公开(公告)号：US20230067464A1

公开(公告)日：2023-03-02

申请号：US17410624

申请日：2021-08-24

Applicant: HERE Global B.V.

Inventor： David JONIETZ ,  Michael KOPP ,  Moritz NEUN ,  Bo XU ,  Ali SOLEYMANI

IPC: G08G1/01 ,  G06N20/00 ,  G06K9/62

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.

公开(公告)号：US20240087448A1

公开(公告)日：2024-03-14

申请号：US17941580

申请日：2022-09-09

Applicant: HERE Global B.V.

Inventor： Rohit GUPTA ,  David JONIETZ ,  Bo XU ,  Ali SOLEYMANI ,  Reinhard Walter KÖHN

IPC: G08G1/01 ,  G06F16/29 ,  G06K9/62 ,  G08G1/017

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.

公开(公告)号：US20240085205A1

公开(公告)日：2024-03-14

申请号：US17941607

申请日：2022-09-09

Applicant: HERE Global B.V.

Inventor： David JONIETZ ,  Bo XU ,  Rohit GUPTA ,  Ali SOLEYMANI ,  Reinhard Walter KÖHN

IPC: G01C21/34 ,  G06N5/02

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).

公开(公告)号：US20230160705A1

公开(公告)日：2023-05-25

申请号：US17533877

申请日：2021-11-23

Applicant: HERE Global B.V.

Inventor： Bo XU ,  Rohit GUPTA ,  David JONIETZ ,  Ali SOLEYMANI

IPC: G01C21/34 ,  G06N5/04 ,  G06K9/62

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.

公开(公告)号：US20220292091A1

公开(公告)日：2022-09-15

申请号：US17197974

申请日：2021-03-10

Applicant: HERE Global B.V.

Inventor： Catalin CAPOTA ,  David JONIETZ ,  Ali SOLEYMANI ,  Bo XU ,  Moritz NEUN

IPC: G06F16/2458 ,  G06F16/29

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.