Abstract: A method is provided that includes: receiving a plurality of estimated position points, each estimated position point including a timestamp, where each estimated position point is an estimate of a position of a vehicle at a time respective timestamp; receiving on or more path events, where each of the one or more path events includes a timestamp and data from at least one sensor of the vehicle; generating a path from the plurality of estimated position points, where the estimated position points are arranged in order of ascending time represented by the respective timestamp; and interpolating between two of the estimated position points to determine a location corresponding to one of the one or more path events, where the timestamp of the one of the one or more path event corresponds to a time that is between the times represented by the timestamps of the two estimated position points.

Abstract: Dangerous driving events may be reported by detecting an occurrence of a dangerous event relating to the operation of a vehicle. A notification message of the dangerous event may be generated involving a time of occurrence of the dangerous event, a location of the dangerous event, and an event type of a plurality of event types for the dangerous event. The notification message may then be transmitted to communicate the occurrence dangerous driving event and information related to the dangerous driving event.

Abstract: An approach is provided for generating delivery data models for aerial package delivery. The approach involves determining at least one delivery surface data object to represent one or more delivery surfaces of at least one delivery location, wherein the one or more delivery surfaces represents at least one surface upon which to deliver at least one package. The approach further involves causing, at least in part, a creation of at least one complete delivery data model based, at least in part, on the at least one delivery surface data object to represent the at least one delivery location. The approach further involves causing, at least in part, an encoding of at least one geographic address in the at least one complete delivery data model to cause, at least in part, an association of the at least one complete delivery data model with at least one geographic location.

Abstract: Methods for providing a highly assisted driving (HAD) service include: (a) transmitting telematics sensor data from a vehicle to a remote first server; (b) transmitting at least a portion of the telematics sensor data from the remote first server to a remote second server, wherein the remote second server is configured to execute a HAD application using received telematics sensor data, and wherein the HAD application is configured to output a HAD service result; and (c) transmitting the HAD service result from the remote second server to a client. Apparatuses for providing a HAD service are described.

Abstract: Methods for providing a highly assisted driving (HAD) service include: (a) transmitting telematics sensor data from a vehicle to a remote first server; (b) transmitting at least a portion of the telematics sensor data from the remote first server to a remote second server, wherein the remote second server is configured to execute a HAD application using received telematics sensor data, and wherein the HAD application is configured to output a HAD service result; and (c) transmitting the HAD service result from the remote second server to a client. Apparatuses for providing a HAD service are described.

Abstract: Dangerous driving events may be reported by detecting an occurrence of a dangerous event relating to the operation of a vehicle. A notification message of the dangerous event may be generated involving a time of occurrence of the dangerous event, a location of the dangerous event, and an event type of a plurality of event types for the dangerous event. The notification message may then be transmitted to communicate the occurrence dangerous driving event and information related to the dangerous driving event.

Abstract: An approach is provided for providing geographic delivery location for aerial package delivery. The approach involves determining building footprint information for at least one building associated with at least one geographic address. The approach also involves determining source data associated with the at least one building, the at least one geographic address, or a combination thereof. The approach further involves processing and/or facilitating a processing of the building footprint information and the source data to determine one or more entrances associated with the at least one building. The approach also involves processing and/or facilitating a processing of the source data associated with the one or more entrances to determine one or more delivery surfaces for the at least one geographic address.

Abstract: Dangerous driving events may be reported by detecting an occurrence of a dangerous event relating to the operation of a vehicle. A notification message of the dangerous event may be generated involving a time of occurrence of the dangerous event, a location of the dangerous event, and an event type of a plurality of event types for the dangerous event. The notification message may then be transmitted to communicate the occurrence dangerous driving event and information related to the dangerous driving event.

Abstract: An approach is provided for generating delivery data models for aerial package delivery. The approach involves determining at least one delivery surface data object to represent one or more delivery surfaces of at least one delivery location, wherein the one or more delivery surfaces represents at least one surface upon which to deliver at least one package. The approach further involves causing, at least in part, a creation of at least one complete delivery data model based, at least in part, on the at least one delivery surface data object to represent the at least one delivery location. The approach further involves causing, at least in part, an encoding of at least one geographic address in the at least one complete delivery data model to cause, at least in part, an association of the at least one complete delivery data model with at least one geographic location.

Abstract: An approach is provided for generating delivery data models for aerial package delivery. The approach involves determining at least one delivery surface data object to represent one or more delivery surfaces of at least one delivery location, wherein the one or more delivery surfaces represents at least one surface upon which to deliver at least one package. The approach further involves causing, at least in part, a creation of at least one complete delivery data model based, at least in part, on the at least one delivery surface data object to represent the at least one delivery location. The approach further involves causing, at least in part, an encoding of at least one geographic address in the at least one complete delivery data model to cause, at least in part, an association of the at least one complete delivery data model with at least one geographic location.

