Centre de documentation
scientifique

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]

Exploring data fusion for multi-object detection for intelligent transportation systems using deep learning / Amira Mimouna (2022)  

Public Titre : Exploring data fusion for multi-object detection for intelligent transportation systems using deep learning Type de document : Thèse/HDR Auteurs : Amira Mimouna, Auteur ; Abdelmalik Taleb-Ahmed, Directeur de thèse ; Najoua Essoukri Ben Amara, Directeur de thèse Editeur : Valenciennes : Université polytechnique Hauts-de-France Année de publication : 2022 Note générale : bibliographie Thèse de doctorat pour obtenir le grade de Docteur de l'Université polytechnique Hauts-de-France et l'INSA Hauts-de-France et l'Université de Sousse, spécialité Electronique, Acoustique et Télécommunications Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal récurrent [Termes IGN] détection d'objet [Termes IGN] données publiques [Termes IGN] entropie [Termes IGN] profil d'obstacle [Termes IGN] segmentation d'image [Termes IGN] système de transport intelligent [Termes IGN] trafic urbain [Termes IGN] transformation en ondelettes [Termes IGN] vision par ordinateur [Termes IGN] zone d'intérêt Index. décimale : THESE Thèses et HDR Résumé : (auteur) Building reliable environment perception systems is a crucial task for autonomous driving, especially in dense traffic areas. Researching in this field is evolving increasingly. However, we are at the beginning of a research pathway towards a future generation of intelligent transportation systems. In fact, challenging conditions in real-world driving circumstances, infrastructure monitoring, and accurate real-time system response, are the predominant concerns when developing such systems. Recent improvements and breakthroughs in scene understanding for intelligent transportation systems have been mainly based on deep learning and the fusion of different modalities. In this context, firstly, we introduce OLIMP : A heterOgeneous MuLtimodal Dataset for Advanced EnvIronMent Perception . This is the first public, multimodal and synchronized dataset that includes Ultra Wide-Band (UWB) radar data, acoustic data, narrowband radar data and images. OLIMP comprises 407 scenes and 47,354 synchronized frames, including four categories: pedestrians, cyclists, cars and trams. The dataset presents various challenges related to dense urban traffic such as cluttered environments and differentweather conditions. To demonstrate the usefulness of the introduced dataset, we propose, afterwards, a fusion framework that combines the four modalities for multi object detection. The obtained results are promising and spur for future research. In short range settings, UWB radars represent a promising technology for building reliable obstacle detection systems as they are robust to environmental conditions. However, UWB radars suffer from a segmentation challenge: localizing relevant Regions Of Interests (ROIs) within its signals. Therefore, we put froward a segmentation approach to detect ROIs in an environment perception-dedicated UWB radar as a third contribution. Specifically, we implement a differential entropy analysis to detect ROIs. The obtained results show higher performance in terms of obstacle detection compared to state-of-theart techniques, as well as stable robustness even with low amplitude signals. Subsequently, we propose a novel framework that exploits Recurrent Neural Networks (RNNs) with UWB signals for multiple road obstacle detection as a deep learning-based approach. Features are extracted from the time-frequency domain using the discrete wavelet transform and are forwarded to the Long short-term memory (LSTM) network. The obtained results show that the LSTM-based system outperforms the other implemented related techniques in terms of obstacle detection. Note de contenu : 1- Introduction 2- Environment perception system: State of the art 3- OLIMP: A heterogeneous multimodal dataset for advanced environment perception 4- Multiple object detectors using UWB signals 5- Conclusions and perspectives Numéro de notice : 15289 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Electronique, Acoustique et Télécommunications : Université polytechnique Hauts-de-France : 2022 Organisme de stage : Institut d'électronique, de microélectronique et de nanotechnologie DOI : sans En ligne : https://hal.science/tel-03522730 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101520

GIS-based survey over the public transport strategy: An instrument for economic and sustainable urban traffic planning / Gabriela Droj in ISPRS International journal of geo-information, vol 11 n° 1 (January 2022)  

