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A framework to manage uncertainty in the computation of waste collection routes after a flood / Arnaud Le Guilcher in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-4-2021 (July 2021)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-4-2021 (July 2021) . - pp 61 - 68 Titre : A framework to manage uncertainty in the computation of waste collection routes after a flood Type de document : Article/Communication Auteurs : Arnaud Le Guilcher , Auteur ; Sofiane Martel, Auteur ; Mickaël Brasebin , Auteur ; Yann Méneroux , Auteur Année de publication : 2021 Projets : 1-Pas de projet / Conférence : ISPRS 2021, Commission 4, 24th ISPRS Congress, Imaging today foreseeing tomorrow 05/07/2021 09/07/2021 Nice on-line France OA Annals Commission 4 Article en page(s) : pp 61 - 68 Note générale : biblographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] cadre conceptuel [Termes IGN] calcul d'itinéraire [Termes IGN] catastrophe naturelle [Termes IGN] collecte des déchets [Termes IGN] discrétisation spatiale [Termes IGN] incertitude géométrique [Termes IGN] inondation [Termes IGN] programmation stochastique [Termes IGN] variable aléatoire Résumé : (auteur) In this paper, we describe a framework to find a good quality waste collection tour after a flood, without having to solve a complicated optimization problem from scratch in limited time. We model the computation of a waste collection tour as a capacitated routing problem, on the vertices or on the edges of a graph, with uncertain waste quantities and uncertain road availability. Multiple models have been conceived to manage uncertainty in routing problems, and we build on the ideas of discretizing the uncertain parameters and computing master solutions that can be adapted to propose an original method to compute efficient solutions. We first introduce our model for the progressive removal of the uncertainty, then outline our method to compute solutions: our method first considers a low-dimensional set of random variables that govern the behaviour of the problem parameters, discretizes these variables and computes a solution for each discrete point before the flood, and then uses these solutions as a basis to build operational solutions when there are enough information about the parameters of the routing problem. We then give computational tools to implement this method. We give a framework to compute the basis of solutions in an efficient way, by computing all the solutions simultaneously and sharing information (that can lead to good quality solutions) between the different problems based on how close their parameters are, and we also describe how real solutions can be derived from this basis. Our main contributions are our model for the progressive removal of uncertainty, our multi-step method to compute efficient solutions, and our intrusive framework to compute solutions on the discrete grid of parameters. Numéro de notice : A2021-316 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-4-2021-61-2021 En ligne : https://doi.org/10.5194/isprs-annals-V-4-2021-61-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97946 [article]

Finding the most navigable path in road networks / Ramneek Kaur in Geoinformatica [en ligne], vol 25 n° 1 (January 2021)  

Public [article]  inGeoinformatica [en ligne] > vol 25 n° 1 (January 2021) . - pp 207 - 240 Titre : Finding the most navigable path in road networks Type de document : Article/Communication Auteurs : Ramneek Kaur, Auteur ; Vikram Goyal, Auteur ; Venkata M. V. Gunturi, Auteur Année de publication : 2021 Article en page(s) : pp 207 - 240 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] arc [Termes IGN] calcul d'itinéraire [Termes IGN] programmation dynamique [Termes IGN] réseau routier [Termes IGN] temps de trajet Résumé : (Auteur) Input to the Most Navigable Path (MNP) problem consists of the following: (a) a road network represented as a directed graph, where each edge is associated with numeric attributes of cost and “navigability score” values; (b) a source and a destination and; (c) a budget value which denotes the maximum permissible cost of the solution. Given the input, MNP aims to determine a path between the source and the destination which maximizes the navigability score while constraining its cost to be within the given budget value. The problem can be modeled as the arc orienteering problem which is known to be NP-hard. The current state-of-the-art for this problem may generate paths having loops, and its adaptation for MNP that yields simple paths, was found to be inefficient. In this paper, we propose five novel algorithms for the MNP problem. Our algorithms first compute a seed path from the source to the destination, and then modify the seed path to improve its navigability. We explore two approaches to compute the seed path. For modification of the seed path, we explore different Dynamic Programming based approaches. We also propose an indexing structure for the MNP problem which helps in reducing the running time of some of our algorithms. Our experimental results indicate that the proposed solutions yield comparable or better solutions while being orders of magnitude faster than the current state-of-the-art for large real road networks. Numéro de notice : A2021-095 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10707-020-00428-5 Date de publication en ligne : 03/01/2021 En ligne : https://doi.org/10.1007/s10707-020-00428-5 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96935 [article]

Machine learning for the distributed and dynamic management of a fleet of taxis and autonomous shuttles / Tatiana Babicheva (2021)  

