Abstract: A method is provided for establishing determining lane-level route guidance, and more particularly, to establishing recommended lane-level guidance between an origin and a destination based on a safe, efficient, or popular path. Methods may include receiving a plurality of probe data points from a plurality of probe apparatuses, each probe apparatus traveling between a respective origin and destination pair; determining a lane-level maneuver pattern for each probe apparatus between the origin and the destination; providing for storage of the lane-level maneuver patterns for each probe apparatus in the memory; grouping together lane-level maneuver patterns for probe apparatuses having an origin and destination pair within a predefined similarity of origin and destination pairs of other apparatuses; and generating a lane-level maneuver pattern for each group based on at least one of a popularity, efficiency, or relatively safe lane-level maneuver pattern for the respective group.

Abstract: A method is provided for generating lane-level route guidance. Methods may include generating a first lane-level maneuver pattern between an origin and a destination based on a database of historical probe data points, each probe data point received from a probe apparatus of a plurality of probe apparatuses, where the lane-level maneuver pattern includes a recommended lane of travel along the route between the origin and the destination based on at least one of relative safety, relative popularity, or relative efficiency of the lane-level maneuver pattern; receiving a plurality of real-time or near real-time probe data points; generating an updated lane-level maneuver pattern between the origin and the destination based on the first lane-level maneuver pattern and the plurality of real-time or near real-time probe data points; and providing for route guidance of a vehicle based on the updated lane-level maneuver pattern.

Abstract: A plurality of instances of probe data are received. Each instance of probe data corresponds to travel of a vehicle apparatus along a first segment, comprises an indication of at least one parameter characterizing the travel of the vehicle apparatus along the first segment. The at least one parameter is extracted from the instances of probe data to generate a distribution of parameters. One or more clusters of instances of probe data are identified based on the distribution of parameters. Responsive to identifying two or more clusters, a representative at least one parameter is determined for each cluster, and an element of a data structure is modified based thereon for each cluster. Responsive to identifying only one cluster of instances of probe data, the data structure is not modified based on the cluster. A navigation application is configured to use the data structure to perform a navigation function.

Abstract: A plurality of sequences of instances of probe data are received. Each sequence corresponds to travel of a vehicle apparatus along a first segment to one of two or more possible subsequent second segments and comprises an indication of at least one parameter characterizing the travel of the vehicle apparatus along the first segment. Groups of sequences are determined. Each group corresponds to a subsequent second segment. For a first group, at least one representative parameter is determined based on the at least one parameter of the sequences of the first group. An element of a data structure is modified and/or updated with the at least one representative parameter. The element of the data structure corresponds to the first group. A geographic database comprises the data structure and a navigation application is configured to use at least a portion of the data structure to perform a navigation function.

Abstract: An approach is provided for using aerial drones for road and traffic monitoring. The approach, for example, involves navigating an aerial drone to a physical marker located on a road link. The approach also involves initiating a capture of sensor data of the physical marker by a sensor of the aerial drone. The approach further involves adjusting a position, an altitude, or a combination thereof of the aerial drone to a reference position, a reference altitude, or a combination thereof over the physical marker based on the sensor data. The approach further involves initiating a capture of one or more images of the road link by the aerial drone at the reference position, the reference altitude, or a combination thereof. By way of example, the captured images can be processed for road and traffic monitoring and/or other similar applications.

Abstract: A method is provided to generate a route between an origin and a destination using historical travel times between segments of a road network map. Methods may include accessing a memory configured to store road network data segmented into tiles represented by quadkeys; determining a travel time between any two quadkeys of the stored road network data; receiving an origin and a destination within a road network corresponding to the road network data; calculating a route between the origin and the destination using the travel time between quadkeys that can be traversed from the origin to the destination; generating route guidance for the route between the origin and the destination; and providing the route guidance to a user indicating the route between the origin and the destination. The travel time between any two quadkeys may be determined based on historical travel times between the respective two quadkeys.

