Road User Charging (RUC) is designed to reduce congestion and collect revenue for the maintenance of transportation infrastructure. In order to determine the charges, it is important that appropriate Road User Charging Indicators (RUCI) are defined. This paper focusses on Variable Road User Charging (VRUC) as the more dynamic and flexible compared to Fixed Road User Charging (FRUC), and thus is a better reflection of the utility of the road space. The main issues associated with VRUC are the definition of appropriate charging indicators and their measurement. This paper addresses the former by proposing a number of new charging indicators, considering the equalization of the charges and marginal social cost imposed on others. The measurement of the indicators is addressed by a novel data fusion algorithm for the determination of the vehicle state based on the integration of Global Navigation Satellite Systems (GNSS) with Dead Reckoning (DR) and road segment information. Statistical analyses are presented in terms of the Required Navigation Performance (RNP) parameters of accuracy, integrity, continuity and availability, based on simulation and field tests. It is shown that the proposed fusion model is superior to positioning with GPS only, and GPS plus GLONASS, in terms of all the RNP parameters with a significant improvement in availability

Global navigation satellite system is indispensable to provide positioning, navigation, and timing information for pedestrians and vehicles in location-based services. However, tree canopies, although considered as valuable city infrastructures in urban areas, adversely degrade the accuracy of global navigation satellite system positioning as they attenuate the satellite signals. This article proposes a bagging tree-based global navigation satellite system pseudorange error prediction algorithm, by considering two variables, including carrier to noise C/ N0 and elevation angle θe to improve the global navigation satellite system positioning accuracy in the foliage area. The positioning accuracy improvement is then obtained by applying the predicted pseudorange error corrections. The experimental results shows that as the stationary character of the geostationary orbit satellites, the improvement of the prediction accuracy of the BeiDou navigation satellite system solution (85.42% in light foliage and 83.99% in heavy foliage) is much higher than that of the global positioning system solution (70.77% in light foliage and 73.61% in heavy foliage). The positioning error values in east, north, and up coordinates are improved by the proposed algorithm, especially a significant decrease in up direction. Moreover, the improvement rate of the three-dimensional root mean square error of positioning accuracy in light foliage area test is 86% for BeiDou navigation satellite system/global positioning system combination solutions, while the corresponding improvement rate is 82% for the heavy foliage area test.