Christian Stade-Schuldt

Provided herein is a method for a framework to predict the population density for an area based on indirect measurements and contextually similar areas. Methods may include: receiving ground truth population data corresponding to a first region; determining map features associated with the first region; receiving dynamic mobility data associated with the first region; training a machine learning model based on the ground truth population data corresponding to the first region, the map features… Mehr anzeigen

Provided herein is a method for a framework to predict the population density for an area based on indirect measurements and contextually similar areas. Methods may include: receiving ground truth population data corresponding to a first region; determining map features associated with the first region; receiving dynamic mobility data associated with the first region; training a machine learning model based on the ground truth population data corresponding to the first region, the map features associated with the first region, and the dynamic mobility data associated with the first region; receiving dynamic mobility data associated with a second region; determining map features associated with the second region; processing the dynamic mobility data associated with the second region and the map features associated with the second region using the machine learning model; and receiving, from the machine learning model, a population estimate for the second region. Weniger anzeigen

A method, system, and apparatus for estimation of a count of mobile devices include receiving one or more probe data points including a location and a session identifier, constructing a trajectory including the one or more probe data points having a same session identifier, constructing a trajectory including the one or more probe data points having a same session identifier and generated by one mobile device of the plurality of the mobile devices, dividing the trajectory area by an area of the… Mehr anzeigen

A method, system, and apparatus for estimation of a count of mobile devices include receiving one or more probe data points including a location and a session identifier, constructing a trajectory including the one or more probe data points having a same session identifier, constructing a trajectory including the one or more probe data points having a same session identifier and generated by one mobile device of the plurality of the mobile devices, dividing the trajectory area by an area of the observation area, and estimating the count of mobile devices in the observation area from at least the normalized trajectory area. A path of the trajectory is based on the location of the one or more probe data points expanding the path of the trajectory by a radius where a result of the expanding is a trajectory area. A result of the dividing is a first normalized trajectory area.
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An approach is provided for computing a three-dimensional route (e.g., a flight path) based on risk-related data. The approach, for example, involves receiving an input specifying an origin, a destination, or a combination thereof for generating a three-dimensional route at a specified time. The approach also involves retrieving risk-related data for one or more candidate three-dimensional routes based on the origin, the destination, or a combination thereof. The risk-related data, for… Mehr anzeigen

An approach is provided for computing a three-dimensional route (e.g., a flight path) based on risk-related data. The approach, for example, involves receiving an input specifying an origin, a destination, or a combination thereof for generating a three-dimensional route at a specified time. The approach also involves retrieving risk-related data for one or more candidate three-dimensional routes based on the origin, the destination, or a combination thereof. The risk-related data, for instance, indicates an occurrence of a safety risk to a vehicle (e.g., aerial vehicle) traveling the one or more candidate three-dimensional routes at the specified time. The approach further involves determining the three-dimensional route from the one or more candidate three-dimensional routes based on computing that the risk-related data for the determined three-dimensional route meets a risk threshold. Weniger anzeigen

An approach is provided for visualizing risk levels associated with aerial vehicle flights. The approach, for example, involves determining a flight path for an aerial vehicle. The approach also involves calculating at least one risk area along the flight path based on risk-related data associated with the flight path (e.g., data on population density, electromagnetic fields, absence of location signals such as Global Positioning System (GPS) signals, weather, network coverage, aviation related… Mehr anzeigen

An approach is provided for visualizing risk levels associated with aerial vehicle flights. The approach, for example, involves determining a flight path for an aerial vehicle. The approach also involves calculating at least one risk area along the flight path based on risk-related data associated with the flight path (e.g., data on population density, electromagnetic fields, absence of location signals such as Global Positioning System (GPS) signals, weather, network coverage, aviation related data, etc.). The approach further involves generating a representation of the at least one risk area as a virtual obstacle object and rendering the virtual obstacle object in a user interface of a device in relation to the flight path. Weniger anzeigen