A method for computing street parking occupancy for a street segment, wherein the street segment comprises a part of a street, a whole street, or a plurality of streets, the method comprising a. Compute, with a processing component, a baseline occupancy module for the street segment using at least map data and at least image data, wherein the map data comprise data relating to a specific layout of the street segment, wherein the image data comprises aerial images and/or ground-based images and wherein: if the image data used comprises ground-based images, the street segment refers to the visible part of one street and if aerial images are used the street segment refers to a plurality of streets or streets segment visible from above; and b. Compute, with the processing component, a continuous time occupancy module for the street segment using at least the baseline occupancy and at least historical data, wherein the historical data comprises at least one of historical movement data and historical event data; and c. Compute, with the processing component, a forecast occupancy module for the street segment using the continuous time occupancy module and at least real-time data, wherein the real-time data used to compute the forecast occupancy module comprises at least one of real-time movement data and real-time event data; wherein the baseline occupancy module computation comprises using map data to compute a center line module, and wherein the center line module outputs at least georeferenced oriented features corresponding to the middle of the street segment; wherein the output of the center line module is used to distinguish between moving cars and parked cars based on their distance from the center line of the street segment. a. Compute, with a processing component, a baseline occupancy module for the street segment using at least map data and at least image data, wherein the map data comprise data relating to a specific layout of the street segment, wherein the image data comprises aerial images and/or ground-based images and wherein: if the image data used comprises ground-based images, the street segment refers to the visible part of one street and if aerial images are used the street segment refers to a plurality of streets or streets segment visible from above; and wherein the map data comprise data relating to a specific layout of the street segment, wherein the image data comprises aerial images and/or ground-based images and wherein: if the image data used comprises ground-based images, the street segment refers to the visible part of one street and if aerial images are used the street segment refers to a plurality of streets or streets segment visible from above; and if the image data used comprises ground-based images, the street segment refers to the visible part of one street and if aerial images are used the street segment refers to a plurality of streets or streets segment visible from above; and b. Compute, with the processing component, a continuous time occupancy module for the street segment using at least the baseline occupancy and at least historical data, wherein the historical data comprises at least one of historical movement data and historical event data; and c. Compute, with the processing component, a forecast occupancy module for the street segment using the continuous time occupancy module and at least real-time data, wherein the real-time data used to compute the forecast occupancy module comprises at least one of real-time movement data and real-time event data; wherein the baseline occupancy module computation comprises using map data to compute a center line module, and wherein the center line module outputs at least georeferenced oriented features corresponding to the middle of the street segment; wherein the output of the center line module is used to distinguish between moving cars and parked cars based on their distance from the center line of the street segment.