Abstract: Methods for providing a highly assisted driving (HAD) service include: (a) transmitting telematics sensor data from a vehicle to a remote first server; (b) transmitting at least a portion of the telematics sensor data from the remote first server to a remote second server, wherein the remote second server is configured to execute a HAD application using received telematics sensor data, and wherein the HAD application is configured to output a HAD service result; and (c) transmitting the HAD service result from the remote second server to a client. Apparatuses for providing a HAD service are described.

Abstract: Methods for providing a highly assisted driving (HAD) service include: (a) transmitting telematics sensor data from a vehicle to a remote first server; (b) transmitting at least a portion of the telematics sensor data from the remote first server to a remote second server, wherein the remote second server is configured to execute a HAD application using received telematics sensor data, and wherein the HAD application is configured to output a HAD service result; and (c) transmitting the HAD service result from the remote second server to a client. Apparatuses for providing a HAD service are described.

Abstract: A traffic adjustment or a traffic command, for example, is based on data congestion on a cellular network. Position data is received from a mobile device. A portion of a cellular network that corresponds to the position data is selected. The portion of the cellular network may be the geographic area covered by a cell. The network state of the portion of the cellular network is identified. A traffic device is also identified. The traffic device may be a traffic light or a variable speed sign. The traffic adjustment or traffic command for the traffic device is calculated based on the network state.

Abstract: Systems, apparatuses, and methods are provided for developing a fingerprint database for and determining the geographic location of an end-user device (e.g., vehicle, mobile phone, smart watch, etc.) with the database. A fingerprint database may be developed by receiving a depth map for a location in a path network, and then identifying physical structures within the depth map. The depth map may be divided, at each physical structure, into one or more horizontal planes at one or more elevations from a road level. Two-dimensional feature geometries may be extracted from the horizontal planes. At least a portion of the extracted feature geometries may be encoded into the fingerprint database.

Abstract: Systems, methods, and apparatuses are described for predicting the placement of road signs. A device receives data depicting road signs from multiple vehicles. The device analyzes a detected placement of the road signs and at least one characteristic of a collection of the data. The characteristic describes the road upon which the data was collected, an operation of the vehicle from which the data was collected, or an environment in which the data was collected. The device generates a model that associates values for the detected placement of the road signs with values for the at least one characteristic. The model may be later accessed to interpret subsequent sets of data describing one or more road signs.

Abstract: An approach is provided for providing map independent location-based notifications. The location-based notification platform causes an initiation of at least one spatial event request for one or more location-based notifications. The at least one spatial event request specifies at least one bounded area in which at least one device is located or to arrive. The location-based notification platform in response to the at least one spatial event request, receives at least one spatial polygon. The at least one spatial polygon represents at least one geographic area that is determined based on the at least one bounded area, and the at least one spatial polygon is associated with the one or more location-based notifications or one or more triggers for the one or more location-based notifications. The location-based notification platform then causes an overlay of the at least one spatial polygon on at least one travel network traveled by the at least one device.

Abstract: A traffic adjustment or a traffic command, for example, is based on data congestion on a cellular network. Position data is received from a mobile device. A portion of a cellular network that corresponds to the position data is selected. The portion of the cellular network may be the geographic area covered by a cell. The network state of the portion of the cellular network is identified. A traffic device is also identified. The traffic device may be a traffic light or a variable speed sign. The traffic adjustment or traffic command for the traffic device is calculated based on the network state.

Abstract: Systems, apparatuses, and methods are provided for determining the geographic location of an end-user device using a multilateration calculation. The end-user device collects a depth map at a location in a path network. Two-dimensional feature geometries are extracted from the depth map, and a number of control points are identified in the extracted feature geometries. Distances are calculated between the end-user device and the identified control points. Location reference information is received for each identified control point from an external database. A geographic location of the end-user device in the path network is determined through a multilateration calculation using the location reference information of the identified control points and the calculated distances between the end-user device and each identified control point.

Abstract: A method, apparatus and computer program products are provided for automatically determining or fine-tuning bus service schedules for bus stops along a route. One example method includes causing reception of GPS data from a plurality of buses or uses from along a transit route, the GPS data comprised of a plurality of location points and associated temporal data, aggregating the location data by route and direction to determine a trip count, causing reception of at least one bus stop location, partitioning the associated temporal data into a number of clusters in accordance with the trip count, calculating a mean arrival time at the at least one bust stop for each cluster, and generating, using a processor, at least one service schedule for the at least one bus stop, wherein the mean arrival time of each cluster represents the service schedule.

Abstract: Systems, apparatuses, and methods are provided for determining the geographic location of an end-user device (e.g., vehicle, mobile phone, smart watch, etc.). An end-user device may collect a depth map at a location in a path network. Feature geometries may be obtained from a fingerprint database in proximity to the location in the path network. The depth map may be oriented with the feature geometries of the fingerprint. Control points from an extracted feature geometry in the depth map may be compared with control points within the fingerprint. Match rates may be calculated based on the comparison, and a geographic location of the end-user device may be determined when an overall match rate exceeds a minimum threshold value.