Abstract: An approach is provided for detecting road closures programmatically by monitoring probe activity. The approach, for example, involves generating a historical model of expected probe activity on the road segment based on historical probe data. The approach also involves monitoring observed probe activity from the road segment. The approach further involves calculating a probability of the observed probe activity based on the historical model of the expected probe activity. The approach further involves determining the closure status of the road segment based on the probability.

Abstract: An approach is provided for next token prediction based on previously observed tokens. The approach involves receiving an observed time series of tokens, wherein each of the tokens represents an observed data pattern. The approach also involves adding a most recent token from the observed time series of tokens into a variable token set. The approach further involves processing a historical token set to determine a historical token sequence comprising the variable token set followed by a next token. The approach further involves recursively adding a next most recent token from the observed time series of tokens into the variable token set for processing until the next token following the variable token set in the determined historical token sequence is unique or meets a target number of possible predictions. The approach further involves presenting the next token as a predicted next token of the observed time series of tokens.

Abstract: An approach is provided for semantic-free traffic prediction. The approach involves dividing a travel-speed data stream into a plurality of travel-speed patterns. The travel-speed data stream represents vehicle travel speeds occurring in a road network. The approach also involves representing each of the plurality of travel-speed patterns by a respective token. The respective token is selected from a dictionary of tokens representing a plurality of travel-speed templates determined from historical travel-speed data. The approach further involves matching a sequence of the respective tokens corresponding to said each of the plurality of travel-speed patterns to a best-fit sequence of tokens determined from the historical travel-speed data. The approach further involves determining a predicted sequence of tokens based on the best-fit sequence of tokens, and generating a traffic prediction for the road network based on the predicted sequence of tokens.

Abstract: A method, apparatus, and computer program product are described herein for determining pedestrian behavior profiles for road segments of a road network, from those pedestrian behavior profiles, determining the likelihood that an adverse pedestrian event will occur, and determining the action to be taken in response. Example embodiments may provide a mapping system including: a memory having map data; and processing circuitry. The processing circuitry may be configured to: receive data points associated with pedestrian movement; associate pedestrian movement with a road segment; determine, based on the data points, a pedestrian behavior profile for the road segment; and in response to the pedestrian behavior profile for the road segment indicating a likelihood for an adverse pedestrian event that satisfies a predetermined likelihood, cause at least one action in response thereto.

Abstract: An approach is provided for a localized link-centric metric for directional traffic propagation. The approach, for instance, involves designating a base link of the road network. The approach also involves determining a plurality of vehicle trajectories that pass through the base link. The plurality of vehicle trajectories is based on probe data collected from one or more sensors of a plurality of vehicles travelling in the road network. The approach further involves determining a frequency at which the plurality of vehicle trajectories passes through the base link to each of one or more other links in the plurality of vehicle trajectories within a proximity threshold. The approach further involves computing a link-centric metric for said each of the one or more other links relative to the base link based on the determined frequency.

Abstract: An approach is provided for detecting road closures programmatically by monitoring probe activity. The approach, for example, involves generating a historical model of expected probe activity on the road segment based on historical probe data. The approach also involves monitoring observed probe activity from the road segment. The approach further involves calculating a probability of the observed probe activity based on the historical model of the expected probe activity. The approach further involves determining the closure status of the road segment based on the probability.

Abstract: An approach is provided for a localized link-centric metric for directional traffic propagation. The approach, for instance, involves designating a base link of the road network. The approach also involves determining a plurality of vehicle trajectories that pass through the base link. The plurality of vehicle trajectories is based on probe data collected from one or more sensors of a plurality of vehicles travelling in the road network. The approach further involves determining a frequency at which the plurality of vehicle trajectories passes through the base link to each of one or more other links in the plurality of vehicle trajectories within a proximity threshold. The approach further involves computing a link-centric metric for said each of the one or more other links relative to the base link based on the determined frequency.

Abstract: A method, apparatus, and computer program product are described herein for determining pedestrian behavior profiles for road segments of a road network, from those pedestrian behavior profiles, determining the likelihood that an adverse pedestrian event will occur, and determining the action to be taken in response. Example embodiments may provide a mapping system including: a memory having map data; and processing circuitry. The processing circuitry may be configured to: receive data points associated with pedestrian movement; associate pedestrian movement with a road segment; determine, based on the data points, a pedestrian behavior profile for the road segment; and in response to the pedestrian behavior profile for the road segment indicating a likelihood for an adverse pedestrian event that satisfies a predetermined likelihood, cause at least one action in response thereto.

Abstract: An approach is provided for next token prediction based on previously observed tokens. The approach involves receiving an observed time series of tokens, wherein each of the tokens represents an observed data pattern. The approach also involves adding a most recent token from the observed time series of tokens into a variable token set. The approach further involves processing a historical token set to determine a historical token sequence comprising the variable token set followed by a next token. The approach further involves recursively adding a next most recent token from the observed time series of tokens into the variable token set for processing until the next token following the variable token set in the determined historical token sequence is unique or meets a target number of possible predictions. The approach further involves presenting the next token as a predicted next token of the observed time series of tokens.

Abstract: An approach is provided for semantic-free traffic prediction. The approach involves dividing a travel-speed data stream into a plurality of travel-speed patterns. The travel-speed data stream represents vehicle travel speeds occurring in a road network. The approach also involves representing each of the plurality of travel-speed patterns by a respective token. The respective token is selected from a dictionary of tokens representing a plurality of travel-speed templates determined from historical travel-speed data. The approach further involves matching a sequence of the respective tokens corresponding to said each of the plurality of travel-speed patterns to a best-fit sequence of tokens determined from the historical travel-speed data. The approach further involves determining a predicted sequence of tokens based on the best-fit sequence of tokens, and generating a traffic prediction for the road network based on the predicted sequence of tokens.

Abstract: An approach is provided for classifying a traffic jam from probe data. The approach involves receiving the probe data that is map-matched to a roadway on which the traffic jam is detected. The probe data is collected from one or more vehicles traveling the roadway. The approach also involves determining a jam area of the roadway based on the probe data. The jam area corresponds to one or more segments of the roadway affected by the traffic jam. The approach further involves determining a set of features indicated by the probe data from a portion of the probe data collected from the jam area. The approach further involves classifying, using a machine learning classifier, the traffic jam as either a recurring traffic jam or a non-recurring traffic jam based on the set of features.

Abstract: An approach is provided for classifying a traffic jam from probe data. The approach involves receiving the probe data that is map-matched to a roadway on which the traffic jam is detected. The probe data is collected from one or more vehicles traveling the roadway. The approach also involves determining a jam area of the roadway based on the probe data. The jam area corresponds to one or more segments of the roadway affected by the traffic jam. The approach further involves determining a set of features indicated by the probe data from a portion of the probe data collected from the jam area. The approach further involves classifying, using a machine learning classifier, the traffic jam as either a recurring traffic jam or a non-recurring traffic jam based on the set of features.