A method for determining a route may include: obtaining input data from a user, the input data including an indication of an origin and a destination for a trip in a road network; obtaining real-time traffic data from a plurality of probe vehicles spatially distributed across the road network; and obtaining a past traveled route from the plurality of probe vehicles, the past traveled route indicating a sequence of road segments traveled in the trip. The method may further include determining, using a first neural network, a representation of the past traveled route; determining, using a second neural network, a representation of the real-time traffic data; determining, using an adjoint generative process, a representation of the destination; and determining a next road segment for the trip based on the representation of the past traveled route, the representation of the real-time traffic data, and the representation of the destination.
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.
H04W 4/02 - Services utilisant des informations de localisation
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
H04W 4/42 - Services spécialement adaptés à des environnements, à des situations ou à des fins spécifiques pour les véhicules, p. ex. communication véhicule-piétons pour les véhicules de transport collectif, p. ex. les autobus, les trains ou les aéronefs
H04W 4/029 - Services de gestion ou de suivi basés sur la localisation
G01C 21/00 - NavigationInstruments de navigation non prévus dans les groupes
G01C 21/34 - Recherche d'itinéraireGuidage en matière d'itinéraire
3.
Error factor and uniqueness level for anonymized datasets
A dataset's uniqueness level may be calculated by analyzing a dataset to determine a uniqueness level. In cases where the uniqueness level may be too low for a particular purpose, meaning when the dataset may not provide enough anonymity, the dataset may be adjusted by recomputing the dataset with different resolutions of spatial data, temporal data, content data, and relationship data. By adjusting the resolution or accuracy of the data elements, the uniqueness level may thereby be adjusted. An error calculation may be determined by comparing the adjusted dataset to the original data, and the error value may represent the consistency of the data to the original data. The uniqueness level may be used as an assurance level of anonymity, which may be advertised when a dataset is sold or transferred to a third party for analysis.
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.
H04W 4/029 - Services de gestion ou de suivi basés sur la localisation
H04W 4/80 - Services utilisant la communication de courte portée, p. ex. la communication en champ proche, l'identification par radiofréquence ou la communication à faible consommation d’énergie
G06F 16/29 - Bases de données d’informations géographiques
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
H04W 4/02 - Services utilisant des informations de localisation
G01S 5/02 - Localisation par coordination de plusieurs déterminations de direction ou de ligne de positionLocalisation par coordination de plusieurs déterminations de distance utilisant les ondes radioélectriques
G01S 5/08 - Position d'un radiogoniomètre unique obtenue par détermination de la direction de plusieurs sources espacées d'emplacement connu
G01S 5/10 - Position du récepteur obtenue par coordination de plusieurs lignes de position définies par des mesures de différence de parcours
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.
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.
H04W 4/029 - Services de gestion ou de suivi basés sur la localisation
H04W 4/80 - Services utilisant la communication de courte portée, p. ex. la communication en champ proche, l'identification par radiofréquence ou la communication à faible consommation d’énergie
G06F 16/29 - Bases de données d’informations géographiques
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
H04W 4/02 - Services utilisant des informations de localisation
G01S 5/02 - Localisation par coordination de plusieurs déterminations de direction ou de ligne de positionLocalisation par coordination de plusieurs déterminations de distance utilisant les ondes radioélectriques
G01S 5/08 - Position d'un radiogoniomètre unique obtenue par détermination de la direction de plusieurs sources espacées d'emplacement connu
G01S 5/10 - Position du récepteur obtenue par coordination de plusieurs lignes de position définies par des mesures de différence de parcours
Real time status of a device's movements may be determined from a sequence of location observations. The status may be in the form of a state, which may be “stay”, “transit”, “pause”, and “unknown”. A state transition may occur from transit to stay when the device has remained within a predefined radius for a predefined time period. Prior to being labeled a “stay”, a device that may have ceased moving but has not stayed at that location for enough time may be labeled “pause”. For those devices in a “transit” state, a mode of transport may be determined. The real time analysis system may be a low-overhead mechanism by which new location observations may be received and processed. The resulting data may be used by traffic analysts to monitor congestion, for real time traffic data for commuters, and other uses.
