METHOD, APPARATUS, AND SYSTEM FOR TRAFFIC ESTIMATION BASED ON ANOMALY DETECTION

    公开(公告)号:US20230066501A1

    公开(公告)日:2023-03-02

    申请号:US17410698

    申请日:2021-08-24

    Inventor: David JONIETZ

    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.

    METHOD AND APPARATUS FOR GENERATING STRUCTURED TRAJECTORIES FROM GEOSPATIAL OBSERVATIONS

    公开(公告)号:US20230137263A1

    公开(公告)日:2023-05-04

    申请号:US17452822

    申请日:2021-10-29

    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.

    METHOD AND APPARATUS FOR GENERATING STRUCTURED TRAJECTORIES FROM GEOSPATIAL OBSERVATIONS

    公开(公告)号:US20230135578A1

    公开(公告)日:2023-05-04

    申请号:US17452821

    申请日:2021-10-29

    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.

    METHOD AND APPARATUS FOR GENERATING STRUCTURED TRAJECTORIES FROM GEOSPATIAL OBSERVATIONS

    公开(公告)号:US20230132499A1

    公开(公告)日:2023-05-04

    申请号:US17452820

    申请日:2021-10-29

    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.

    METHOD, APPARATUS, AND SYSTEM FOR END-TO-END TRAFFIC ESTIMATION FROM MINIMALLY PROCESSED INPUT DATA

    公开(公告)号:US20230067464A1

    公开(公告)日:2023-03-02

    申请号:US17410624

    申请日:2021-08-24

    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.

    METHOD AND APPARATUS FOR SPATIAL AGGREGATION FOR LOCATION-BASED SERVICES

    公开(公告)号:US20240087448A1

    公开(公告)日:2024-03-14

    申请号:US17941580

    申请日:2022-09-09

    CPC classification number: G08G1/0133 G06F16/29 G06K9/6262 G08G1/017

    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.

    METHOD AND APPARATUS FOR MACHINE LEARNING-BASED PREDICTION OF AN ESTIMATED TIME OF ARRIVAL

    公开(公告)号:US20240085205A1

    公开(公告)日:2024-03-14

    申请号:US17941607

    申请日:2022-09-09

    CPC classification number: G01C21/3484 G06N5/022

    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).

    METHOD, APPARATUS, AND SYSTEM FOR LINEARIZING A NETWORK OF FEATURES FOR MACHINE LEARNING TASKS

    公开(公告)号:US20230160705A1

    公开(公告)日:2023-05-25

    申请号:US17533877

    申请日:2021-11-23

    CPC classification number: G01C21/3461 G06N5/04 G06K9/6256 G06K9/6277

    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.

    METHOD, APPARATUS, AND SYSTEM FOR COMPRESSION OF SPARSE DATA FOR MACHINE LEARNING TASKS

    公开(公告)号:US20220292091A1

    公开(公告)日:2022-09-15

    申请号:US17197974

    申请日:2021-03-10

    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.

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