One or more potential drone and/or flying car (DFC) corridors are identified based on the topology of a road network. Trajectories traveled by vehicles are determined from a plurality of instances of probe data received from a plurality of vehicle apparatuses onboard the vehicles. A volume of traffic for a path through the road network and corresponding to a potential DFC corridor is determined based on the trajectories. A delay metric for the path through the road network and corresponding to the potential DFC corridor is determined based on the trajectories. A traffic metric is then determined for the path based on a combination of the volume of traffic, the delay metric and a measure of the topology of the road network. The one or more potential DFC corridors are ranked by their corresponding traffic metrics.

The present disclosure is directed to dynamically analyzing data and conditions associated with the movement of a vehicle. A processor may evaluate vehicle location and speed information when identifying an amount of navigation data to send to a computer at a vehicle. The amount of information sent to the computer may vary based on the vehicle speed, a type of road that the vehicle is able to drive along, or may vary based on other attributes. The information sent to the computer may also relate to an area that the vehicle is anticipated to move through in a given time frame. Mapping data sent to the vehicle computer may be associated with a larger area when a vehicle is moving at a faster speed and may be associated with a smaller area when the vehicle is moving at a slower speed.

A display apparatus for a vehicle includes: a controller configured to create map information; and a display device configured to display the map information created by the controller, wherein the controller controls the display device to display a path guidance texture based on a road shape when guiding a path among the map information.

A system includes one or more processors to obtain a transportation event and a transportation event time of a vehicle system at a current location on a route from an origin to a destination. The one or more processors determine transportation event conditions based on historical transportation data and predict, by mathematical optimization methods, optimal transportation routes based on one or more of historical transportation routes, contractual routes, contractual junctions, and station master data. The one or more processors cluster from the historical transportation data, by a machine learning classification method, transportation event data clusters and match at the current location the transportation event data to historical transportation data machine learning classification clusters. The one or more processors predict, by a machine learning model, an estimated time of arrival (ETA) of the vehicle system to the destination.

Methods and systems for traffic jam management include generating a representation of a road network that comprises a road graph, with vertices of the road graph representing road segments and edges of the road graph representing pairs of adjacent road segments at each intersection. The road graph is partitioned into sub-graphs to balance a number of messages received from vehicles within each sub-graph and to minimize vehicle transitions between sub-graphs. It is determined that a traffic jam is present on one or more road segments. Navigational information is provided to one or more vehicles responsive to the traffic jam.

A system and method of augmenting a delivery route. The method comprises receiving first location information from a first position device and receiving second location information from a second position device. The method further comprises storing the first and second location information in a memory structure and determining, based on at least one of the first and second information, a time and a location for each of a plurality of stops. The method also comprises reconciling the determined time and location of each of the plurality of stops with a stored route comprising a plurality of stored stops, the stored stops having a stored location and a stored time associated therewith and estimating a plurality of transition times between pairs of the plurality of stops. The method further also comprises constructing an updated timeline based on the plurality of stored stops, the plurality of determined stops, and the plurality of transition times and updating the delivery route based on the constructed timeline.

A method includes receiving project information indicating a location of a worksite. The method also includes providing a first travel path to an electronic device associated with a mobile machine, wherein providing the first travel path to the electronic device causes at least part of the first travel path to be displayed via a display. The method further includes receiving location information indicating an initial location of the machine and one or more additional locations of the machine. Additionally, the method includes determining that the machine reached the worksite, and identifying, based at least in part on the location information, a second travel path extending from the initial location to the worksite. The method further includes determining whether the second travel path matches the first travel path, and storing at least one of the travel paths in a memory associated with a controller.

A point of interest (POI)-based information recommendation method and apparatus, an electronic device, and a computer-readable storage medium are provided. The method includes: determining an encircling region encircling a POI in an electronic map; using an original boundary of the encircling region as an inner boundary and performing expansion processing on the inner boundary, to obtain an outer boundary of the encircling region; determining candidate location relationships between a terminal device and the inner boundary and the outer boundary according to positioning data of the terminal device; determining a real-time state according to a historical state of the terminal device and the candidate location relationships, the real-time state being used for representing a location relationship between the terminal device and the inner boundary; and controlling, according to the real-time state, information about the POI outputted through a location service.

A system including a processor and memory may provide for automatically activating or deactivating a feature of a fleet vehicle. For example, one or more fleet vehicles may include one or more of a global-positioning system, a speed governor, electronically-controlled brakes, an electronically-controlled accelerator, a speed limiter, or an on-board computer with a processor and memory. One or more features may be activated by a local or remote computing device or system. For example, a system may determine one or more recommended routes between two or more locations. The system may track a fleet vehicle's progress along a route, and activate a feature of the fleet vehicle based on the fleet vehicle following or not following the recommended route. For example, the system may cause activation of a speed limiter on the fleet vehicle, disable the fleet vehicle, and/or activate or deactivate autonomous features of the fleet vehicle.

In some implementations, a computing device can provide a map application providing a representation of a physical structure of venues (e.g., shopping centers, airports) identified by the application. In addition, the application can provide an inside view that includes the physical layout and geometry of the venue's structure as well as the location, structure and layout of points of interest (e.g., stores, security check points, restrooms) within the venue. The views become more detailed as the user zooms into the venue to reveal points of interest and to give the user a feel for traversing the venue.