This paper is dedicated to erroneous data detection and imputation methods in surveys. We describe experiments conducted under the scope of a European project for studying new statistical methods based on neural networks. We show that the selforganising map can be used successfully for these tasks. A self-organising map is calibrated according to the available observations, described through a set of correlated variables handled together. The map can then be used both to detect erroneous data and to impute values to partial observations. We apply these principles to a real size transport survey database. We show that the performance of our imputation model compares well to other classical methods, and that the use of a self-organising map for data correction provides a performing system for data validation, data correction and data analysis.
This paper introduces an online pedestrian crossing detection system that uses pre-existing traffic-oriented video-sensors which, at regular intervals, provide coarse spatial measurements on areas along a crosswalk. Pedestrian crossing detection is based on the recognition of occupancy patterns induced by pedestrians when they move on the crosswalk. In order to improve the ability of non-dedicated sensors to detect pedestrians, we introduce an evidential-based data fusion process that exploits redundant information coming from one or two sensors: intra-sensor fusion uses spatiotemporal characteristics of the measurements, and inter-sensor fusion uses redundancy between the two sensors. As part of the EU funded TRACKSS project on co- http://www.sciencedirect.com/science/journal/0968090X patterns obtained and leads to high detection rates of pedestrian crossings with multi-purpose sensors in operational conditions, especially when a secondary sensor is available.
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