Aspects of the disclosure relate to computing platforms that utilize improved machine learning techniques for dynamic device quality evaluation. A computing platform may receive driving data from a mobile device. Using the driving data, the computing platform may compute a plurality of driving metrics, which may include: a geopoint expectation rate score, a trips per day rank score, a consecutive geopoint time difference score, a global positioning system (GPS) accuracy rating score, and a distance between consecutive trips score. By applying a machine learning model to the plurality of driving metrics, the computing platform may compute a device evaluation score, indicating a quality of the driving data received from the mobile device. Based on the device evaluation score, the computing platform may set flags, which may be accessible by a driver score generation platform, causing the driver score generation platform to perform an action with regard to the mobile device.

The present technology improves points-of-interest (POIs) in applications by the gathering and use of data available from various sources to improve metadata of POIs in applications (e.g., map applications) or any other metadata or information that may be of interest to a user regarding any given POI. The present technology resolves transactions to POIs or Brands (in a map application, for example) and improves, updates, creates, and removes POIs/Brands. The present technology can also gain a clear name, granular and correct categorization, a URL, phone/chat contact info, etc. of the transactions.

A method and an apparatus for generating a route planning model and a storage medium are provided. The method for generating a route planning model includes: obtaining a target route data set associated with a target site; and determining a target route planning model of the target site with a site optimization object corresponding to the target site, based on the target route data set and a first route planning model; wherein the first route planning model is determined based on a set of historical route data through at least a first training, the set of historical route data being associated with a plurality of sites different from the target site and a first training optimization object for the first training corresponding to the plurality of sites.

A method may include detecting, during a flight of an aircraft system, a conflict with a planned route of the aircraft system, determining one or more alternate routes for the aircraft system to avoid the conflict, wherein each of the one or more alternate routes avoid secondary conflicts with active flight operations, transmitting first data to cause first visual information indicating the conflict and second visual information indicating the one or more alternate routes to be displayed to a user, receiving second data indicating one of the one or more alternate routes being selected by the user, and updating the planned route of the aircraft system to include the alternate route selected by the user.

A method and system include identifying, by a processor, departing flight information that designates departure airports and departure times in payment card transaction data of a plurality of users. Airport-specific data for a departure airport before a departure time of a departing flight of a user from the plurality of users is received. The airport-specific data is inputted into a machine learning model that outputs a user-specific airport processing time for the user to reach a departure gate upon arriving to the departure airport. A travel time from a geographical location of the computing device of the user to the departure airport is received from a navigation system. The computing device displays a time for the user to start travel to the departure airport based on the user-specific airport processing time and the travel time to the departure airport for the user to reach the departure gate by the departure time.

A cloud-based, geospatially-enabled data recording, notification, and rendering system.

A method of optimizing rider satisfaction includes classifying, using a first neural network of a hybrid neural network, social media data sourced from a plurality of social media sources as indicative of an effect on a transportation system. The method further includes predicting, using a second neural network of the hybrid neural network, at least one aspect of rider satisfaction affected by an effect on the transportation system derived from the social media data classified as indicative of an effect on the transportation system. The method still further includes optimizing, using a third neural network of the hybrid neural network, the at least one aspect of rider satisfaction for at least one rider occupying a vehicle in the transportation system.

Systems and methods for route mapping with familiar routes include striking a balance between optimal routes from standard navigation systems minimizing time and distance and a mapping component that suggests familiar routes based on a user's route history. New routes including one or more familiar routes may be suggested to the user when they are not too far out of the way or take too long compared to the optimal routes and when they may be preferable or more comfortable.

A system is provided generating a context-dependent experience for a vehicle driver, e.g., to relax or enhance the driver's mental state. A system controller is configured to receive driving context data regarding a driving situation from various driving context data sources. The driving context data may include vehicle operation data, e.g., generated by vehicle-based sensors, and environmental data regarding an environment external to the vehicle, e.g., received wirelessly from a remote server. The system controller may identify content triggering events based on (a) the received driver context data and (b) a set of content triggering rules. For each content triggering event, the system controller may select one or more human-perceivable contents elements (e.g., audio clips, seat massage settings, or air conditioner settings), and control one or more content output devices (e.g., speakers, seat massage system, or vehicle HVAC system) to output the selected content element(s) to the driver.

A method, apparatus and computer program product are provided for improving user experiences for autonomous driving. In context of a method, the method determines one or more autonomous transition region parameters for a respective autonomous transition region along a route. The method also, based on the one or more autonomous transition region parameters, determines whether an action is to be performed by a vehicle in accordance with user preference data associated with a user. The method also causes the vehicle to perform the action in accordance with a determination that the action is to be performed by the vehicle.