Abstract: Methods, computer-readable media, software, and system may generally identify, determine, and understand the significance of commute location data using telematics data. The system and methods may identify significant commute location data and points by analyzing telematics data and capturing GPS locations associated with the mobility of a user. The commute location data may be classified as data points including origin, destination, and waypoints. This commute location data may be used with metadata to identify significant locations associated with the user. The commute location data may also be used with metadata to understand mobility behavior of the user. Lastly, the commute location data may be used with metadata to determine risk associated with the user, such as based on a risk map.

Abstract: Methods, computer-readable media, software, and system may generally build and quantify mobility patterns based on user location data, both at an individual level and an aggregate level. The system may determine the origin and destination data for each trip taken by a user. The system may then define areas of mobility using a mobility graph built from the data. The graph may include nodes and edges. In some examples, the nodes are constructed from the origins and destinations of the trajectories using spatial clustering techniques. Further, the edges between nodes may be constructed based on the trips between them, such as two nodes are connected by an edge if there is at least one trip between them. The edges may be given different weights based on trip frequencies. The system may then determine a region of mobility using the generated mobility graph and data clustering techniques.

Abstract: Aspects of the disclosure relate to using machine learning methods for customized output generation. A computing platform may train a model (using historical data) by classifying the historical data by a trip context, a device interaction context, and physical condition context, or a personality context, and training models using the classified historical data. The computing platform may monitor a data source system to collect new data, which may include information about multiple drivers. The computing platform may generate, by inputting the new data into the model, a customized driving output for a first driver, where the customized driving output is based at least in part on information about a second driver. The computing platform may send, to a computing device, the customized driving output and commands directing the computing device to display the customized driving output, which may cause the computing device to display the customized driving output.

Abstract: In one aspect, a method includes receiving a telematics data associated with a vehicle collected from one or more data sources and determining, using a machine-learning model trained to identify high-risk driving behaviors using telematics data, one or more predictions based on the telematics data. A prediction of the one or more predictions is associated with a current time. The method may further include generating a time-based report of the one or more predictions. The time-based report identifies instances of the one or more predictions that reach a threshold value.

Abstract: Implementations described herein provide systems, methods, and devices for vehicle monitoring and control based on advanced driving assistance system (ADAS) feature usage. The systems, methods, and devices include a driving data collection system for collecting driving-related data from an original equipment manufacturer (OEM) server and/or a mobile device. The OEM server receives the ADAS-related data from the vehicle and sends the ADAS-related data to the vehicle monitoring and control platform. Systems also include a vehicle/driver operation assessment system which uses one or more first deep-learning models to generate vehicle operation behavior values from the ADAS-related data. Furthermore the systems include a dynamic risk control model which uses one or more second deep-learning models to generate target outputs based on the vehicle operation behavior values. The target outputs include modification or control of the vehicle operations, one or more alerts, and/or a pricing variable.

Abstract: Methods, computer-readable media, software, systems and apparatuses may receive, from a user device, notification of a user enrolling in a privacy incident protection application, receive, from the user device, user account information associated with one or more user accounts of the user, where the user account information includes a plurality of contextual settings, determine a risk footprint associated with the user based on the user account information, monitor the one or more user accounts, receive an indication of an incident based on monitoring the one or more user accounts and based on the risk footprint, and transmit an incident notification to a data server provider associated with the incident. The incident notification may include instructions to perform a mitigation action associated with the incident.

Abstract: Aspects of the disclosure relate to using machine learning methods for customized output generation. A computing platform may train a model (using historical data) by classifying the historical data by a trip context, a device interaction context, and physical condition context, or a personality context, and training models using the classified historical data. The computing platform may monitor a data source system to collect new data, which may include information about multiple drivers. The computing platform may generate, by inputting the new data into the model, a customized driving output for a first driver, where the customized driving output is based at least in part on information about a second driver. The computing platform may send, to a computing device, the customized driving output and commands directing the computing device to display the customized driving output, which may cause the computing device to display the customized driving output.

Abstract: Methods, computer-readable media, software, and system may generally build and quantify mobility patterns based on user location data, both at an individual level and an aggregate level. The system may determine the origin and destination data for each trip taken by a user. The system may then define areas of mobility using a mobility graph built from the data. The graph may include nodes and edges. In some examples, the nodes are constructed from the origins and destinations of the trajectories using spatial clustering techniques. Further, the edges between nodes may be constructed based on the trips between them, such as two nodes are connected by an edge if there is at least one trip between them. The edges may be given different weights based on trip frequencies. The system may then determine a region of mobility using the generated mobility graph and data clustering techniques.

Abstract: Aspects of the disclosure relate to using machine learning methods for customized output generation. A computing platform may train a model (using historical data) by classifying the historical data by a trip context, a device interaction context, and physical condition context, or a personality context, and training models using the classified historical data. The computing platform may monitor a data source system to collect new data, which may include information about multiple drivers. The computing platform may generate, by inputting the new data into the model, a customized driving output for a first driver, where the customized driving output is based at least in part on information about a second driver. The computing platform may send, to a computing device, the customized driving output and commands directing the computing device to display the customized driving output, which may cause the computing device to display the customized driving output.

Abstract: Methods, computer-readable media, software, and system may generally identify, determine, and understand the significance of commute location data using telematics data. The system and methods may identify significant commute location data and points by analyzing telematics data and capturing GPS locations associated with the mobility of a user. The commute location data may be classified as data points including origin, destination, and waypoints. This commute location data may be used with metadata to identify significant locations associated with the user. The commute location data may also be used with metadata to understand mobility behavior of the user. Lastly, the commute location data may be used with metadata to determine risk associated with the user, such as based on a risk map.

