convolutional Neural Network (Gated CNN) to extract the temporal feature from the complex historical data. On the other hand, typical models did not consider the different spatial dependencies between different metro stations; this work proposes various adjacent relationships to characterize the degree of association between nodes. In order to extract spatial and temporal features at the same time, the historical data of recent-term and long-term is merged together to extract spatial features through a multi-graph neural network module. By combining Gated CNN and multi-graph module, we propose a multi-time multi-graph neural network named MTMGNN for metro passenger flow prediction. The result of our experiment on real-world datasets shows that our model MTMGNN is better than all state-of-art methods. Numéro de notice : A2023-113 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-022-00466-1 Date de publication en ligne : 25/04/2022 En ligne : https://doi.org/10.1007/s10707-022-00466-1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102478 [article]

Street-level traffic flow and context sensing analysis through semantic integration of multisource geospatial data / Yatao Zhang in Transactions in GIS, vol 26 n° 8 (December 2022)  

Public [article]  inTransactions in GIS > vol 26 n° 8 (December 2022) . - pp 3330 - 3348 Titre : Street-level traffic flow and context sensing analysis through semantic integration of multisource geospatial data Type de document : Article/Communication Auteurs : Yatao Zhang, Auteur ; Martin Raubal, Auteur Année de publication : 2022 Article en page(s) : pp 3330 - 3348 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] allocation de Dirichlet latente [Termes IGN] appariement sémantique [Termes IGN] approche hiérarchique [Termes IGN] données multisources [Termes IGN] espace urbain [Termes IGN] flux [Termes IGN] milieu urbain [Termes IGN] point d'intérêt [Termes IGN] segmentation en régions [Termes IGN] Singapour [Termes IGN] trafic routier [Termes IGN] utilisation du sol Résumé : (auteur) Sensing urban spaces from multisource geospatial data is vital to understanding the transportation system in the urban context. However, the complexity of urban context and its indirect interaction with traffic flow deepen the difficulty of exploring their relationship. This study proposes a geo-semantic framework first to generate semantic representations of multi-hierarchical urban context and street-level traffic flow, and then investigate their mutual correlation and predictability using a novel semantic matching method. The results demonstrate that each street is associated with its multi-hierarchical spatial signatures of urban context and street-level temporal signatures of traffic flow. The correlation between urban context and traffic flow displays higher values after semantic matching than those in multi-hierarchies. Moreover, we found that utilizing traffic flow to predict urban context results in better accuracy than the reversed prediction. The results of signature analysis and relationship exploration can contribute to a deeper understanding of context-aware transportation research. Numéro de notice : A2022-916 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1111/tgis.13005 Date de publication en ligne : 27/11/2022 En ligne : https://doi.org/10.1111/tgis.13005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102348 [article]

Augmented reality for scene text recognition, visualization and reading to assist visually impaired people / Imene Ouali in Procedia Computer Science, vol 207 (2022)  

Public [article]  inProcedia Computer Science > vol 207 (2022) . - pp 158 - 167 Titre : Augmented reality for scene text recognition, visualization and reading to assist visually impaired people Type de document : Article/Communication Auteurs : Imene Ouali, Auteur ; Mohamed Ben Halima, Auteur ; Ali Wali, Auteur Année de publication : 2022 Article en page(s) : pp 158 - 167 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] enquête [Termes IGN] personne malvoyante [Termes IGN] réalité augmentée [Termes IGN] reconnaissance de caractères [Termes IGN] signalisation routière [Termes IGN] visualisation Résumé : (auteur) Reading traffic signs while driving a car for visually impaired people and people with visual problems is a very difficult task for them. This task is encountered every day, sometimes incorrect reading of traffic signs can lead to very serious results. In particular, the Arabic language is very difficult, making recognizing and viewing Arabic text a difficult task. In this context, we are looking for an effective solution to remove errors and results that can sometimes end someone's life. This article aims to correctly read traffic signs with Arabic text using augmented reality technology. Our system is composed of three modules. The first is text detection and recognition. The second is Text visualization. The third is Text to speech methods conversion. With this system, the user can have two different results. The first result is visual with much-improved text and enhancement. The second result is sound, he can hear the text aloud. This system is very applicable and effective for daily life. To assess the effectiveness of our work, we offer a survey to a group of visually impaired people to give their opinion on the use of our application. The results have been good for most people. Numéro de notice : A2023-010 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Article DOI : 10.1016/j.procs.2022.09.048 Date de publication en ligne : 19/10/2022 En ligne : https://doi.org/10.1016/j.procs.2022.09.048 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102119 [article]

