Traffic Classification Based on Spatial Neighbor Model

Patent metadata

Field	Value
Publication number	US-2016267789-A1
Application number	US-201615162001-A
Country	US
Kind code	A1
Filing date	May 23, 2016
Priority date	Dec 1, 2014
Publication date	Sep 15, 2016
Grant date	—

Abstract

Systems, methods, and apparatuses are described for estimating traffic conditions on road segments when no real time traffic data is available. A computing device may access a road topology comprising links from a geographic database. One of the links is selected from road topology. The computing device identifies a subset of the road topology having neighboring links that have an influential conditional probability on the selected link. In one example, the subset of the neighboring links includes parent links for the selected link, child links for the selected link, and parents of child links of the selected link. The computing device generates a traffic estimation model for the selected link using the subset of road topology and historical traffic data for the neighboring links.

First claim

1 . A method comprising:
accessing a road topology comprising links from a geographic database; selecting, using a processor, a link from the road topology; identifying, using the processor, a subset of the road topology having neighboring links that have a significant conditional probability on the selected link; and generating traffic estimation, using the processor, a traffic estimation model for the selected link using the subset of road topology and historical traffic data for the neighboring links an historical traffic data for the selected link, wherein the subset of the road topology includes a Markov blanket for the selected link in the road topology.

2 . The method of claim 1 , wherein the Markov blanket is defined according to a functional classification of the selected link.

3 . A method comprising:
accessing data indicative of a road network from a geographic database; selecting, using a processor, a selected link from the road network; identifying, using the processor, a subset of the road network having neighboring links that have a significant conditional probability on the selected link, wherein the subset of the road network includes at least one neighboring link not adjacent to the selected link; and generating, using the processor, traffic data for the selected link using the subset of the road network and historical traffic data for the neighboring links including the at least one neighboring link not adjacent to the selected link.

4 . The method of claim 3 , wherein the traffic data includes a classification, a category, or a coloring representative of a traffic condition.

5 . The method of claim 3 , wherein the subset of the road network includes link of a same functional classification as the selected link.

6 . The method of claim 3 , further comprising:
identifying a shield link between the selected link and the subset of the road network, wherein the shield link shields the selected link from links that do not have significant impact on the selected link.

7 . The method of claim 3 , further comprising:
providing the traffic data to an assisted driving system.

8 . The method of claim 7 , wherein the assisted driving system includes an advanced driving assistance system (ADAS), a highly assisted driving (HAD) system or an autonomous vehicle.

9 . The method of claim 3 , further comprising:
receiving current traffic data for the neighboring links; and calculating a current traffic level for the selected link based on current traffic data for the neighboring links.

10 . The method of claim 3 , wherein the subset of the road topology includes a Markov blanket for the selected link in the road network.

11 . The method of claim 10 , wherein the Markov blanket is defined according to a functional classification of the selected link.

12 . The method of claim 3 , wherein links of the road network outside of the subset of the road network have a conditional probability with the selected link that is less than a minimum threshold probability.

13 . The method of claim 3 , wherein the significant conditional probability is greater than the minimum threshold probability.

14 . An apparatus comprising:
at least one processor; and at least one memory including computer program code for one or more programs; the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to at least: selecting a selected link from a road network; identifying a subset of the road network having neighboring links that have a significant conditional probability on the selected link, wherein the subset of the road network includes at least one neighboring link not adjacent to the selected link; and generating traffic data for the selected link using the subset of the road network and traffic data for the neighboring links including the at least one neighboring link not adjacent to the selected link.

15 . The apparatus of claim 14 , wherein the traffic data includes a classification, a category, or a coloring representative of a traffic condition.

16 . The apparatus of claim 14 , wherein the subset of the road network includes link of a same functional classification as the selected link.

17 . The apparatus of claim 14 , wherein the road network includes a shield link between the selected link and the subset of the road network, wherein the shield link shields the selected link from links that do not have significant impact on the selected link.

18 . The apparatus of claim 14 , further comprising:
providing the traffic data to an assisted driving system.

19 . The apparatus of claim 18 , wherein the assisted driving system includes an advanced driving assistance system (ADAS), a highly assisted driving (HAD) system or an autonomous vehicle.

20 . The apparatus of claim 14 , wherein the subset of the road topology includes a Markov blanket for the selected link in the road network.

Assignees

• Here Global Bv

Inventors

• Fowe James

Classifications

• G08G1/096827

  where the route is computed onboard · CPC title

• G08G1/0141

  for traffic information dissemination · CPC title

• G08G1/0129

  for creating historical data or processing based on historical data · CPC title

• G08G1/0116

  from roadside infrastructure, e.g. beacons · CPC title

• G08G1/0112

  from the vehicle, e.g. floating car data [FCD] · CPC title