Abstract: Methods, computer-readable media, software, and system may generally build and quantify mobility patterns based on user location data, both at an individual level and an aggregate level. The system may determine the origin and destination data for each trip taken by a user. The system may then define areas of mobility using a mobility graph built from the data. The graph may include nodes and edges. In some examples, the nodes are constructed from the origins and destinations of the trajectories using spatial clustering techniques. Further, the edges between nodes may be constructed based on the trips between them, such as two nodes are connected by an edge if there is at least one trip between them. The edges may be given different weights based on trip frequencies. The system may then determine a region of mobility using the generated mobility graph and data clustering techniques.

Abstract: Methods, computer-readable media, software, and system may generally identify, determine, and understand the significance of commute location data using telematics data. The system and methods may identify significant commute location data and points by analyzing telematics data and capturing GPS locations associated with the mobility of a user. The commute location data may be classified as data points including origin, destination, and waypoints. This commute location data may be used with metadata to identify significant locations associated with the user. The commute location data may also be used with metadata to understand mobility behavior of the user. Lastly, the commute location data may be used with metadata to determine risk associated with the user, such as based on a risk map.

Abstract: Methods, computer-readable media, software, and apparatuses include receiving, from a plurality of risk information sources, risk information associated with a user account, wherein the risk information includes a plurality of risk components, determining, for each of the plurality of risk components, an impact score and a risk probability by applying a machine learning model to risk information associated with the user account, generating an interactive risk index dashboard including a plurality of interactive risk index elements, wherein each of the plurality of interactive risk index elements is associated with a risk component of the plurality of risk components, and displaying, on the display of the apparatus, the interactive risk index dashboard, wherein each of the plurality of interactive risk index elements is displayed in a portion of the interactive risk index dashboard in accordance with a respective determined impact score and risk probability.

Abstract: Methods, computer-readable media, software, and system may generally identify, determine, and understand the significance of commute location data using telematics data. The system and methods may identify significant commute location data and points by analyzing telematics data and capturing GPS locations associated with the mobility of a user. The commute location data may be classified as data points including origin, destination, and waypoints. This commute location data may be used with metadata to identify significant locations associated with the user. The commute location data may also be used with metadata to understand mobility behavior of the user. Lastly, the commute location data may be used with metadata to determine risk associated with the user, such as based on a risk map.

Abstract: Methods, computer-readable media, software, and system may generally build and quantify mobility patterns based on user location data, both at an individual level and an aggregate level. The system may determine the origin and destination data for each trip taken by a user. The system may then define areas of mobility using a mobility graph built from the data. The graph may include nodes and edges. In some examples, the nodes are constructed from the origins and destinations of the trajectories using spatial clustering techniques. Further, the edges between nodes may be constructed based on the trips between them, such as two nodes are connected by an edge if there is at least one trip between them. The edges may be given different weights based on trip frequencies. The system may then determine a region of mobility using the generated mobility graph and data clustering techniques.

Abstract: Methods, computer-readable media, software, and apparatuses include receiving, from a plurality of risk information sources, risk information associated with a user account, wherein the risk information includes a plurality of risk components, determining, for each of the plurality of risk components, an impact score and a risk probability by applying a machine learning model to risk information associated with the user account, generating an interactive risk index dashboard including a plurality of interactive risk index elements, wherein each of the plurality of interactive risk index elements is associated with a risk component of the plurality of risk components, and displaying, on the display of the apparatus, the interactive risk index dashboard, wherein each of the plurality of interactive risk index elements is displayed in a portion of the interactive risk index dashboard in accordance with a respective determined impact score and risk probability.

Abstract: Methods, computer-readable media, software, and apparatuses include receiving, from a plurality of risk information sources, risk information associated with a user account, wherein the risk information includes a plurality of risk components, determining, for each of the plurality of risk components, an impact score and a risk probability by applying a machine learning model to risk information associated with the user account, generating an interactive risk index dashboard including a plurality of interactive risk index elements, wherein each of the plurality of interactive risk index elements is associated with a risk component of the plurality of risk components, and displaying, on the display of the apparatus, the interactive risk index dashboard, wherein each of the plurality of interactive risk index elements is displayed in a portion of the interactive risk index dashboard in accordance with a respective determined impact score and risk probability.

Abstract: Methods, computer-readable media, software, systems and apparatuses may receive, from a user device, notification of a user enrolling in a privacy incident protection application, receive, from the user device, user account information associated with one or more user accounts of the user, where the user account information includes a plurality of contextual settings, determine a risk footprint associated with the user based on the user account information, monitor the one or more user accounts, receive an indication of an incident based on monitoring the one or more user accounts and based on the risk footprint, and transmit an incident notification to a data server provider associated with the incident. The incident notification may include instructions to perform a mitigation action associated with the incident.

Abstract: An apparatus and methods provide trajectory data for a geographic area. Multiple probe data sets are received or identified from one or more mobile devices. The probe data sets include time values in a first sequence associated with location values in the first sequence. At least one of the probe data sets is modified to reverse the location values such that the modified probe data set includes time values in a first sequence associated with location values in a second sequence. A location clustering algorithm is performed on the plurality of probe data sets and the modified probe data set based on the location values.

Abstract: Apparatus and methods are described for identification of a geographic location for sign placement. Probe data is received for a geographic area, and the probe data is collected by one or more sensors. Trips including destinations within the geographic area are matched with road segments from a geographic database. At least one potential sign placement road segment is compared to road segments of the trip. One or more destinations are selected from the trips based on the comparison of the at least one sign placement road segment to the trip road segments.