H04W 4/02 - Services utilisant des informations de localisation
H04W 4/029 - Services de gestion ou de suivi basés sur la localisation
H04W 4/80 - Services utilisant la communication de courte portée, p. ex. la communication en champ proche, l'identification par radiofréquence ou la communication à faible consommation d’énergie
G06F 16/29 - Bases de données d’informations géographiques
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
G01S 5/02 - Localisation par coordination de plusieurs déterminations de direction ou de ligne de positionLocalisation par coordination de plusieurs déterminations de distance utilisant les ondes radioélectriques
G01S 5/08 - Position d'un radiogoniomètre unique obtenue par détermination de la direction de plusieurs sources espacées d'emplacement connu
G01S 5/10 - Position du récepteur obtenue par coordination de plusieurs lignes de position définies par des mesures de différence de parcours
Daily activities of mobile data may be processed as images. The image processing techniques may include classifying, pattern matching, and other automated analyses. Even when the images contain such highly condensed and summarized versions of the underlying raw data, very meaningful classification, pattern matching, and other analyses may be performed quickly and efficiently. Some analysis techniques may involve processing mobility data into individual dimensions, then prioritizing the dimensions based on activity observations. Other analysis techniques may involve processing mobility data into predefined dimensions.
G06F 15/18 - dans lesquels un programme est modifié en fonction de l'expérience acquise par le calculateur lui-même au cours d'un cycle complet; Machines capables de s'instruire (systèmes de commande adaptatifs G05B 13/00;intelligence artificielle G06N)
G06N 99/00 - Matière non prévue dans les autres groupes de la présente sous-classe
9.
HUMAN DAILY ACTIVITY REPRESENTED BY AND PROCESSED AS IMAGES
Daily activities of mobile data may be represented as and processed as an image consisting of days of the week versus time of day. The images may be rapidly processed from raw data, but also may be readily analyzed using image processing techniques. The daily activities may be a composite of several images, each of which may represent observations for a particular dimension. The dimension may represent a type of activity, a physical location, a labeled location, or some other aspect. The image having time of day versus day of week may show relationships or patterns that may occur from one day to the next, which may otherwise be difficult to detect.
A trajectory may be derived from noisy location data by mapping candidate locations for a user, then finding a match between successive locations. Location data may come from various sources, including telecommunications networks. Telecommunications networks may give location data based on observations of users in a network, and such data may have many inaccuracies. The observations may be mapped to physical constraints, such as roads, pathways, train lines, and the like, as well as applying physical rules such as speed analysis to smooth the data and identify outlier data points. A trajectory may be resampled or interpolated to generate a detailed set of trajectory points from a sparse and otherwise ambiguous dataset.
Machine learning techniques may be applied to determining a mode oftransportation for a trajectory of a sequence of user locations. The mode oftransportation, such as walking, bicycling, riding in a car or bus, riding in a train, or othermode, may be determined by creating a training set of data, then using classificationmechanisms to classify trajectories by mode of transport. The training set may begenerated by tracking then verifying a user's transportation mode. In some cases, a usermay manually input or verify their transportation mode, while in other cases, a user'stransportation mode may be determined through other data sources.Page 40
G06F 15/18 - dans lesquels un programme est modifié en fonction de l'expérience acquise par le calculateur lui-même au cours d'un cycle complet; Machines capables de s'instruire (systèmes de commande adaptatifs G05B 13/00;intelligence artificielle G06N)
G01C 21/20 - Instruments pour effectuer des calculs de navigation
G01C 21/26 - NavigationInstruments de navigation non prévus dans les groupes spécialement adaptés pour la navigation dans un réseau routier
12.
TRAJECTORY ANALYSIS THROUGH FUSION OF MULTIPLE DATA SOURCES
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.
G01S 5/02 - Localisation par coordination de plusieurs déterminations de direction ou de ligne de positionLocalisation par coordination de plusieurs déterminations de distance utilisant les ondes radioélectriques
H04W 64/00 - Localisation d'utilisateurs ou de terminaux pour la gestion du réseau, p. ex. gestion de la mobilité
G01S 19/01 - Systèmes de positionnement par satellite à radiophares émettant des messages horodatés, p. ex. GPS [Système de positionnement global], GLONASS [Système global de navigation par satellite] ou GALILEO
13.
