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 identifying objects present in mesh representation of a geo-location, generating accurate 3D models for the objects, and aligning the 3D models to their corresponding objects in an application. The approach comprises processing and/or facilitating a processing of textured three-dimensional mesh data in one or more regions of interest to cause, at least in part, a generation of at least one two-dimensional depth image representation. The approach further comprises causing, at least in part, a filtering of the textured three-dimensional mesh data in the one or more regions of interest to remove mesh data below at least one threshold height based, at least in part, on the at least one two-dimensional depth image representation. Additionally, the approach comprises processing and/or facilitating a processing of the filtered textured three-dimensional mesh data to cause, at least in part, a generation of at least one partial three-dimensional model, including one or more upper facades above the at least one threshold height, of one or more objects located within the one or more regions of interest.
Abstract:
An approach is provided for processing and/or facilitating a processing of probe trace data to determine one or more mode indicators, wherein the one or more mode indicators include, at least in part, one or more attributes of the probe trace data. The approach involves causing, at least in part, a modeling of one or more statistical patterns of at least one pedestrian mode of transport, at least one non-pedestrian mode of transport, or a combination thereof based, at least in part, on determining one or more probabilities that one or more mode indicators are associated with the at least one pedestrian mode of transport, the at least one non-pedestrian mode of transport, or a combination thereof. The approach also involves causing, at least in part, a classification of other probe trace data as being associated with the at least one pedestrian mode of transport or the at least one non-pedestrian mode of transport based, at least in part, on the one or more mode indicators that are associated with the other probe trace data and the one or more statistical patterns.
Abstract:
In one implementation, sets of probe data are collected by probe vehicles. The probe data describes the driving characteristics of the probe vehicles. The sets of probe data are sent to a server or a mobile device for analysis. A polycurve, including a piecewise function of map data, is modified based on the probe data. The polycurve may be a spline curve for an advanced driver assistance system. The modified polycurve may be used in the advanced driver assistance system for a vehicle traveling along the same path previously traversed by the probe vehicles. Based on the polycurve modified by the probe data, a driver assistance feature is provided to the vehicle.
Abstract:
In one implementation, sets of probe data are collected by probe vehicles. The probe data describes the driving characteristics of the probe vehicles. The sets of probe data are sent to a server or a mobile device for analysis. A polycurve, including a piecewise function of map data, is modified based on the probe data. The polycurve may be a spline curve for an advanced driver assistance system. The modified polycurve may be used in the advanced driver assistance system for a vehicle traveling along the same path previously traversed by the probe vehicles. Based on the polycurve modified by the probe data, a driver assistance feature is provided to the vehicle.
Abstract:
An approach is provided for sharing annotations and recalling geospatial information. The approach involves processing and/or facilitating a processing of communication information exchanged between a plurality of devices engaged in a communication session to cause, at least in part, a parsing of geospatial information from the communication information. The approach also involves determining whether the geospatial information meet, at least in part, one or more logic thresholds. The one or more logic thresholds are for determining a potential relevance of the geospatial information to the communication session, the plurality of devices, one or more users of the plurality of devices, or a combination thereof. The approach further involves causing, at least in part, a presentation of the geospatial information to the plurality of devices, the one or more users, or a combination thereof based, at least in part, on the determination.
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 objects that are present at a geo-location and providing an uncluttered presentation of images of some of the objects in an application such as a map application. The approach includes determining one or more regions of interest associated with at least one geo-location, wherein the one or more regions of interest are at least one textured three-dimensional representation of one or more objects that may be present at the at least one geo-location. The approach also includes processing and/or facilitating a processing of the at least one textured three-dimensional representation to determine at least one two-dimensional footprint and three-dimensional geometry information for the one or more objects. The approach further includes causing, at least in part, a generation of at least one two-dimensional image representation of the one or more regions of interest by causing, at least in part, a projection of three-dimensional texture information of the at least one textured three-dimensional representation onto the at least one two-dimensional footprint. The approach also includes causing, at least in part, a classification of the one or more objects based, at least in part, on the at least one two-dimensional image representation and the three-dimensional geometry information.
Abstract:
An approach is provided for predicting starting points and/or ending points for traffic jams in one or more travel segments. The approach involves processing and/or facilitating a processing of probe data associated with at least one travel segment to cause, at least in part, a generation of at least one speed curve with respect to a distance dimension and a time dimension, wherein the probe data includes speed information, and wherein the at least one speed curve indicates at least one previous starting point, at least one previous ending point, or a combination thereof for one or more previous traffic jams based, at least in part, on the speed information. The approach also involves processing and/or facilitating a processing of the at least one previous starting point, the at least one previous ending point, or a combination thereof to determine at least one starting point trend curve, at least one ending point trend curve, or a combination thereof with respect to the distance dimension and the time dimension. The approach further involves determining at least one predicted evolution of at least one starting point, at least one ending point, or a combination thereof for at least one traffic jam in the at least one travel segment based, at least in part, on the at least one starting point trend curve, the at least one ending point trend curve, or a combination thereof.