Abstract: A plurality of instances of pre-intersection and post-intersection probe data are received. Each instance of pre-intersection probe data corresponds to traveling along a pre-intersection road segment before traveling through an intersection. Each instance of post-intersection probe data corresponds to traveling along a post-intersection road segment following traveling through the intersection. Instances of pre-intersection probe data are clustered into pre-intersection clusters based on a post-intersection road segment identified by the corresponding instance of post-intersection probe data. Instances of post-intersection probe data are clustered into post-intersection clusters based on the post-intersection road segment identified thereby. A traffic level indicator is determined for each cluster. A traffic level indicator difference is determined for each pair of corresponding pre-intersection and post-intersection clusters.

Abstract: An approach is provided for determining probe data generated by a device travelling on a road segment is for pedestrian travel. A lane matching platform determines a speed of a probe point. The lane matching platform also determines a spatial distance of the probe point from a center line vector of a road segment. The lane matching platform also determines an allowed transport mode for the road segment. The lane matching platform further identifies the transport mode of the probe point based on the speed, the location of the probe point with respect to the center line, and the allowed transport mode. The transport mode, the allowed transport mode, or a combination thereof includes a car transport mode or a pedestrian transport mode.

Abstract: A method, apparatus and computer program product are provided for establishing direction based traffic events and lane-level traffic associated therewith. Methods may include receiving a plurality of probe data points, where each probe data point includes location information associated with the respective probe apparatus; determining a path through an intersection from among a plurality of paths through the intersection for each probe apparatus based, at least in part, on a sequence of probe data points from each respective probe apparatus; determining, for each path through the intersection, an average speed of probe apparatuses traveling along the respective path and an average travel time along a road link approaching the intersection of the probe apparatuses traveling along the respective path; and determining a direction based traffic event in response to the differential amount of time between the highest average travel time and the lowest average travel time.

Abstract: A method is disclosed comprising: obtaining data associated with each road segment of at least one road segment, said data comprising: a representative of at least one link associated with the respective road segment; obtaining probe data associated with the respective road segment, the probe data comprising: at least one piece of position information; determining a sinuous driving metric, which is a value being indicative of a sinuosity of driving on the respective road segment based at least partially on the probe data and its allocation with respect to the respective road segment. It is further disclosed an according apparatus, computer program and system.

Abstract: System and methods are provided for calculating a lane maneuver delay for different lane maneuvers and different road segments at different times of the day to generate a predicted lane maneuver delay for use in routing and navigation services. A lane-level map-matcher identifies the lane a vehicle is driving on using positional sensors. Sensor data is obtained from vehicle sensors using the left-turn and right-turn signal lights sensor. A lane maneuver delay value is calculated from the time period a left-turn or right-turn signaling light was kept on before a vehicle completed a lane maneuver. The lane maneuver delay values are aggregated to generate a predicted lane maneuver delay that may be used in lane level routing instructions.

Abstract: An approach is provided for determining one or more modal routes between one or more origin areas and one or more destination areas based on trajectory data. The approach involves querying, by a processor, a trajectory database to retrieve one or more trajectories that contain one or more probe points located within the one or more origin areas and the one or more destination areas. The approach also involves clustering, by the processor, the one or more trajectories into one or more trajectory clusters based on a distance. The approach further involves determining, by the processor, respective counts of the one or more trajectories within each of the one or more trajectory clusters. The approach further involves presenting, by the processor, at least one of the one or more trajectory clusters as the one or more modal routes based on the respective counts.

Abstract: An apparatus is configured to perform a method for collaborative localization of multiple devices in a geographic area including receiving global localization data originating with one or more neighboring devices, receiving local localization data originating with a mobile device, determining a first confidence level from the local localization data, determining a second confidence level from the global localization data, and performing, by a processor, a collaborative localization calculation for the mobile device based on the first confidence level and the second confidence level.

