A method for extracting travel patterns using data polishing

With recent developments in ICT, the interest in using large amounts of accumulated data for traffic policy planning has increased significantly. In recent years, data polishing has been proposed as a new method of big data analysis. Data polishing is a graphical clustering method, which can be used to extract patterns that are similar or related to each other by identifying the cluster structures present in the data. The purpose of this study is to identify the travel patterns of railway passengers by applying data polishing to smart card data collected in the Kagawa Prefecture, Japan. To this end, we consider 9,008,709 data points collected over a period of 15 months, ranging from December 1st, 2013 to February 28th, 2015. This dataset includes various types of information, including trip histories and types of passengers. This study implements data polishing to cluster 4,667,520 combinations of information regarding individual rides in terms of the day of the week, the time of the day, passenger types, and origin and destination stations. Via the analysis, 127 characteristic travel patterns are identified in aggregate

Variational Bayesian Inference for Source Separation and Robust Feature Extraction

International audienceWe consider the task of separating and classifying individual sound sources mixed together. The main challenge is to achieve robust classification despite residual distortion of the separated source signals. A promising paradigm is to estimate the uncertainty about the separated source signals and to propagate it through the subsequent feature extraction and classification stages. We argue that variational Bayesian (VB) inference offers a mathematically rigorous way of deriving uncertainty estimators, which contrasts with state-of-the-art estimators based on heuristics or on maximum likelihood (ML) estimation. We propose a general VB source separation algorithm, which makes it possible to jointly exploit spatial and spectral models of the sources. This algorithm achieves 6% and 5% relative error reduction compared to ML uncertainty estimation on the CHiME noise-robust speaker identification and speech recognition benchmarks, respectively, and it opens the way for more complex VB approximations of uncertainty.Dans cet article, nous considérons le problème de l'extraction des descripteurs de chaque source dans un enregistrement audio multi-sources à l'aide d'un algorithme général de séparation de sources. La difficulté consiste à estimer l'incertitude sur les sources et à la propager aux descripteurs, afin de les estimer de façon robuste en dépit des erreurs de séparation. Les méthodes de l'état de l'art estiment l'incertitude de façon heuristique, tandis que nous proposons d'intégrer sur les paramètres de l'algorithme de séparation de sources. Nous décrivons dans ce but une méthode d'inférence variationnelle bayésienne pour l'estimation de la distribution a posteriori des sources et nous calculons ensuite l'espérance des descripteurs par propagation de l'incertitude selon la méthode d'identification des moments. Nous évaluons la précision des descripteurs en terme d'erreur quadratique moyenne et conduisons des expériences de reconnaissance du locuteur afin d'observer la performance qui en découle pour un problème réel. Dans les deux cas, la méthode proposée donne les meilleurs résultats