Abstract: The present invention is a method for determining the amount of pollutant emissions (Q) from at least one vehicle over a road network section, wherein a pollutant emission model (MFE) is constructed by machine learning (APP) using macroscopic data (MAC) of a learning road network and traffic data (TRA). This model (MFE) is then applied to a road network section.

Abstract: The invention relates to a method of predicting at least one vehicle speed profile for a road network portion (POR), wherein a vehicle speed model (MOD) is constructed by use of macroscopic road network data (MAC) and of travelled route data (DTR), and then this model is applied to the road network portion (POR) being considered.

Abstract: The present invention is a method for determining an optimal speed of a first vehicle preceded by a second vehicle. Position, speed and acceleration of the second vehicle are measured in order to determine a trajectory thereof, and a dynamic model of the first vehicle is constructed. The optimal speed is then determined by minimizing the energy consumption of the vehicle by use of the dynamic model by minimization being constrained by the trajectory of the second vehicle.

Abstract: The present invention relates to a method of determining a route (ITI) minimizing the energy consumption of a vehicle, based on the use of a dynamic model (MOD) of the vehicle depending on the vehicle speed and acceleration, the construction of a line graph (GA) and a shortest path algorithm (ALG) suited for negative energies.

Abstract: The present invention relates to a method of determining physical parameters (Phy) relative to pollutant and/or noise emissions and/or road safety of a vehicle fleet (P1) on a road portion. The method comprises the following steps: a) measuring (MES) the positions (posGPS), speeds (vGPS) and altitudes (altGPS) of vehicles on the road portion so as to determine (DET1) a speed profile (pv), b) determining (DET2) at least one physical characteristic (Tab) on the road portion for each of the fleet vehicles, according to the characteristics (PAR) of these vehicles and to the speed profile (pv) determined in step a), c) applying (APP) the fleet to the physical characteristics determined in the previous step to obtain a distribution (Rep) of the physical characteristics on the fleet, d) determining (DET3) physical parameter (Phy) on the part of the road network portion by means of distribution (Rep) of the physical characteristics obtained in step c).

Abstract: The present invention relates to a method of determining at least one area (ZON) of a road network reachable by a vehicle driving on the road network. The method is based on the use of a dynamic model (MOD) of the vehicle depending on the vehicle speed and acceleration, the construction of a line graph (GA) and a shortest path algorithm (ALG).

Abstract: The present invention relates to a method for determining an optimal speed to be reached for a first vehicle preceded by a second vehicle. For this method, the position, the speed and the acceleration of the second vehicle are measured (MES) in order to determine the trajectory thereof (POS), and a dynamic model (MOD) of the first vehicle is constructed. The speed to be reached (VIT) is then determined by minimizing (MIN) the energy consumption of the vehicle by means of the dynamic model (MOD), the minimization being constrained by the trajectory (POS) of the second vehicle.

Abstract: The present invention relates to a method of determining at least one area (ZON) of a road network reachable by a vehicle driving on the road network. The method is based on the use of a dynamic model (MOD) of the vehicle depending on the vehicle speed and acceleration, the construction of a line graph (GA) and a shortest path algorithm (ALG).

Abstract: The present invention relates to a method of determining a route (ITI) minimizing the energy consumption of a vehicle, based on the use of a dynamic model (MOD) of the vehicle depending on the vehicle speed and acceleration, the construction of a line graph (GA) and a shortest path algorithm (ALG) suited for negative energies.