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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]

Route intersection reduction with connected autonomous vehicles / Sadegh Motallebi in Geoinformatica [en ligne], vol 25 n° 1 (January 2021)  

Public [article]  inGeoinformatica [en ligne] > vol 25 n° 1 (January 2021) . - pp 99 - 125 Titre : Route intersection reduction with connected autonomous vehicles Type de document : Article/Communication Auteurs : Sadegh Motallebi, Auteur ; Hairuo Xie, Auteur ; Egemen Tanin, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 99 - 125 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] calcul d'itinéraire [Termes IGN] carrefour [Termes IGN] chemin le plus court, algorithme du [Termes IGN] gestion de trafic [Termes IGN] optimisation (mathématiques) [Termes IGN] réseau routier [Termes IGN] trafic routier Résumé : (Auteur) A common cause of traffic congestions is the concentration of intersecting vehicle routes. It can be difficult to reduce the intersecting routes in existing traffic systems where the routes are decided independently from vehicle to vehicle. The development of connected autonomous vehicles provides the opportunity to address the intersecting route problem as the route of vehicles can be coordinated globally. We prototype a traffic management system for optimizing traffic with connected autonomous vehicles. The system allocates routes to the vehicles based on streaming traffic data. We develop two route assignment algorithms for the system. The algorithms can help to mitigate traffic congestions by reducing intersecting routes. Extensive experiments are conducted to compare the proposed algorithms and two state-of-the-art route assignment algorithms with both synthetic and real road networks in a simulated traffic management system. The experimental results show that the proposed algorithms outperform the competitors in terms of the travel time of the vehicles. Numéro de notice : A2021-093 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10707-020-00420-z Date de publication en ligne : 23/08/2020 En ligne : https://doi.org/10.1007/s10707-020-00420-z Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96933 [article]

From small sets of GPS trajectories to detailed movement profiles: quantifying personalized trip-dependent movement diversity / Elham Naghizade in International journal of geographical information science IJGIS, vol 34 n° 10 (October 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 10 (October 2020) . - pp 2004 - 2029 Titre : From small sets of GPS trajectories to detailed movement profiles: quantifying personalized trip-dependent movement diversity Type de document : Article/Communication Auteurs : Elham Naghizade, Auteur ; jeffrey Chan, Auteur ; Martin Tomko, Auteur Année de publication : 2020 Article en page(s) : pp 2004 - 2029 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] comportement [Termes IGN] données GPS [Termes IGN] données spatiotemporelles [Termes IGN] échantillonnage [Termes IGN] gestion de trafic [Termes IGN] modélisation spatio-temporelle [Termes IGN] origine - destination [Termes IGN] trajet (mobilité) Résumé : (auteur) The ubiquity of personal sensing devices has enabled the collection of large, diverse, and fine-grained spatio-temporal datasets. These datasets facilitate numerous applications from traffic monitoring and management to location-based services. Recently, there has been an increasing interest in profiling individuals' movements for personalized services based on fine-grained trajectory data. Most approaches identify the most representative paths of a user by analyzing coarse location information, e.g., frequently visited places. However, even for trips that share the same origin and destination, individuals exhibit a variety of behaviors (e.g., a school drop detour, a brief stop at a supermarket). The ability to characterize and compare the variability of individuals' fine-grained movement behavior can greatly support location-based services and smart spatial sampling strategies. We propose a TRip DIversity Measure --TRIM – that quantifies the regularity of users' path choice between an origin and destination. TRIM effectively captures the extent of the diversity of the paths that are taken between a given origin and destination pair, and identifies users with distinct movement patterns, while facilitating the comparison of the movement behavior variations between users. Our experiments using synthetic and real datasets and across geographies show the effectiveness of our method. Numéro de notice : A2020-512 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1730849 Date de publication en ligne : 09/03/2020 En ligne : https://doi.org/10.1080/13658816.2020.1730849 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95666 [article]

A hybrid deep learning–based model for automatic car extraction from high-resolution airborne imagery / Mehdi Khoshboresh Masouleh in Applied geomatics, vol 12 n° 2 (June 2020)  

Public [article]  inApplied geomatics > vol 12 n° 2 (June 2020) . - pp 107 - 119 Titre : A hybrid deep learning–based model for automatic car extraction from high-resolution airborne imagery Type de document : Article/Communication Auteurs : Mehdi Khoshboresh Masouleh, Auteur ; Reza Shah-Hosseini, Auteur Année de publication : 2020 Article en page(s) : pp 107 - 119 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] extraction automatique [Termes IGN] gestion de trafic [Termes IGN] image à haute résolution [Termes IGN] image aérienne [Termes IGN] modèle orienté objet [Termes IGN] orthophotographie [Termes IGN] segmentation sémantique [Termes IGN] trafic routier [Termes IGN] véhicule automobile Résumé : (auteur) Automatic car extraction (ACE) from high-resolution airborne imagery (i.e., true-orthophoto) has been a hot research topic in the field of photogrammetry and machine learning. ACE from high-resolution airborne imagery is the most suitable method for control and monitoring practices in large cities such as traffic management. The use of deep learning–based feature extraction methods, such as convolutional neural networks, have been providing state-of-the-art performance in the last few years, particularly, these techniques have been successfully applied to automatic object extraction from images. In this paper, we proposed a novel hybrid method to take advantage of the semantic segmentation of high-resolution airborne imagery to ACE that is realized based on the combination of deep convolutional neural networks and restricted Boltzmann machine (RBM). This hybrid method is called RBMDeepNet. We trained and tested our model on the ISPRS Potsdam and Vaihingen benchmark datasets (non-big data) which is more challenging for ACE. Here, Potsdam data is a true-color dataset, and Vaihingen data is a false-color dataset. The results obtained in the present study showed that the proposed method for ACE from high-resolution airborne imagery achieves a 7% improvement in accuracy with about 10% improvement in processing time compared to similar methods. Numéro de notice : A2020-558 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s12518-019-00285-4 Date de publication en ligne : 06/08/2019 En ligne : https://doi.org/10.1007/s12518-019-00285-4 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95868 [article]

