Abstract: An approach is provided for resolving an inconsistency in road closure data stored in a mapping platform. The approach, for example, involves processing map data to generate a roadway graph representing a spatial relationship between a first road segment and a second road segment. The spatial relationship indicates that a first closure state of the first road segment cannot differ from a second closure state of the second road segment. The approach also involves determining that the inconsistency in the road closure data for the first road segment and the second road segment indicates that first closures state and the second closure state do not match. The approach further involves changing the road closure data stored in the mapping platform either to match the first road closure state with the second road closure state, or to match the second road closure state with the first road closure state in response to the inconsistency.

Abstract: An approach is provided for automatically verifying a road closure report. The approach, for example, involves determining one or more features of probe data collected from a plurality of vehicles traveling on a connected set of road links of a closure link graph. The closure link graph, for instance, comprises a road link indicated by the road closure report, one or more upstream links from the road link, one or more downstream links from the road link, or a combination thereof. The approach also involves evaluating a closure probability of the road link indicated by the road closure report based on the one or more features. The road closure report is then automatically verified based on the closure probability.

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 providing probe anomaly detection. The approach, for example, involves determining an expected vehicle volume for a geographic region over a time epoch. The expected vehicle volume is calculated from historical vehicle volume data. The approach also involves determining an actual vehicle volume for the geographic region over the time epoch. The actual vehicle volume is based on current vehicle volume data collected from a plurality of vehicles traveling in the geographic region. The approach further involves computing a vehicle volume change based on the expected vehicle volume and the actual vehicle volume. The approach then involves providing the vehicle volume change as an indicator of a probe anomaly in the geographic region.

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 automatically detecting a road closure during a probe anomaly. The approach, for example, involves determining a drop in an expected vehicle volume in a geographic area over a time epoch, wherein the expected vehicle volume represents an expected number of unique vehicles traveling in the geographic area over the time epoch. The approach also involves computing an adjusted expected vehicle volume for a road segment in the geographic area based on the drop and a historical expected vehicle volume for the road segment over the time epoch. The approach further involves determining a time window comprising at least the time epoch. The approach further involves performing an automatic road closure detection on the road segment using the time window.

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 using pedestrian probe data for ridesharing. The approach involves, for example, receiving pedestrian probe data from a search zone associated with a designated location. The pedestrian probe data is collected, for instance, from one or more location sensors of at least one device associated with at least one pedestrian. The approach also involves processing the pedestrian probe data to determine one or more pedestrian paths terminating at the designated location. The approach further involves identifying at least one common origin, at least one common destination, or a combination thereof of the one or more pedestrian paths and providing the least one common origin, the at least one common destination, or a combination thereof as an output.

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 report. The approach, for example, involves determining one or more features of probe data collected from a plurality of vehicles traveling on a connected set of road links of a closure link graph. The closure link graph, for instance, comprises a road link indicated by the road closure report, one or more upstream links from the road link, one or more downstream links from the road link, or a combination thereof. The approach also involves evaluating a closure probability of the road link indicated by the road closure report based on the one or more features. The road closure report is then automatically verified based on the closure probability.

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 combining discontinuous road closures detected in a road network. The approach, for example, involves retrieving a roadway graph including one or more open segments and at least two discontinuous closed segments. The approach also involves computing an importance weight for road links of the segments of the roadway graph. The importance weight is based on one or more attributes of the road links. The approach further involves computing a closure confidence score for the one or more open segments and/or the at least two closed segments based on the importance weight and a link closure confidence score for each link. The approach further involves changing or not changing a road closure state of the open and/or closed segments based on the closure confidence score and a minimum distance threshold between the at least two closed segments.

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 detecting a road closure during a probe anomaly. The approach, for example, involves determining a drop in an expected vehicle volume in a geographic area over a time epoch, wherein the expected vehicle volume represents an expected number of unique vehicles traveling in the geographic area over the time epoch. The approach also involves computing an adjusted expected vehicle volume for a road segment in the geographic area based on the drop and a historical expected vehicle volume for the road segment over the time epoch. The approach further involves determining a time window comprising at least the time epoch. The approach further involves performing an automatic road closure detection on the road segment using the time window.

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 automatically verifying a road closure report. The approach, for example, involves determining one or more features of probe data collected from a plurality of vehicles traveling on a connected set of road links of a closure link graph. The closure link graph, for instance, comprises a road link indicated by the road closure report, one or more upstream links from the road link, one or more downstream links from the road link, or a combination thereof. The approach also involves evaluating a closure probability of the road link indicated by the road closure report based on the one or more features. The road closure report is then automatically verified based on the closure probability.

Abstract: An approach is provided for providing probe anomaly detection. The approach, for example, involves determining an expected vehicle volume for a geographic region over a time epoch. The expected vehicle volume is calculated from historical vehicle volume data. The approach also involves determining an actual vehicle volume for the geographic region over the time epoch. The actual vehicle volume is based on current vehicle volume data collected from a plurality of vehicles traveling in the geographic region. The approach further involves computing a vehicle volume change based on the expected vehicle volume and the actual vehicle volume. The approach then involves providing the vehicle volume change as an indicator of a probe anomaly in the geographic region.