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Understanding the movement predictability of international travelers using a nationwide mobile phone dataset collected in South Korea / Yang Xu in Computers, Environment and Urban Systems, vol 92 (March 2022)  

Public [article]  inComputers, Environment and Urban Systems > vol 92 (March 2022) Titre : Understanding the movement predictability of international travelers using a nationwide mobile phone dataset collected in South Korea Type de document : Article/Communication Auteurs : Yang Xu, Auteur ; Dan Zou, Auteur ; Sangwon Park, Auteur ; et al., Auteur Année de publication : 2022 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] chaîne de Markov [Termes IGN] classification par réseau neuronal récurrent [Termes IGN] Corée du sud [Termes IGN] durée de trajet [Termes IGN] mobilité humaine [Termes IGN] modèle de simulation [Termes IGN] prévision à court terme [Termes IGN] téléphone intelligent [Termes IGN] téléphonie mobile [Termes IGN] tourisme [Termes IGN] voyage Résumé : (auteur) The abilities to predict tourist movements are critical to many urban applications, such as travel recommendations, targeted advertising, and infrastructure planning. Despite its importance, our understanding on the movement predictability of urban tourists and visitors is still limited, partially due to difficulties in accessing large scale mobility observations. In this study, we aim to bridge this gap by analyzing a nationwide mobile phone dataset. The dataset captures movement traces of a large number of international travelers who visited South Korea in 2018. By introducing two prediction models, one being Markov chain and the other with a recurrent neural network architecture, we assess how well travelers’ movements can be predicted under different model settings, and examine how predictability relates to travelers’ length of stay and activeness in travel patterns. Since travelers’ destination choices are quite diverse in South Korea, this enables us to further investigate the geographic variation of the models’ performance. Results show that the Markov chain model achieves an overall accuracy between 33.4% (@Acc1 metric) and 64.2% (@Acc5 metric), compared to 41.9% (@Acc1) and 67.7% (@Acc5) for the recurrent neural network model. The prediction capabilities of both models are largely unequal across individuals, with active travelers being more predictable in general. There is a notable geographic variation in the models’ performance, meaning that travelers’ movements are more predictable in some cities, but less in others. We believe this study represents a new effort in portraying the movement predictability of urban tourists and visitors. The analytical framework can be applied to assist tourism planning and service deployment in cities. Numéro de notice : A2022-085 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101753 Date de publication en ligne : 06/01/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101753 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99490 [article]

Pedestrian trajectory prediction with convolutional neural networks / Simone Zamboni in Pattern recognition, vol 121 (January 2022)  

Public [article]  inPattern recognition > vol 121 (January 2022) . - n° 108252 Titre : Pedestrian trajectory prediction with convolutional neural networks Type de document : Article/Communication Auteurs : Simone Zamboni, Auteur ; Zekarias Tilahun Kefato, Auteur ; Sarunas Girdzijauskas, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 108252 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] classification par réseau neuronal récurrent [Termes IGN] distance euclidienne [Termes IGN] filtre de Gauss [Termes IGN] itinéraire piétionnier [Termes IGN] modèle de simulation [Termes IGN] navigation pédestre [Termes IGN] piéton [Termes IGN] prévision à court terme [Termes IGN] réseau social [Termes IGN] trajet (mobilité) Résumé : (auteur) Predicting the future trajectories of pedestrians is a challenging problem that has a range of application, from crowd surveillance to autonomous driving. In literature, methods to approach pedestrian trajectory prediction have evolved, transitioning from physics-based models to data-driven models based on recurrent neural networks. In this work, we propose a new approach to pedestrian trajectory prediction, with the introduction of a novel 2D convolutional model. This new model outperforms recurrent models, and it achieves state-of-the-art results on the ETH and TrajNet datasets. We also present an effective system to represent pedestrian positions and powerful data augmentation techniques, such as the addition of Gaussian noise and the use of random rotations, which can be applied to any model. As an additional exploratory analysis, we present experimental results on the inclusion of occupancy methods to model social information, which empirically show that these methods are ineffective in capturing social interaction. Numéro de notice : A2022-109 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.patcog.2021.108252 Date de publication en ligne : 13/08/2021 En ligne : https://doi.org/10.1016/j.patcog.2021.108252 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99615 [article]

Ultra short-term prediction of pole coordinates via combination of empirical mode decomposition and neural networks / Yu Lei in Artificial satellites, vol 51 n° 4 (December 2016)  

