Abstract: An approach is provided for verifying reported ramp closures. The approach involves, for example, processing a ramp closure report to determine reported ramp link(s) associated with the ramp closure report. The approach also involves constructing a ramp network comprising the reported ramp link(s) and other ramp link(s) connected to the reported ramp link(s). The approach further involves collecting probe data collected from one or more sensors of one or more vehicles traveling within the ramp network. The approach further involves identifying one or more driving patterns based on one or more vehicle paths determined from the probe data. The approach further involves performing a verification of a closure status of the reported ramp link(s) based on the one or more driving patterns. The approach further involves providing the verification of the closure status as an output.

Abstract: An approach is provided for automatic road closure detection. The approach, for example, involves designating a dynamic time window comprising one or more time epochs ending before a current time epoch. The approach also involves retrieving a first set of probe data collected from a road link during the dynamic time window. The approach further involves adjusting a size of the dynamic time window by adding or removing another time epoch ending before the current time epoch until at least one criterion related to the probe data, the dynamic time window, or a combination thereof is met. The approach further involves extracting a plurality of features from the first set of probe data, from a second set of probe data collected from the road link during the current time epoch, or a combination thereof. The approach then involves detecting a closure status of the road link based on the plurality of features.

Abstract: An approach is provided for generating/breaking vehicle paths on a limited graph area. The approach, for example, determining an expected frequency of location data collected from a sensor of a vehicle traveling on a roadway, wherein the location data include a plurality of probe points that are time-sequenced. The approach also involves detecting an exit or an entry of the vehicle on the roadway based on comparing the expected frequency to an observed frequency of the location data on at least one portion of the roadway. The approach further involves initiating an identification, a creation, a breaking, or a combination thereof of a path constructed from the location data based on the detecting of the exit or the entry of the vehicle. The approach further involves providing the path as an output.

Abstract: An approach is provided for detecting/verifying contraflow lane shift incidents using bidirectional lateral distance measurements. The approach involves, for example, for each one of a plurality of vehicle data points travelling at a main direction of a main side of a bi-directional road, determining a first lateral distance to a center of the main side and a second lateral distance to a center of an opposite side of the bi-directional road. The approach also involves, for the plurality of vehicle data points, aggregating the first lateral distance into a main feature and aggregating the second lateral distance into an opposite feature. The approach further involves identifying a lane shift to the opposite side based on the main feature, the opposite feature, or a combination thereof. The approach further involves providing the lane shift as an output.

Abstract: An approach is provided for automatically verifying a road closure using multiple possible vehicle paths between two probe points. The approach involves, for example, processing probe data to determine a possible path of a vehicle over a road graph, wherein the road graph represents a road link and one or more other road links entering or exiting the road link. The approach also involves calculating a path probability for the possible path, wherein the path probability indicates a likelihood that the possible path is a true path of the vehicle over the road graph. The approach further involves assigning a weighted vehicle count to the road link and/or one or more other road links contained in the possible path, wherein the weighted vehicle count is based on the path probability. The approach further involves detecting a traffic anomaly occurring on the road link based on the weighted vehicle count.

Abstract: An approach is provided for verification of a road closure. The approach, for example, involves generating a road graph including probe data associated with a plurality of road links over respective detection dynamic time windows used for detecting a road closure. The approach involves extracting respective verification dynamic time windows for the plurality of road links. the approach involves filling the respective verification dynamic time windows using the probe data. The approach involves initiating a verification of the road closure based on the filled respective verification dynamic time windows.

Abstract: An approach is provided for verification of a road closure. The approach, for example, involves generating a road graph comprising a road links associated with a road closure detected by a road closure detection system. The road closure detection system stores probe data for the road links collected over respective detection dynamic time windows. The approach also involves extracting respective verification dynamic time windows for the road links that are used by a road closure verification system to verify the road closure. The approach further involves filling the respective verification dynamic time windows using the probe data stored by the road closure detection system. The approach further involves initiating a verification of the road closure based on the filled respective verification dynamic time windows.

Abstract: An approach is provided for automatically verifying a road closure using multiple possible vehicle paths between two probe points. The approach involves, for example, processing probe data to determine a possible path of a vehicle over a road graph, wherein the road graph represents a road link and one or more other road links entering or exiting the road link. The approach also involves calculating a path probability for the possible path, wherein the path probability indicates a likelihood that the possible path is a true path of the vehicle over the road graph. The approach further involves assigning a weighted vehicle count to the road link and/or one or more other road links contained in the possible path, wherein the weighted vehicle count is based on the path probability. The approach further involves detecting a traffic anomaly occurring on the road link based on the weighted vehicle count.

Abstract: An approach is provided for verification of a road closure. The approach, for example, involves generating a road graph comprising a road links associated with a road closure detected by a road closure detection system. The road closure detection system stores probe data for the road links collected over respective detection dynamic time windows. The approach also involves extracting respective verification dynamic time windows for the road links that are used by a road closure verification system to verify the road closure. The approach further involves filling the respective verification dynamic time windows using the probe data stored by the road closure detection system. The approach further involves initiating a verification of the road closure based on the filled respective verification dynamic time windows.