Assessing road accidents in spatial context via statistical and non-statistical approaches to detect road accident hotspot using GIS / Yegane Khosravi in Geodetski vestnik, vol 66 n° 3 (September - November 2022)  

Public [article]  inGeodetski vestnik > vol 66 n° 3 (September - November 2022) . - pp 412 - 431 Titre : Assessing road accidents in spatial context via statistical and non-statistical approaches to detect road accident hotspot using GIS Type de document : Article/Communication Auteurs : Yegane Khosravi, Auteur ; Farhad Hosseinali, Auteur ; Mostafa Adresi, Auteur Année de publication : 2022 Article en page(s) : pp 412 - 431 Note générale : bibliographie Langues : Anglais (eng) Slovène (slv) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] accident de la route [Termes IGN] analyse de groupement [Termes IGN] autocorrélation spatiale [Termes IGN] classification par nuées dynamiques [Termes IGN] corrélation automatique de points homologues [Termes IGN] distance de Manhattan [Termes IGN] estimation par noyau [Termes IGN] Iran [Termes IGN] méthode statistique [Termes IGN] pente [Termes IGN] processus de hiérarchisation analytique [Termes IGN] regroupement de données [Termes IGN] système d'information géographique Résumé : (auteur) Road accidents are among the most critical causes of fatality, personal injuries, and financial damage worldwide. Identifying accident hotspots and the causes of accidents and improving the condition of these hotspots is an economical way to improve road traffic safety. In this study, to identify the accident hotspots of “Dehbala” road located in Yazd province-Iran, statistical and non-statistical clustering methods were used. First, the weighting of the criteria was performed by an expert using the AHP method. Hence, the spatial correlation of slope and curvature was calculated by Global Moran’I. Anselin Local Moran index and Getis-Ord Gi* and Kernel Density Estimation were used to identify accident hotspots based on accident location due to the density of points. As a result, four accident hotspots were obtained by the Anselin Local Moran index, three accident hotspots by Getis-Ord Gi*and one accident-prone area were obtained by Kernel Density Estimation method. Three algorithms, k-means, k-medoids, and DBSCAN, were used to identify accident-prone areas or points using non-statistical methods. The dense cluster of each method was considered as an accident-prone cluster. Then the results of statistical and non- statistical methods were intersected with each other and the final accident-prone area was obtained. This study revealed the effect of geometric charcateristics of the road (slope and curvature) on the occurance of accidents. Numéro de notice : A2022-781 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.15292/geodetski-vestnik.2022.03.412-431 Date de publication en ligne : 04/08/2022 En ligne : https://doi.org/10.15292/geodetski-vestnik.2022.03.412-431 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101864 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
139-2022031	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Exploring multi-modal evacuation strategies for a landlocked population using large-scale agent-based simulations / Kevin Chapuis in International journal of geographical information science IJGIS, vol 36 n° 9 (September 2022)  

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A model development on GIS-driven data to predict temporal daily collision through integrating Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN) algorithms; case study: Tehran-Qazvin freeway / Reza Sanayeia in Geocarto international, vol 37 n° 14 ([20/07/2022])  

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A framework for urban land use classification by integrating the spatial context of points of interest and graph convolutional neural network method / Yongyang Xu in Computers, Environment and Urban Systems, vol 95 (July 2022)  

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Detecting spatiotemporal traffic events using geosocial media data / Shishuo Xu in Computers, Environment and Urban Systems, vol 94 (June 2022)  

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A geospatial workflow for the assessment of public transit system performance using near real-time data / Anastassios Dardas in Transactions in GIS, vol 26 n° 4 (June 2022)  

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GIS-based assessment of long-term traffic accidents using spatiotemporal and empirical Bayes analysis in Turkey / Saffet Erdoğan in Applied geomatics, vol 14 n° 2 (June 2022)  

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3D lidar point-cloud projection operator and transfer machine learning for effective road surface features detection and segmentation / Heyang Thomas Li in The Visual Computer, vol 38 n° 5 (May 2022)  

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A graph attention network for road marking classification from mobile LiDAR point clouds / Lina Fang in International journal of applied Earth observation and geoinformation, vol 108 (April 2022)  

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GIS-based employment availabilities by mode of transport in Kuwait / S. Alkheder in Applied geomatics, vol 14 n° 1 (March 2022)  

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Traffic sign three-dimensional reconstruction based on point clouds and panoramic images / Minye Wang in Photogrammetric record, vol 37 n° 177 (March 2022)  

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