Abstract: An approach is provided for machine learning-based prediction of an estimated time of arrival (ETA) or any other trip characteristic. The approach involves, for example, receiving a request for an ETA (or any other trip characteristic). The request specifies an origin, a destination, and a time of departure. The approach also involves discretizing the origin to an origin ETA homogenous zone and the destination to a destination ETA homogenous zone. The approach further involves determining one or more features of one or more pre-computed k-shortest paths for an origin-destination (O-D) zone pair comprising the origin ETA homogenous zone and the destination ETA homogenous zone. The approach further involves providing the one or more features as an input to a trained machine learning to predict the ETA of the trip (or any other trip characteristic).

Abstract: An approach is provided for spatial aggregation for location based services. The approach involves, for example, determining a plurality of partitions for a geographic area. The approach also involves determining a set of destinations that is common to a first partition and a second partition of the plurality of partitions. The set of destinations are associated with a plurality of trips originating from first partition, the second partition, or a combination thereof. The approach further involves determining a statistical property of the plurality of trips between any of the set of destinations and the first partition, the second partition, or a combination thereof. The approach further involves merging the first partition with the second partition into the traffic analysis zone based on the statistical property.

Abstract: It is determined whether the number of second probe apparatuses in the vicinity of a first probe apparatus satisfies a volume threshold requirement. Responsive to determining that the volume threshold requirement is satisfied, an instance of individual probe data is generated and provided. Responsive to determining that the volume threshold requirement is not satisfied, it is determined if a first instance of collaborative probe data corresponds to a portion of a trajectory of the first probe apparatus. Responsive to determining that the first instance of collaborative probe data corresponds to the portion of the trajectory, a contribution is added to the first instance of collaborative probe data and the updated first instance of collaborative probe data is provided. Otherwise, a first instance of collaborative probe data is generated that comprises a partial representation of probe data corresponding to the portion of the trajectory of the first probe apparatus.

Abstract: An approach is provided for linearizing a network of features for machine learning tasks. The approach involves, for instance, receiving a graph representation of a network of a plurality of features. For example, a plurality of vertices of the graph representation, an edge connecting two vertices of the plurality of vertices, or a combination thereof respectively represents the plurality of features. The approach also involves determining a linear order of the plurality of features based on a selected criterion. The approach further involves generating a vector representation of the plurality of features based on the linear order. The approach further involves using the vector representation as an input, an output, or a combination thereof of a machine learning model.

Abstract: A method, apparatus and computer program product are provided for generating structured trajectories based on probe data for simulating traffic flow. Methods include: receiving a plurality of sequences of probe data points; identifying splitting points in each of the plurality of sequences representing points where a respective sequence is split and decomposed into a plurality of legs; identifying the plurality of legs between pairs of splitting points; grouping legs according to a hierarchical optimizer into bunches of legs; determining, from the bunches of legs, a map representation of a road network; composing bunches of legs into a directed graph based on leg continuations, where the directed graph is formed by bunches of legs connected to continuation bunches of legs, and graph nodes of the directed graph represent intersection decision points; and simulating a condition including traffic flow within the road network based on decisions made at the intersection decision points.

Abstract: A method, apparatus and computer program product are provided for generating structured trajectories based on probe data while maintaining privacy and user information, and generating a map representation of a road network from the structured trajectories. Methods may include: receiving a plurality of trajectories of probe data points from a plurality of probe apparatuses; identifying splitting points in each of the plurality of trajectories of probe data points; identifying legs of the plurality of trajectories of probe data points between pairs of splitting points; assigning legs into bunches of legs; searching a solution space determined by leg bunch assignments; determining, from the bunches of legs, a selected solution representing a map of a road network; and facilitating at least one of navigational assistance or at least semi-autonomous vehicle control using the map of the road network.

Abstract: A method, apparatus and computer program product are provided for generating structured trajectories based on probe data while maintaining privacy and user information. Methods may include: receiving a plurality of sequences of probe data points from a plurality of probe apparatuses; identifying splitting points in each of the plurality of sequences of probe data points; identifying legs of the plurality of sequences of probe data points between pairs of splitting points; grouping legs within a predefined degree of similarity into bunches of legs; performing a guided search of a solution space containing the bunches of legs by performing successive mutations on candidate solutions in the solution space to identify a solution satisfying a fitness metric threshold; and identifying, from the solution satisfying a fitness metric threshold, a road network.