STAY AND TRAJECTORY IDENTIFICATION FROM HISTORICAL ANALYSIS OF COMMUNICATIONS NETWORK OBSERVATIONS
Sequences of location data points can be broken down into travel periods and stay periods through historical analysis of location data points. The noise and inaccuracies of location data points gathered from communications networks, such as mobile telephony networks,makes it difficult to accurately estimate when a user has stayed or dwelled at a particular location. The stay analysis may generate clusters of sequential location coordinates and may identify data points that appear to show movement but are likely to be noise, which can be artifacts of the communications network. Further, stay or travel sequences may initially be defined using thresholds of time and distance. Such thresholds may vary from one location to another and may be gathered and optimized over time.
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.
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.
Real time status of a device's movements may be determined from a sequence of location observations. The status may be in the form of a state, which may be "stay", "transit","pause", and "unknown". A state transition may occur from transit to stay when the device has remained within a predefined radius for a predefined time period. Prior to being labeled a"stay", a device that may have ceased moving but has not stayed at that location for enough time may be labeled "pause". For those devices in a "transit" state, a mode of transport maybe determined. The real time analysis system may be a low-overhead mechanism by which new location observations may be received and processed quickly and efficiently. The resulting data may be used by traffic analysts to monitor congestion, for real time traffic data for commuters, and other uses.
A user's location may be estimated by applying a probability function to raw user location data taken from various telephony or wireless systems. The probability function may estimate a user's location based on a training dataset that may be generated a priori to the analysis. A training dataset may be generated or updated by analyzing queries made with global positioning system (GPS) data to extract a device's GPS location. The probability function may be generated in part from physical maps. The estimated location may improve location accuracy, especially when attempting to map a user's location with accuracies that may be much smaller than a cell of a cellular telephony system.
H04W 64/00 - Localisation d'utilisateurs ou de terminaux pour la gestion du réseau, p. ex. gestion de la mobilité
H04W 4/02 - Services utilisant des informations de localisation
G01S 5/02 - Localisation par coordination de plusieurs déterminations de direction ou de ligne de positionLocalisation par coordination de plusieurs déterminations de distance utilisant les ondes radioélectriques
A dataset's uniqueness level may be calculated by analyzing a dataset to determine a uniqueness level. In cases where the uniqueness level may be too low for a particular purpose, meaning when the dataset may not provide enough anonymity, the dataset may be adjusted by recomputing the dataset with different resolutions of spatial data, temporal data, content data, and relationship data. By adjusting the resolution or accuracy of the data elements, the uniqueness level may thereby be adjusted. An error calculation may be determined by comparing the adjusted dataset to the original data, and the error value may represent the consistency of the data to the original data. The uniqueness level may be used as an assurance level of anonymity, which may be advertised when a dataset is sold or transferred to a third party for analysis.
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.
Human behavior may be predicted by building a model of people's physical movements along with a model that represents their affinities to various ontological elements, and their relationships to other people. The model may include a graph of interrelated people, as well as their hobbies, interests, employment, and other elements. The model may be analyzed by simulating people's activities as those activities interact with real or hypothetical networks. The real networks may be used to verify the model by comparing measured parameters to predictive parameters derived from simulation to calibrate the models. A calibrated model may then be used with a modified or hypothetical network to analyze the effects of changes to the real network.
H04W 24/00 - Dispositions de supervision, de contrôle ou de test
H04W 4/02 - Services utilisant des informations de localisation
H04W 4/04 - dans un environnement spécialisé, p.ex. des immeubles ou des véhicules
G06F 17/30 - Recherche documentaire; Structures de bases de données à cet effet
G06N 7/06 - Simulation sur des calculateurs universels
G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
G06Q 30/02 - MarketingEstimation ou détermination des prixCollecte de fonds
21.
Transportation network monitoring using cellular radio metadata
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.
H04W 4/029 - Services de gestion ou de suivi basés sur la localisation
H04W 4/42 - Services spécialement adaptés à des environnements, à des situations ou à des fins spécifiques pour les véhicules, p. ex. communication véhicule-piétons pour les véhicules de transport collectif, p. ex. les autobus, les trains ou les aéronefs
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.
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.
G08G 1/01 - Détection du mouvement du trafic pour le comptage ou la commande
G06F 19/00 - Équipement ou méthodes de traitement de données ou de calcul numérique, spécialement adaptés à des applications spécifiques (spécialement adaptés à des fonctions spécifiques G06F 17/00;systèmes ou méthodes de traitement de données spécialement adaptés à des fins administratives, commerciales, financières, de gestion, de surveillance ou de prévision G06Q;informatique médicale G16H)
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.
brevets
