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This work presents an estimation strategy that provides a good localization and a perfect measurement of the distance run by a public transport vehicle. To this aim, the vehicle is equipped with a Global Positioning System (GPS), an odometer, to measure the vehicle velocity, and a gyrometer, to measure the yaw velocity.A model based hybrid procedure has been proposed, based on the GPS signal reliability, guaranteeing robustness for temporary loss of satellite signal. In particular the algorithm switches among a Kalman filter strategy, if the GPS is available, and an open loop model based estimation, if it is not available. The algorithm has been tested both in simulation and with experimental data furnished by Centro Ricerche FIAT.

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One of the objectives of the Atena project [1] was the definition of a methodology for the predictive evaluation of the environmental impact of different types of vehicles used in an urban scenario. The target is to obtain a methodology that allow the decision maker to verify in simulation the effects of possible measures like low enforcement to the access restrictions or vehicle fleet composition. The main obstacle is the realisation and the managing of real driving cycles in order to that overtake the limits derived from the utilisation of typical cycles (i.e. ECE o NEDC) or the simple consideration of average speed. The starting point is a digital representation of the urban network where all the roads are represented with one or more arcs and for all this arcs is available an estimation of the traffic variables like the vehicle flow [vehicles per hour] or the average speed [kph]. Every arc is described in terms of traffic parameters like the type of road (i.e. highway, district road). The results presented in this work will be the starting point for the definition of one methodology to describe the emissions of vehicles fleets in terms of traffic parameters.