Abstract: An approach is provided for automatic road closure detection. The approach, for example, involves designating a dynamic time window comprising one or more time epochs ending before a current time epoch. The approach also involves retrieving a first set of probe data collected from a road link during the dynamic time window. The approach further involves adjusting a size of the dynamic time window by adding or removing another time epoch ending before the current time epoch until at least one criterion related to the probe data, the dynamic time window, or a combination thereof is met. The approach further involves extracting a plurality of features from the first set of probe data, from a second set of probe data collected from the road link during the current time epoch, or a combination thereof. The approach then involves detecting a closure status of the road link based on the plurality of features.

Abstract: An approach is provided for generating/breaking vehicle paths on a limited graph area. The approach, for example, determining an expected frequency of location data collected from a sensor of a vehicle traveling on a roadway, wherein the location data include a plurality of probe points that are time-sequenced. The approach also involves detecting an exit or an entry of the vehicle on the roadway based on comparing the expected frequency to an observed frequency of the location data on at least one portion of the roadway. The approach further involves initiating an identification, a creation, a breaking, or a combination thereof of a path constructed from the location data based on the detecting of the exit or the entry of the vehicle. The approach further involves providing the path as an output.

Abstract: An approach is provided for automatic road closure detection. The approach, for example, involves designating a dynamic time window comprising one or more time epochs ending before a current time epoch. The approach also involves retrieving a first set of probe data collected from a road link during the dynamic time window. The approach further involves adjusting a size of the dynamic time window by adding or removing another time epoch ending before the current time epoch until at least one criterion related to the probe data, the dynamic time window, or a combination thereof is met. The approach further involves extracting a plurality of features from the first set of probe data, from a second set of probe data collected from the road link during the current time epoch, or a combination thereof. The approach then involves detecting a closure status of the road link based on the plurality of features.

Abstract: An approach is provided for automatic detection of vehicle parking, stopping, and/or standing locations (e.g., parking lots, alleys, loading zones, standing zones, etc.). The approach, for example, involves collecting probe data transmitted from a plurality of vehicles traveling on a road segment. The approach also involves processing the probe data to determine a vehicle behavior indicating a vehicle parking location, a vehicle stopping location, a vehicle standing location, or a combination thereof. The approach further involves flagging the road segment as being associated with the vehicle parking location, the vehicle stopping location, the vehicle standing location, or a combination thereof based on the vehicle behavior to update a geographic database.

Abstract: An approach is provided for automatic road closure detection. The approach, for example, involves designating a dynamic time window comprising one or more time epochs ending before a current time epoch. The approach also involves retrieving a first set of probe data collected from a road link during the dynamic time window. The approach further involves adjusting a size of the dynamic time window by adding or removing another time epoch ending before the current time epoch until at least one criterion related to the probe data, the dynamic time window, or a combination thereof is met. The approach further involves extracting a plurality of features from the first set of probe data, from a second set of probe data collected from the road link during the current time epoch, or a combination thereof. The approach then involves detecting a closure status of the road link based on the plurality of features.

Abstract: An approach is provided for determining one or more real-time traffic data series associated with one or more road segments over one or more time intervals. The approach involves causing, at least in part, a determination of one or more differences of the real-time data series with one or more historical models. The approach also involves determining whether the one or more real-time difference exceeds a threshold difference. The approach involves causing, at least in part, a selection of one or more other historical models for the one or more road segments over the one or more time intervals based, at least in part, on a determination that the real-time difference exceeds the threshold difference.

Abstract: An approach is provided for determining one or more varying decay rates associated with one or more road segments. The approach involves causing, at least in part, a decaying of real-time traffic data to historical traffic data associated with the one or more road segments based, at least in part, on the one or more varying decay rates. The approach also involves determining one or more traffic predictions for the one or more road segments based, at least in part, on the decaying of the real-time traffic data to the historical traffic data.

Abstract: An approach is provided for determining one or more real-time traffic data series associated with one or more road segments over one or more time intervals. The approach involves causing, at least in part, a determination of one or more differences of the real-time data series with one or more historical models. The approach also involves determining whether the one or more real-time difference exceeds a threshold difference. The approach involves causing, at least in part, a selection of one or more other historical models for the one or more road segments over the one or more time intervals based, at least in part, on a determination that the real-time difference exceeds the threshold difference.

Abstract: An approach is provided for determining one or more varying decay rates associated with one or more road segments. The approach involves causing, at least in part, a decaying of real-time traffic data to historical traffic data associated with the one or more road segments based, at least in part, on the one or more varying decay rates. The approach also involves determining one or more traffic predictions for the one or more road segments based, at least in part, on the decaying of the real-time traffic data to the historical traffic data.