Abstract: An approach is provided for determining tunnel speed for a vehicle travelling through a tunnel. A tunnel processing platform aggregates probe data associated with at least one vehicle into at least one tunnel path based, at least in part, on a network geometry topology for at least one tunnel. The tunnel processing platform also designates at least one probe point collected upstream of the at least one tunnel as at least one starting point of the at least one tunnel path and at least one temporary probe point as at least one endpoint of the at least one tunnel path, wherein the at least one temporary probe point is downstream of the at least one tunnel. It then determines at least one temporary tunnel speed for the at least one tunnel path based, at least in part, on the timestamp for the at least one probe point and the current time associated with the at least one temporary probe point.

Abstract: An apparatus for matching probe measurements to a path in a geographic location includes a receiver, a window manager, a location generator, a path calculator, and an output. The receiver is configured to receive a stream of probe measurements. The window manager is configured to fill a window with the measurements, to select an additional measurement from the stream, and to select an oldest measurement in the window. The location generator is configured to generate candidate locations for the measurements in the window and the additional measurement. The path calculator is configured to match the oldest measurement to a candidate location. The output is configured to output a path-matched probe measurement based on the oldest measurement and the candidate location matched to the oldest measurement.

Abstract: A plurality of instances of probe data are received. Each instance is matched to a link of a digital map. A distance parameter for each instance is determined based on the link. A most likely lane for each instance is determined based on the corresponding distance parameter. Probe trajectories are constructed based on sequences of instances of probe data identified in the plurality of instances of probe data. At least one of the probe trajectories is analyzed based at least in part on lane change probabilities to generate at least one lane level trajectory. Lane level traffic information is determined based on the at least one lane level trajectory and the corresponding instances of probe data. At least a portion of the lane level traffic information, which is configured for use in performing at least one navigation-related function, is provided.

Abstract: A method performed by at least one apparatus is described, said method comprising: obtaining a routing request for determining a route between a first position and a second position in a transport network of a map; determining or triggering determining alternate routes between said first position and said second position at least based on costs of a first cost type for travelling a respective route; and determining or triggering determining costs of a second cost type for travelling a respective route for at least one of said alternate routes, wherein said second cost type is based on a probability of an unexpected traffic event on a respective route.

Abstract: In an example embodiment, a plurality of sequences of instances of probe data are received. Each sequence of instances of probe data is captured and provided by a probe apparatus comprising a plurality of sensors and is onboard a vehicle. An instance of probe data comprises location information indicating a location of the corresponding probe apparatus and the instances are ordered by capture time to form the sequence of instances. A travel direction of each probe apparatus is determined based on the corresponding sequence. Each probe apparatus is matched to a lane of a road segment based on the determined travel direction and a predetermined vehicle lane pattern. The vehicle lane pattern comprises at least one reversible lane. Probe apparatuses matched to the at least one reversible lane are identified. An active direction is determined based on the number of identified probe apparatuses corresponding to each travel direction.

Abstract: A method is provided to determine a number of vehicle travel lanes along a road segment. A method may include: receiving probe data from a plurality of probes, where the probe data includes probe data points having location and heading; matching the probe data to a road segment to generate map-matched probe data; analyzing the probe data relative to the road segment to establish a multi-modal distribution of probe data representing a distance of the probe data from a predefined reference position of the road segment; determining a number of vehicle travel lanes of the road segment based on peaks in the established multi-modal distribution being associated with individual lanes; and providing the determined number of vehicle travel lanes and the associated road segment to a map services database for lane-level route guidance.

Abstract: An apparatus is configured to perform a method for collaborative localization of multiple devices in a geographic area including receiving global localization data originating with one or more neighboring devices, receiving local localization data originating with a mobile device, determining a first confidence level from the local localization data, determining a second confidence level from the global localization data, and performing, by a processor, a collaborative localization calculation for the mobile device based on the first confidence level and the second confidence level.