Centre de documentation
scientifique

An improved optimization model for crowd evacuation considering individual exit choice preference / Fei Gao in Transactions in GIS, vol 26 n° 7 (November 2022)  

Public [article]  inTransactions in GIS > vol 26 n° 7 (November 2022) . - pp 2850 - 2873 Titre : An improved optimization model for crowd evacuation considering individual exit choice preference Type de document : Article/Communication Auteurs : Fei Gao, Auteur ; Zhiqiang Du, Auteur ; Martin Werner, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2850 - 2873 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] comportement [Termes IGN] événement [Termes IGN] gestion de crise [Termes IGN] optimisation (mathématiques) [Termes IGN] optimisation par essaim de particules [Termes IGN] planification [Termes IGN] secours d'urgence Résumé : (auteur) Guidance-assisted crowd evacuation is a process of combining individual exit choice behavior with managers'exit assignment control. The knowledge of individual exit choice preference is of great significance for optimizing global exit assignment planning. This study proposes an improved optimization model for crowd evacuation by integrating the individual-level exit choice preference analysis with system-level exit assignment optimization to represent more realistic crowd evacuation decisions. First, the impact factors of individual exit choice behavior are considered in a mixed logit model to predict the probability of each individual choosing each exit in specific situations. Second, a preference-based exit filtering strategy is designed to analyze the sensible alternative exits for individuals or groups in multi-scale evacuation cells. Finally, to pursue optimal exit assignment planning, a multi-objective particle swarm optimization algorithm and an improved social force model are adopted to simulate the process of crowd evacuation and evaluate the performance of the specific exit assignment plans. The case study of an outdoor multiple-exit scenario in Xi'an, China, indicates that the proposed model can help managers to understand the heterogeneity of individual evacuation behaviors. Furthermore, it will support more reliable and realistic evacuation decisions in real-life situations than conventional plans that typically implement the top-n strategy. Numéro de notice : A2022-833 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1111/tgis.12984 Date de publication en ligne : 04/09/2022 En ligne : https://doi.org/10.1111/tgis.12984 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102216 [article]

Grid pattern recognition in road networks using the C4.5 algorithm / Jing Tian in Cartography and Geographic Information Science, Vol 43 n° 3 (June 2016)  

Public [article]  inCartography and Geographic Information Science > Vol 43 n° 3 (June 2016) . - pp 266 - 282 Titre : Grid pattern recognition in road networks using the C4.5 algorithm Type de document : Article/Communication Auteurs : Jing Tian, Auteur ; Zihan Song, Auteur ; Fei Gao, Auteur ; Feng Zhao, Auteur Année de publication : 2016 Article en page(s) : pp 266 - 282 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] apprentissage dirigé [Termes IGN] classification dirigée [Termes IGN] exploration de données géographiques [Termes IGN] grille [Termes IGN] reconnaissance de formes [Termes IGN] réseau routier Résumé : (Auteur) Pattern recognition in road networks can be used for different applications, including spatiotemporal data mining, automated map generalization, data matching of different levels of detail, and other important research topics. Grid patterns are a common pattern type. This paper proposes and implements a method for grid pattern recognition based on the idea of mesh classification through a supervised learning process. To train the classifier, training datasets are selected from worldwide city samples with different cultural, historical, and geographical environments. Meshes are subsequently labeled as composing or noncomposing grids by participants in an experiment, and the mesh measures are defined while accounting for the mesh’s individual characteristics and spatial context. The classifier is generated using the C4.5 algorithm. The accuracy of the classifier is evaluated using Kappa statistics and the overall rate of correctness. The average Kappa value is approximately 0.74, which corresponds to a total accuracy of 87.5%. Additionally, the rationality of the classifier is evaluated in an interpretation step. Two other existing grid pattern recognition methods were also tested on the datasets, and comparison results indicate that our approach is effective in identifying grid patterns in road networks. Numéro de notice : A2016-167 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/15230406.2015.1062425 En ligne : https://doi.org/10.1080/15230406.2015.1062425 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80473 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
032-2016031	RAB	Revue	Centre de documentation	En réserve L003	Disponible