Public Titre : Machine learning for the distributed and dynamic management of a fleet of taxis and autonomous shuttles Titre original : Machine Learning pour la gestion distribuée et dynamique d’une flotte de taxis et navettes autonomes Type de document : Thèse/HDR Auteurs : Tatiana Babicheva, Auteur ; Leïla Kloul, Directeur de thèse ; Dominique Barth, Directeur de thèse Editeur : Université Paris-Saclay (Université Paris 11) Année de publication : 2021 Importance : 190 p. Format : 21 x 30 cm Note générale : bibliographie Thèse de Doctorat de l'Université Paris-Saclay, Informatique Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] apprentissage par renforcement [Termes IGN] autopartage [Termes IGN] calcul d'itinéraire [Termes IGN] circulation urbaine [Termes IGN] méthode heuristique [Termes IGN] navigation autonome [Termes IGN] OpenStreetMap [Termes IGN] optimisation (mathématiques) [Termes IGN] réseau neuronal artificiel [Termes IGN] réseau routier [Termes IGN] taxi [Termes IGN] trafic routier [Termes IGN] véhicule électrique [Termes IGN] ville intelligente Résumé : (auteur) In this thesis are investigated methods to manage shared electric autonomous taxi urban systems under online context in which customer demands occur over time, and where vehicles are available for ride-sharing and require electric recharging management. We propose the heuristics based on problem decomposition which include road network repartition and highlighting of subproblems such as charging management, empty vehicle redistribution and dynamic ride-sharing.The set of new methods for empty vehicle redistribution is proposed, such as proactive, meaning to take into account both current demand and anticipated future demand, in contrast to reactive methods, which act based on current demand only.We provide the reinforcement learning in different levels depending on granularity of the system.We propose station-based RL model for small networks and zone-based RL model, where the agents are zones of the city obtained by partitioning, for huge ones. The complete information optimisation is provided in order to analyse the system performance a-posteriori in offline context.The evaluation of the performance of proposed methods is provided in set of road networks of different nature and size. The proposed method provides promising results outperforming the other tested methods and the real data on the taxi system performance in terms of number of satisfied passengers under fixed fleet size. Note de contenu : 1- Introduction 2- State-of-the-art 3- Modelling the electrical aTaxisystem 4- Functional architecture of aTaxi system management 5- Reinforcement learning for aTaxi system optimisation 6- Evaluation scenarii 7- Numerical evaluation of aTaxi systems 8- Conclusion and discussion Numéro de notice : 28591 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Thèse française Note de thèse : thèse de Doctorat : Informatique : Paris-Saclay : 2021 Organisme de stage : Données et Algorithmes pour une ville intelligente et durable (UVSQ) DOI : sans En ligne : https://tel.archives-ouvertes.fr/tel-03230845/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97968

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) [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]

Automated labeling of schematic maps by optimization with knowledge acquired from existing maps / Tian Lan in Transactions in GIS, Vol 24 n° 6 (December 2020)  

Public [article]  inTransactions in GIS > Vol 24 n° 6 (December 2020) . - pp 1722 - 1739 Titre : Automated labeling of schematic maps by optimization with knowledge acquired from existing maps Type de document : Article/Communication Auteurs : Tian Lan, Auteur ; Zhilin Li, Auteur ; Qian Peng, Auteur ; Xinyu Gong, Auteur Année de publication : 2020 Article en page(s) : pp 1722 - 1739 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes IGN] automatisation [Termes IGN] calcul d'itinéraire [Termes IGN] Chine [Termes IGN] données cartographiques [Termes IGN] étiquetage sémantique [Termes IGN] Hong-Kong [Termes IGN] optimisation (mathématiques) [Termes IGN] réseau métropolitain Résumé : (Auteur) Schematic maps are simplified representations of line networks, aiming to help people quickly and accurately perform route planning and orientation tasks. The automated generation of such maps is generally treated as an optimization problem. Most researchers prefer to optimize network layouts and name labels separately, because optimizing them simultaneously is still intractable. It is found that optimizing network layouts is extensively studied, while optimizing name labels is rarely considered. In the optimization of name labels, constraints can be established with rules from cartographic experts, literature (e.g., specification and technical documents), and/or existing maps. However, some rules from experts and literature cannot be explicitly and mathematically expressed. This study aims to develop an automated labeling method with rules from existing maps. We first acquire the rules (i.e., the potential positions and the preferences of these positions) from some existing schematic maps and then integrate them into an optimization algorithm. Experimental evaluation is conducted by a questionnaire in terms of “ease level of finding name labels,” “congestion level,” and “satisfaction level” using Tianjin and Hong Kong metro schematic maps and the labels of our method. The results show that the proposed method can automatically generate effective labels. Numéro de notice : A2020-769 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12671 Date de publication en ligne : 06/08/2020 En ligne : https://doi.org/10.1111/tgis.12671 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96661 [article]

Les stations virtuelles au service de la cartographie mobile / Mathieu Regul in XYZ, n° 165 (décembre 2020)

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Generation of crowd arrival and destination locations/times in complex transit facilities / Brian Ricks in The Visual Computer, vol 36 n° 8 (August 2020)  

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SMSM: a similarity measure for trajectory stops and moves / Andre L. Lehmann in International journal of geographical information science IJGIS, vol 33 n° 9 (September 2019)  

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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 topographically preserved road‐network tile model and optimal routing method for virtual globes / Quanhua Dong in Transactions in GIS, vol 23 n° 2 (April 2019)  

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Inferring 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)  

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Géomatique webmapping en open source / David Collado (2019)

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Un algorithme pour battre le record du SwissTrainChallenge : poser le pied dans chacun des 26 cantons le plus rapidement possible en utilisant uniquement des transports publics / Emmanuel Cledat in XYZ, n° 157 (décembre 2018 - février 2019)

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An algorithm for on-the-fly K shortest paths finding in multi-storey buildings using a hierarchical topology model / Rosen Ivanov in International journal of geographical information science IJGIS, vol 32 n° 11-12 (November - December 2018)  

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A hybrid ensemble learning method for tourist route recommendations based on geo-tagged social networks / Lin Wan in International journal of geographical information science IJGIS, vol 32 n° 11-12 (November - December 2018)  

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