Public [article]  inISPRS International journal of geo-information > vol 11 n° 1 (January 2022) . - n° 16 Titre : GIS-based survey over the public transport strategy: An instrument for economic and sustainable urban traffic planning Type de document : Article/Communication Auteurs : Gabriela Droj, Auteur ; Laurentiu Droj, Auteur ; Ana-Cornelia Badea, Auteur Année de publication : 2022 Article en page(s) : n° 16 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] analyse spatiale [Termes IGN] modèle mathématique [Termes IGN] planification urbaine [Termes IGN] pollution atmosphérique [Termes IGN] Roumanie [Termes IGN] trafic routier [Termes IGN] trafic urbain [Termes IGN] transport public Résumé : (auteur) Traffic has a direct impact on local and regional economies, on pollution levels and is also a major source of discomfort and frustration for the public who have to deal with congestion, accidents or detours due to road works or accidents. Congestion in urban areas is a common phenomenon nowadays, as the main arteries of cities become congested during peak hours or when there are additional constraints such as traffic accidents and road works that slow down traffic on road sections. When traffic increases, it is observed that some roads are predisposed to congestion, while others are not. It is evident that both congestion and urban traffic itself are influenced by several factors represented by complex geospatial data and the spatial relationships between them. In this paper were integrated mathematical models, real time traffic data with network analysis and simulation procedures in order to analyze the public transportation in Oradea and the impact on urban traffic. A mathematical model was also adapted to simulate the travel choices of the population of the city and of the surrounding villages. Based on the network analysis, traffic analysis and on the traveling simulation, the elements generating traffic congestion in the inner city can be easily determined. The results of the case study are emphasizing that diminishing the traffic and its effects can be obtained by improving either the public transport density or its accessibility. Numéro de notice : A2022-039 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11010016 Date de publication en ligne : 30/12/2021 En ligne : https://doi.org/10.3390/ijgi11010016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99382 [article]

A multiagent systems with Petri Net approach for simulation of urban traffic networks / Mauricio Flores Geronimo in Computers, Environment and Urban Systems, vol 89 (September 2021)  

Public [article]  inComputers, Environment and Urban Systems > vol 89 (September 2021) . - n° 101662 Titre : A multiagent systems with Petri Net approach for simulation of urban traffic networks Type de document : Article/Communication Auteurs : Mauricio Flores Geronimo, Auteur ; Eduardo Gamaliel Hernandez Martinez, Auteur ; Enrique Ferreira Vasquez, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 101662 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] Matlab [Termes IGN] Montevideo [Termes IGN] réseau routier [Termes IGN] signalisation routière [Termes IGN] système multi-agents [Termes IGN] trafic routier [Termes IGN] trafic urbain Résumé : (auteur)This paper presents a novel model framework for complex urban traffic systems based on the interconnection of a dynamical multi-agent system in a macroscopic level. The agents describe all the types of street segments, intersections, sources and sinks of cars, modelling the behavior of the flow of vehicles through them as simple differential equations. These agents include the phenomena of changes in the flow rate due to congestions, traffic signals and the density of the vehicles. Traffic signal changes are obtained by the evolution of Petri Nets, in order to represent a more real behavior. Therefore, a complex network can be constructed by the interconnection of the agents, in continuous time, and the Petri Nets, in a discrete-event behavior, becoming a hybrid and scalable system. In order to analyze the performance of the approach, a real set of streets and intersections in Montevideo City is studied. Also, the approach is compared with a simulation realized in the software TSIS-CORSIM, which contains real data of density of vehicles. The multi-agent system achieves comparable results, taking into account the differences in the level of details respect to TSIS-CORSIM. Thus, the results can represent the most important issues of vehicular traffic with less computational resources. Numéro de notice : A2021-536 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101662 Date de publication en ligne : 04/06/2021 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101662 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98001 [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 : Bures-sur-Yvette : Université Paris-Saclay 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] 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] trafic urbain [Termes IGN] véhicule électrique [Termes IGN] ville intelligente Index. décimale : THESE Thèses et HDR 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.hal.science/tel-03230845/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97968

Recueil des contributions, Colloque international Tous (im)mobiles, tous cartographes ? Approches cartographiques des mobilités, des circulations, des flux et des déplacements : Méthodes, outils, représentations, pratiques et usages / Françoise Bahoken (2021)  

Permalink

Unfolding spatial-temporal patterns of taxi trip based on an improved network kernel density estimation / Boxi Shen in ISPRS International journal of geo-information, vol 9 n° 11 (November 2020)  

Permalink

Network-constrained bivariate clustering method for detecting urban black holes and volcanoes / Qiliang Liu in International journal of geographical information science IJGIS, vol 34 n° 10 (October 2020)  

Permalink

An empirical study on the intra-urban goods movement patterns using logistics big data / Pengxiang Zhao in International journal of geographical information science IJGIS, vol 34 n° 6 (June 2020)  

Permalink

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)  

Permalink

Création d’un réseau routier Sapeurs-Pompiers / Maud Desjardins (2018) 

Permalink

Prediction of traffic counts using statistical and neural network models / Abul Kalam Azad in Geomatica, vol 69 n° 3 (september 2015)  

Permalink

Transaction-based intelligent transportation system (TBITS) using stochastic user utility model / J. Olusina in Transactions in GIS, vol 17 n° 1 (February 2013)  

Permalink

Street hierarchies: a minority of streets account for a majority of traffic flow / Bin Jiang in International journal of geographical information science IJGIS, vol 23 n° 7-8 (july 2009)  

Permalink

Faites un détour par Montréal / Françoise de Blomac in SIG la lettre, n° 65 (mars 2005)

Permalink