Public [article]  inArtificial satellites > vol 51 n° 4 (December 2016) . - pp 149 – 161 Titre : Ultra short-term prediction of pole coordinates via combination of empirical mode decomposition and neural networks Type de document : Article/Communication Auteurs : Yu Lei, Auteur ; Danning Zhao, Auteur ; Hongbing Cai, Auteur Année de publication : 2016 Article en page(s) : pp 149 – 161 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique [Termes IGN] filtre passe-bas [Termes IGN] fonction de base radiale [Termes IGN] mouvement du pôle [Termes IGN] oscillation [Termes IGN] prévision à court terme [Termes IGN] réseau neuronal artificiel [Termes IGN] terme de Chandler Résumé : (auteur) It was shown in the previous study that the increase of pole coordinates prediction error for about 100 days in the future is mostly caused by irregular short period oscillations. In this paper, the ultra short-term prediction of pole coordinates is studied for 10 days in the future by means of combination of empirical mode decomposition (EMD) and neural networks (NN), denoted EMD-NN. In the algorithm, EMD is employed as a low pass filter for eliminating high frequency signals from observed pole coordinates data. Then the annual and Chandler wobbles are removed a priori from pole coordinates data with high frequency signals eliminated. Finally, the radial basis function (RBF) networks are used to model and predict the residuals. The prediction performance of the EMD-NN approach is compared with that of the NN-only solution and the prediction methods and techniques involved in the Earth orientation parameters prediction comparison campaign (EOP PCC). The results show that the prediction accuracy of the EMD-NN algorithm is better than that of the NN-only solution and is also comparable with that of the other existing prediction method and techniques. Numéro de notice : A2016-977 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1515/arsa-2016-0013 En ligne : https://doi.org/10.1515/arsa-2016-0013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83688 [article]

Using remote sensing data to develop seasonal outlooks for Arctic regional sea-ice minimum extent / S. Drobot in Remote sensing of environment, vol 111 n° 2-3 (30 November 2007)  

Public [article]  inRemote sensing of environment > vol 111 n° 2-3 (30 November 2007) . - pp 136 - 147 Titre : Using remote sensing data to develop seasonal outlooks for Arctic regional sea-ice minimum extent Type de document : Article/Communication Auteurs : S. Drobot, Auteur Année de publication : 2007 Article en page(s) : pp 136 - 147 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Arctique [Termes IGN] Canada [Termes IGN] épaisseur de la glace [Termes IGN] glace de mer [Termes IGN] prévision à court terme [Termes IGN] régression linéaire [Termes IGN] variation saisonnière Résumé : (Auteur) This paper discusses the development of simple multiple linear regression (MLR) models for developing seasonal forecasts of the annual minimum sea-ice extent in the Beaufort/Chukchi Seas, the Laptev/East Siberian Seas, the Kara/Barents Seas, and the Canadian Arctic Archipelago regions. The potential predictor data are based on mean monthly weighted indices of sea-ice concentration, multiyear sea-ice concentration, surface skin temperature, surface albedo, and downwelling longwave radiation flux at the surface. Predictions are developed based on data available in March (spring forecast), to coincide with the National American Ice Service's annual outlooks, and based on data available in June (summer forecast), which would provide a seasonal revision. The final regression equations retain one to three predictors, and each of the MLR models is superior to climatology. The r2 for the MLR models range from a low of 0.44 (for the spring forecast in the Canadian Arctic Archipelago) to a high of 0.80 (for the summer forecast in the Beaufort/Chukchi Seas). Copyright Elsevier Numéro de notice : A2007-486 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2007.03.024 En ligne : https://doi.org/10.1016/j.rse.2007.03.024 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28849 [article]

Real-time monitoring and short-term forecasting of land surface phenology / M.A. White in Remote sensing of environment, vol 104 n° 1 (15/09/2006)  

Public [article]  inRemote sensing of environment > vol 104 n° 1 (15/09/2006) . - pp 43 - 49 Titre : Real-time monitoring and short-term forecasting of land surface phenology Type de document : Article/Communication Auteurs : M.A. White, Auteur ; Ramakrishna R. Nemani, Auteur Année de publication : 2006 Article en page(s) : pp 43 - 49 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse diachronique [Termes IGN] phénologie [Termes IGN] prédiction [Termes IGN] prévision à court terme [Termes IGN] seuillage d'image [Termes IGN] surface du sol [Termes IGN] surveillance écologique [Termes IGN] temps réel Résumé : (Auteur) Land surface phenology is an important process for real-time monitoring and short-term forecasting in diverse land management, health, and hydrologic modeling applications. Yet current efforts to characterize phenological processes are limited by remote sensing challenges and lack of uncertainty estimates. Here, for a global distribution of phenologically and climatically similar phenoregions, we used the Advanced Very High Resolution Radiometer to develop a conceptually and computationally simple technique for real-time and forecast applications. Our overall approach was to analyze the phenological behavior of groups of pixels without recourse to smoothing or fitting. We used a 3-step initial process: (1) define a phenoregion specific normalized difference vegetation index threshold; (2) for all days from 1982–2003, calculate the percent of pixels above the threshold (PAT); (3) calculate daily 1982–2003 empirical distributions of PAT. For real-time monitoring, the current PAT may then be compared to the historical range of variability and visualized in relation to user-defined levels. Using similar concepts, we projected daily PAT up to one month in the future and compared predicted and actual dates at which a hypothetical PAT was reached. We found that the maximum lead-time of phenological forecasts could be analytically defined for user-specified uncertainty levels. The approach is adaptable to different remote sensing technologies and provides a foundation for ascribing a sequence of ground conditions (e.g. snowmelt, vegetative growth, pollen production, insect phenology) to remotely sensed land surface phenology observations. Copyright Elsevier Numéro de notice : A2006-393 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2006.04.014 En ligne : https://doi.org/10.1016/j.rse.2006.04.014 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28117 [article]