Method for determining a vehicle flow on at least one section of a road network under consideration, by means of at least one fixed traffic measurement sensor arranged at at least one measurement point arranged on a training road network, characterized in that the following steps are implemented: a. a vehicle flow is measured (CAPa) at said at least one measurement point by means of said at least one fixed traffic measurement sensor; b. time-stamped geolocation data (GPSa) on journeys made within said training road network are acquired; c. a first training database is constructed on the basis of said time-stamped geolocation data (GPSa) on journeys made within the training road network, macroscopic data (MACa) on said training road network, and a classification (CLUa) of said journeys made within said training road network, said classification comprising at least one class, each class being composed of journeys made having similar behaviours in terms of measured flows, said macroscopic data being chosen from the topology, the type of road section, the number of lanes of the road section, the maximum speed of the road section, the traffic signals of the road section, the slope of the road section, and the length of the road section; d. a classifier is constructed by means of a first machine learning method trained on said constructed first training database, said classifier associating at least one class of said classification with at least one section of said road network depending on said macroscopic data on said section of said road network, time parameters relating to the periods of the journeys made, and said geolocation data on journeys made within said road network; e. a second training database is constructed on the basis of said vehicle flow measurements (CAPa), said time-stamped geolocation data (GPSa) on journeys made within said training road network, and said classification (CLUa) of said journeys made within said training road network; f. a flow model is constructed, for each class of said classification, by means of a second machine learning method trained on said constructed second training database, each flow model associating a vehicle flow for the class under consideration depending on said geolocation data on journeys made; g. time-stamped geolocation data (GPSb) on journeys made within said road network under consideration are acquired; h. at least one section of the road network under consideration is classified by means of said classifier, said time-stamped geolocation data (GPSb), macroscopic data (MACb) on said section of the road network under consideration, and time parameters; and i. the flow of said at least one section of the road network under consideration is determined by means of said flow model, said determined class of said at least one section, and said time-stamped geolocation data (MACb). a. a vehicle flow is measured (CAPa) at said at least one measurement point by means of said at least one fixed traffic measurement sensor; b. time-stamped geolocation data (GPSa) on journeys made within said training road network are acquired; c. a first training database is constructed on the basis of said time-stamped geolocation data (GPSa) on journeys made within the training road network, macroscopic data (MACa) on said training road network, and a classification (CLUa) of said journeys made within said training road network, said classification comprising at least one class, each class being composed of journeys made having similar behaviours in terms of measured flows, said macroscopic data being chosen from the topology, the type of road section, the number of lanes of the road section, the maximum speed of the road section, the traffic signals of the road section, the slope of the road section, and the length of the road section; d. a classifier is constructed by means of a first machine learning method trained on said constructed first training database, said classifier associating at least one class of said classification with at least one section of said road network depending on said macroscopic data on said section of said road network, time parameters relating to the periods of the journeys made, and said geolocation data on journeys made within said road network; e. a second training database is constructed on the basis of said vehicle flow measurements (CAPa), said time-stamped geolocation data (GPSa) on journeys made within said training road network, and said classification (CLUa) of said journeys made within said training road network; f. a flow model is constructed, for each class of said classification, by means of a second machine learning method trained on said constructed second training database, each flow model associating a vehicle flow for the class under consideration depending on said geolocation data on journeys made; g. time-stamped geolocation data (GPSb) on journeys made within said road network under consideration are acquired; h. at least one section of the road network under consideration is classified by means of said classifier, said time-stamped geolocation data (GPSb), macroscopic data (MACb) on said section of the road network under consideration, and time parameters; and i. the flow of said at least one section of the road network under consideration is determined by means of said flow model, said determined class of said at least one section, and said time-stamped geolocation data (MACb).