Abstract: There is provided a method for generating traffic data indicative of a traffic volume and/or traffic density within a navigable network in an area covered by an electronic map, the electronic map comprising a plurality of segments representing navigable elements of the navigable network. The method generally comprises obtaining positional data relating to the movement of a plurality of devices along the navigable elements represented by the segments of the electronic map; determining, using (at least) positional data relating to one or more congested segments within a region of the navigable network, an estimate of one or more average penetration rates for the region; determining a sample volume for a non-congested segment within the region based on the obtained positional data; and estimating, using the determined sample volume and a selected one of the average penetration rates for the region, a traffic volume and/or traffic density for the non-congested segment.

Abstract: There is provided a method for generating traffic data indicative of a traffic volume and/or traffic density within a navigable network in an area covered by an electronic map, the electronic map comprising a plurality of segments representing navigable elements of the navigable network. The method generally comprises obtaining positional data relating to the movement of a plurality of devices along the navigable elements represented by the segments of the electronic map; determining, using (at least) positional data relating to one or more congested segments within a region of the navigable network, an estimate of one or more average penetration rates for the region; determining a sample volume for a non-congested segment within the region based on the obtained positional data; and estimating, using the determined sample volume and a selected one of the average penetration rates for the region, a traffic volume and/or traffic density for the non-congested segment.

Abstract: A method of estimating the geographical location of an object from location information reported by one or more mobile devices having location determining means comprises collecting location data from the one or more mobile devices, preparing the data by selecting features of the data, training an estimator by inputting selected features of the data, and applying the estimator to a map grid to estimate the location of the object. During training of the estimator a kernel machine operates on histograms of distances that are computed with respect to a considered location and the user reports nearby. During application of the estimator, if the estimator assigns a score above a certain threshold a location is marked as the true location of the object. In case multiple locations are predicted, a subsequent clustering merges multiple predictions into one by using the weighted mean.

Abstract: A method of estimating the geographical location of an object from location information reported by one or more mobile devices having location determining means comprises collecting location data from the one or more mobile devices, preparing the data by selecting features of the data, training an estimator by inputting selected features of the data, and applying the estimator to a map grid to estimate the location of the object. During training of the estimator a kernel machine operates on histograms of distances that are computed with respect to a considered location and the user reports nearby. During application of the estimator, if the estimator assigns a score above a certain threshold a location is marked as the true location of the object. In case multiple locations are predicted, a subsequent clustering merges multiple predictions into one by using the weighted mean.