Abstract: An approach is provided for traffic estimation/detection based on anomaly detection. The approach involves, for instance, retrieving probe data or other sensor data collected from sensors of devices traveling in a geographic area. The approach also involves aggregating the probe or sensor data into a sequence of frames. Each frame comprises a plurality of spatial cells representing the geographic area at a respective time interval. The approach further involves computing a similarity of the sequence to one or more historical sequences comprising historical frames of spatially and temporally binned historical probe data. The approach further involves determining a classification of a traffic state associated with the probe or sensor data as either a normal traffic state or as a traffic anomaly based on the similarity. By way of example, the traffic state of the probe data can then be estimated/predicted based on the classification.

Abstract: An approach is provided for end-to-end traffic estimation. The approach involves, for instance, retrieving probe data or other sensor data collected from sensors of devices traveling in a geographic area. The approach also involves optionally aggregating the probe or sensor data into a sequence of frames. Each frame comprises a plurality of spatial cells representing the geographic area at a respective time interval. The probe or sensor data is spatially and temporally binned into the spatial cells. The approach further involves initiating an offline pre-processing pipeline to associate the probe or sensor data with road segments of a geographic database and/or otherwise determining a ground-truth traffic state for each frame or sensor data. The approach further involves training a machine learning model using the ground-truth traffic state to determine a predicted traffic state directly from input frames or sensor data.

Abstract: An approach is provided for compression of sparse data for machine learning or equivalent tasks. The approach involves, for instance, receiving data that is binned into a plurality of bins. The data, for instance, represents a spatial surface such as a geographic region. The approach also involves processing the data by applying a compression criterion to classify one or more bins of the plurality of bins as either data-containing bins or empty bins. The approach further involves establishing a space filling curve over the plurality of bins, wherein the space filling curve linearizes the plurality of bins according to a placement order. The approach further involves storing the data-containing bins of the plurality of bins in a compressed data structure based on the placement order of the space filling curve.

Abstract: It is determined whether the number of second probe apparatuses in the vicinity of a first probe apparatus satisfies a volume threshold requirement. Responsive to determining that the volume threshold requirement is satisfied, an instance of individual probe data is generated and provided. Responsive to determining that the volume threshold requirement is not satisfied, it is determined if a first instance of collaborative probe data corresponds to a portion of a trajectory of the first probe apparatus. Responsive to determining that the first instance of collaborative probe data corresponds to the portion of the trajectory, a contribution is added to the first instance of collaborative probe data and the updated first instance of collaborative probe data is provided. Otherwise, a first instance of collaborative probe data is generated that comprises a partial representation of probe data corresponding to the portion of the trajectory of the first probe apparatus.

Abstract: It is determined whether the number of second probe apparatuses in the vicinity of a first probe apparatus satisfies a volume threshold requirement. Responsive to determining that the volume threshold requirement is satisfied, an instance of individual probe data is generated and provided. Responsive to determining that the volume threshold requirement is not satisfied, it is determined if a first instance of collaborative probe data corresponds to a portion of a trajectory of the first probe apparatus. Responsive to determining that the first instance of collaborative probe data corresponds to the portion of the trajectory, a contribution is added to the first instance of collaborative probe data and the updated first instance of collaborative probe data is provided. Otherwise, a first instance of collaborative probe data is generated that comprises a partial representation of probe data corresponding to the portion of the trajectory of the first probe apparatus.

Abstract: It is determined whether the number of second probe apparatuses in the vicinity of a first probe apparatus satisfies a volume threshold requirement. Responsive to determining that the volume threshold requirement is satisfied, an instance of individual probe data is generated and provided. Responsive to determining that the volume threshold requirement is not satisfied, it is determined if a first instance of collaborative probe data corresponds to a portion of a trajectory of the first probe apparatus. Responsive to determining that the first instance of collaborative probe data corresponds to the portion of the trajectory, a contribution is added to the first instance of collaborative probe data and the updated first instance of collaborative probe data is provided. Otherwise, a first instance of collaborative probe data is generated that comprises a partial representation of probe data corresponding to the portion of the trajectory of the first probe apparatus.