公开(公告)号：US20180240026A1

公开(公告)日：2018-08-23

申请号：US15439622

申请日：2017-02-22

Applicant: HERE Global B.V.

Inventor： Davide PIETROBON ,  Andrew LEWIS ,  Jane MACFARLANE

IPC: G06N5/04 ,  G06N99/00 ,  G08G1/01

CPC classification number: G06N20/00 ,  G08G1/0112 ,  G08G1/0129 ,  G08G1/0133 ,  G08G1/0141

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.

公开(公告)号：US20190189001A1

公开(公告)日：2019-06-20

申请号：US15842433

申请日：2017-12-14

Applicant: HERE GLOBAL B.V.

Inventor： Evan SMOTHERS ,  Antonio HARO ,  Andrew LEWIS ,  Davide PIETROBON

IPC: G08G1/01 ,  G08G1/08 ,  G08G1/0967

CPC classification number: G08G1/0104 ,  G08G1/08 ,  G08G1/096716 ,  G08G1/09675 ,  G08G1/096775

Abstract: An approach is provided for a localized link-centric metric for directional traffic propagation. The approach, for instance, involves designating a base link of the road network. The approach also involves determining a plurality of vehicle trajectories that pass through the base link. The plurality of vehicle trajectories is based on probe data collected from one or more sensors of a plurality of vehicles travelling in the road network. The approach further involves determining a frequency at which the plurality of vehicle trajectories passes through the base link to each of one or more other links in the plurality of vehicle trajectories within a proximity threshold. The approach further involves computing a link-centric metric for said each of the one or more other links relative to the base link based on the determined frequency.

公开(公告)号：US20180350232A1

公开(公告)日：2018-12-06

申请号：US15993204

申请日：2018-05-30

Applicant: HERE GLOBAL B.V.

Inventor： Davide PIETROBON ,  Andrew LEWIS ,  Jane MACFARLANE ,  Robert BERRY

IPC: G08G1/01 ,  G06F17/30 ,  G06K9/62

CPC classification number: G06Q10/04 ,  G06Q50/30

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.

公开(公告)号：US20170352262A1

公开(公告)日：2017-12-07

申请号：US15172897

申请日：2016-06-03

Applicant: HERE Global B.V.

Inventor： Bo XU ,  Tiffany BARKLEY ,  Andrew LEWIS ,  Jane MACFARLANE ,  Davide PIETROBON ,  Matei STROILA

IPC: G08G1/01

CPC classification number: G08G1/0133 ,  G08G1/0112

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.