公开(公告)号：US20240142245A1

公开(公告)日：2024-05-02

申请号：US17977679

申请日：2022-10-31

Applicant: HERE Global B.V.

Inventor： Basel HASHISHO ,  Bo XU ,  Rohit GUPTA ,  Reinhard KOHN ,  Sebastian Hendrik VAN DE HOEF

IPC: G01C21/34 ,  G01C21/00

CPC classification number: G01C21/3453 ,  G01C21/3874

Abstract: An approach is provided for stop classification and journey extraction from vehicle location trace data. The approach involves, for example, processing vehicle location trace data to determine a sequence of vehicle stop locations. The sequence of vehicle stop locations comprises a first stop location, a second stop location, and a third stop location in chronological order. The approach also involves determining a first route cost (e.g., a first route length) from the first stop location to the third stop location via the second stop location and a second route cost (e.g., a second route length) from the first stop location directly to the third stop location. The approach further involves determining a classification of the second stop location as either a task stop or a rest stop based, at least in part, on a comparison of the first route length and the second route length.

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.