Detecting and mapping traffic signs from Google Street View images using deep learning and GIS / Andrew Campbell in Computers, Environment and Urban Systems, vol 77 (september 2019)  

Public [article]  inComputers, Environment and Urban Systems > vol 77 (september 2019) Titre : Detecting and mapping traffic signs from Google Street View images using deep learning and GIS Type de document : Article/Communication Auteurs : Andrew Campbell, Auteur ; Alan Both, Auteur ; Qian (Chayn) Sun, Auteur ; Qian (Chayn) Sun, Auteur Année de publication : 2019 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] apprentissage profond [Termes IGN] base de données routières [Termes IGN] détection d'objet [Termes IGN] feu de circulation [Termes IGN] gestion de trafic [Termes IGN] image Streetview [Termes IGN] réseau routier [Termes IGN] signalisation routière [Termes IGN] système d'information géographique [Termes IGN] trafic routier [Termes IGN] vision par ordinateur Résumé : (auteur) Street traffic sign infrastructure remains an extremely difficult asset for local government to manage due to its diverse physical structure and geographical distribution. A spatial registrar of traffic infrastructure is currently a required component of local government councils' mandatory road management plans. Recent advancements of object detection technology in machine learning have presented an automated approach for the detection and classification of street signage captured by Google's Street View (GSV) imagery. This paper explores the possibility of using deep learning to produce an autonomous system for detecting traffic signs on GSV images to assist in traffic assets monitoring and maintenance. By leveraging Google's Street View API, this research offers an economic approach of building purposeful street sign computer vision datasets. A custom object detection model was trained to detect and classify Stop and Give Way signs from images captured at intersection approaches. Considering the output detected bounding box coordinates, photogrammetry approach was applied to calculate the approximate location of each detected sign in two-dimensional geographical space. The newly located and classified street signs can be combined with relevant spatial data for implementation into an asset management system. By combining GIS and the GSV API, the process is completely scalable to any level of street sign classification scope. The experiments conducted on the road network of study area recorded a detection accuracy of 95.63% and classification accuracy of 97.82%. Our proposed automated approach to the detection and localisation of street sign infrastructure has displayed a promising potential for its use by local government authorities. Our workflow can be used to detect other traffic signs and applied to other road sections and other cities. Of primary importance, this approach takes an entirely free and open-source approach throughout. The continuation of Google's Street View program will account for the spatiotemporal representation of street sign infrastructure for the ongoing maintenance and renewal programs of this valuable asset. Numéro de notice : A2019-412 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.compenvurbsys.2019.101350 Date de publication en ligne : 07/06/2019 En ligne : https://doi.org/10.1016/j.compenvurbsys.2019.101350 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93536 [article]

Analyse spatiotemporelle des tournées de livraison d’une entreprise de livraison à domicile / Khaled Belhassine in Revue internationale de géomatique, vol 29 n° 2 (avril - juin 2019)  

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A methodology with a distributed algorithm for large-scale trajectory distribution prediction / QiuLei Guo in International journal of geographical information science IJGIS, Vol 33 n° 3-4 (March - April 2019)  

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Design principles of a stream-based framework for mobility analysis / Loic Salmon in Geoinformatica [en ligne], vol 21 n° 2 (April - June 2017)  

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Panda∗: A generic and scalable framework for predictive spatio-temporal queries / Abdeltawab M. Hendawi in Geoinformatica [en ligne], vol 21 n° 2 (April - June 2017)  

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Allier analyse géographique et expertise locale dans un SIG pour une stratégie territoriale de sécurité routière / Eliane Propeck-Zimmermann in Revue internationale de géomatique, vol 26 n° 2 (avril - juin 2016)  

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Exploring alternative map products to enhance transportation option awareness / Jessica V. Fayne in Cartography and Geographic Information Science, Vol 42 n° 4 (September 2015)  

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Location based services and fleet management: removing the entry barrier / E. Beinat in Geoinformatics, vol 7 n° 1 (01/01/2004)

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Navigation : principles of positioning and guidance / Bernhard Hofmann-Wellenhof (2003)

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Circulation urbaine en temps réel au grand Lyon / S. Petitier in Urb.AO, n° 11 (01/07/2002)

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Apport de l'information géographique dans le traitement des incidents ferroviaires / E. Rousseau (2002)

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