Abstract: Machine learning techniques may be applied to determining a mode of transportation for a trajectory of a sequence of user locations. The mode of transportation, such as walking, bicycling, riding in a car or bus, riding in a train, or other mode, may be determined by creating a training set of data, then using classification mechanisms to classify trajectories by mode of transport. The training set may be generated by tracking then verifying a user's transportation mode. In some cases, a user may manually input or verify their transportation mode, while in other cases, a user's transportation mode may be determined through other data sources.

Abstract: Mobility observations may be analyzed to create so-called mobility genes, which may be intermediate data forms from which various analyses may be performed. The mobility genes may include a trajectory gene, which may describe a trajectory through which a user may have travelled. The trajectory gene may be analyzed from raw location observations and processed into a form that may be more easily managed. The trajectory genes may be made available to third parties for analysis, and may represent a large number of location observations that may have been condensed, smoothed, and anonymized. By analyzing only trajectories, a third party may forego having to analyze huge numbers of individual observations, and may have valuable data from which to make decisions.

Abstract: A visit mobility gene may be generated from analyzing raw location observations and may be made available for further analysis. The visit mobility gene may include summarized statistics about a certain location or location type, and in some cases may include ingress and egress travel information for visitors. The visit mobility gene may be made available to third parties for further analysis, and may represent a concise, rich, and standardized dataset that may be generated from several sources of mobility data.

Abstract: A transportation network monitoring system may use handoff metadata from cellular telephone and other communication networks to monitor train movement, traffic density, and traffic movement within the transportation network. Many communication technologies have handoff protocols that change a mobile device's connection from one base station to another. In a transportation network, such as a subway, a subway train may have several hundreds of riders, each of which may have a mobile device. As the train travels along the track, the handoff characteristics of those mobile devices may be analyzed to determine several characteristics of the transportation network, including the real time presence and speed of the train, as well as estimating the number of passengers and even the number of available seats on the train.

Abstract: Estimating a location of a device at a particular point of time may incorporate one, two, or more different location data points. The location data points may be derived from communications networks, where there may be different mechanisms for determining location. As part of the location estimation, each cellular location in a cellular network may have a different error range associated with each cell, for example. The error range for each cell may be generated by collecting precise location data from Global Positioning System or other mechanism with high accuracy, and comparing that data to location data gathered from other sources. A database of error ranges for each cell and each location mechanism may be gathered and used to estimate the actual location of a device for a given time period.

Abstract: A knowledge model is derived from many different data sources, including activities of a person's mobile devices and various media consumption habits. A graph may be built having various nodes representing concepts from the data sources and edges representing relationships between them. From the graph, various inferences may be made that can provide insight that could not otherwise be obtained. The knowledge model may be deployed as several services, including rich geolocation services, recommendation services, and other services. The services may be accessed through an application programming interface, which may be a paid service with various payment options.

Abstract: A traffic routing and analysis system uses data from individual cellular or mobile devices to determine traffic density within a transportation network, such as subways, busses, roads, pedestrian walkways, or other networks. The system may use historical data derived from monitoring people's travel patterns, and may compare historical data to real time or near real time data to detect abnormalities. The system may be used for policy analysis, predicted commute times and route selection based on traffic patterns, as well as broadcast statistics that may be displayed to commuters. The system may be accessed through an application programming interface (API) for various applications, which may include applications that run on mobile devices, desktop or cloud based computers, or other devices.

Abstract: A system may generate abstracted graphs from a social relationship graph in response to a query. A query may identify a person for which permission has been obtains to collect their data. The abstracted graphs may include summary statistics for various relationships of the person. The relationships may include other persons, places, things, concepts, brands, or other object that may be present in a social relationship graph, and the relationships may be presented in an abstracted or summarized form. The abstracted form may preserve data that may be useful for the requestor, yet may prevent the requestor from receiving some raw data. When two or more people have given consent, the data relating to the consenting persons may be presented in a non-abstracted manner, while other data may be presented in an abstracted manner.

Abstract: A traffic routing and analysis system uses data from individual cellular or mobile devices to determine traffic density within a transportation network, such as subways, busses, roads, pedestrian walkways, or other networks. The system may use historical data derived from monitoring people's travel patterns, and may compare historical data to real time or near real time data to detect abnormalities. The system may be used for policy analysis, predicted commute times and route selection based on traffic patterns, as well as broadcast statistics that may be displayed to commuters. The system may be accessed through an application programming interface (API) for various applications, which may include applications that run on mobile devices, desktop or cloud based computers, or other devices.