Abstract: An approach is provided for synchronizing traffic flow and/or traffic signals in alternate travel segments. The approach involves determining traffic congestion information for at least one travel segment that is classified in at least one first functional class. The approach also involves processing and/or facilitating a processing of the traffic congestion information to determine at least one alternate travel segment that is classified in at least one second functional class. The approach further involves determining at least one calculated traffic flow for the at least one alternate travel segment based, at least in part, on traffic flow information associated with the at least one first functional class. The approach also involves causing, at least in part, a configuration of one or more traffic signaling parameters for the at least one alternate travel segment based, at least in part, on the calculated traffic flow.

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: 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.

Abstract: An approach is provided for synchronizing traffic flow and/or traffic signals in alternate travel segments. The approach involves determining traffic congestion information for at least one travel segment that is classified in at least one first functional class. The approach also involves processing and/or facilitating a processing of the traffic congestion information to determine at least one alternate travel segment that is classified in at least one second functional class. The approach further involves determining at least one calculated traffic flow for the at least one alternate travel segment based, at least in part, on traffic flow information associated with the at least one first functional class. The approach also involves causing, at least in part, a configuration of one or more traffic signaling parameters for the at least one alternate travel segment based, at least in part, on the calculated traffic flow.

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 and selecting feature geometries for determining the geographic location of a device. A device collects a depth map at a location in a path network. Two-dimensional feature geometries from the depth map are extracted using a processor of the device. The extracted feature geometries are ranked to provide ranking values for the extracted feature geometries. A portion of the extracted feature geometries are selected based upon the ranking values and a geographic distribution of the extracted feature geometries.

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: 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, apparatuses, and methods are provided for developing a fingerprint database and extracting feature geometries for determining the geographic location of an end-user device. A device collects, or a processor receives, a depth map of a location in a path network. A physical structure is identified within the depth map. The depth map is divided, at the physical structure, into a horizontal plane at an elevation from the road level. A two-dimensional feature geometry is extracted from the horizontal plane of the depth map using a linear regression algorithm, a curvilinear regression algorithm, or a machine learning algorithm.

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: 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: In an embodiment, a system receives mobile device data from a plurality of mobile devices. The system compares the mobile device data with cartographic data representing a structure and determines a structure access characteristic based on the comparison. The structure access characteristic may be the location of an entrance or exit to the structure. The structure access characteristic may also be a structure access type, a structure access status, a structure access schedule, or any other structure access characteristic.

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: A method and apparatus for predicting the arrival time of a transit vehicle at a transit stop of a transit route is presented. The arrival time is predicted using historical location information of a plurality of vehicles that have previously traveled the route, location information of several of the most recent vehicles that have arrived at the stop, and current location information of a particular vehicle currently traveling the route. Values such as average arrival times and arrival time errors or variances are determined from the historical and recent location data. These values are used with the current location information to predict the arrival time of the particular vehicle.

Abstract: In one embodiment, a mobile device or a network device is configured to identify when a transit vehicle deviates from a transit path. The mobile device is configured to perform a positioning technique to generate data indicative of the location of a mobile device. Based on the location of the mobile device, a path is identified. The path is associated with an estimated path width based on the classification of the path and/or the accuracy of the positioning technique. A target route is calculated using the estimated path width. As the transit vehicle travels, the target route is compared to the location of the mobile device. If the mobile device and or transit vehicle deviates from the target route, a message is generated. The message may indicate that the transit vehicle is being re-routed and/or recommends the computation of a new path.

Abstract: In one embodiment, a mobile device or a network device is configured to identify when a transit vehicle deviates from a transit path. The mobile device is configured to perform a positioning technique to generate data indicative of the location of a mobile device. Based on the location of the mobile device, a path is identified. The path is associated with an estimated path width based on the classification of the path and/or the accuracy of the positioning technique. A target route is calculated using the estimated path width. As the transit vehicle travels, the target route is compared to the location of the mobile device. If the mobile device and or transit vehicle deviates from the target route, a message is generated. The message may indicate that the transit vehicle is being re-routed and/or recommends the computation of a new path.

Abstract: A method, apparatus and computer program products are provided for automatically detecting specific locations, i.e. bus stops, stop lights, and/or traffic signals, based on received GPS reports. The method can also be adopted to detect the utilization of the specific locations along the route. One example method includes receiving GPS data from a plurality of buses from along a transit route, and utilizes a machine learning classification strategy that captures the mobility patterns of the GPS equipped buses, at specific locations. The method may then generate mini-clusters, each comprised of a first location point from a first route and one or more subsequent location points located within a predetermined distance of the first location point. The mobility patterns of the mini-clusters within larger clusters are represented as a normalized histogram where the bin values become classification features. A machine learning model is then utilized to determine a location of the specific locations.

Abstract: A method, apparatus and computer program products are provided for collecting and analyzing route to determine the routes of a transit system. One example method includes receiving GPS data from a plurality of buses or uses from along a transit roué, the discreet GPS reports organized into a set, generating a plurality of clusters, each cluster is comprised of a first GPS point from a first set and one or more subsequent GPS points, each subsequent GPS point from a different set, and wherein each subsequent GPS point is located within a predetermined distance of the first GPS point. The clusters are then connected to form a route polylines. Outliers, inaccuracies, and/or route endpoints may be pruned to improve accuracy and efficiency of the method. The method may also order the aggregated clusters using spatial and temporal data of the GPS reports to improve accuracy.

Abstract: A method and apparatus for predicting the arrival time of a transit vehicle at a transit stop of a transit route is presented. The arrival time is predicted using historical location information of a plurality of vehicles that have previously traveled the route, location information of several of the most recent vehicles that have arrived at the stop, and current location information of a particular vehicle currently traveling the route. Values such as average arrival times and arrival time errors or variances are determined from the historical and recent location data. These values are used with the current location information to predict the arrival time of the particular vehicle.