FILTERING ROAD TRAFFIC DATA FROM MULTIPLE DATA SOURCES

• US 20150094941A1
• Filed: 10/09/2014
• Published: 04/02/2015
• Est. Priority Date: 03/03/2006
• Status: Active Grant

• Current Assignee: Inrix Incorporated
• Original Assignee: Inrix Incorporated
• Inventors: Chapman, Craig H., Parikh, Kush G., Downs, Oliver B., Cahn, Robert C., Hersch, Jesse S.
• Granted Patent: US 9,280,894 B2
• Time in Patent Office: Days
• US Class Current: 701/119
• CPC Class Codes: G01C 21/3691 Retrieval, searching and output of information related to real-time traffic, weather, or environmental conditions arrangements for giving variable traffic instructions G08G1/09
• CPC Class Codes: G08G 1/00 Traffic control systems for road vehicles arrangement of road signs or traffic signals E01F9/00 ; automatic vehicle control B62D
• CPC Class Codes: G08G 1/0104 Measuring and analyzing of parameters relative to traffic conditions
• CPC Class Codes: G08G 1/0112 from the vehicle, e.g. floating car data [FCD]
• CPC Class Codes: G08G 1/0116 from roadside infrastructure, e.g. beacons
• CPC Class Codes: G08G 1/012 from other sources than vehicle or roadside beacons, e.g. mobile networks
• CPC Class Codes: G08G 1/0129 for creating historical data or processing based on historical data
• CPC Class Codes: G08G 1/0133 for classifying traffic situation
• CPC Class Codes: G08G 1/0141 for traffic information dissemination
• CPC Class Codes: G08G 1/0968 Systems involving transmission of navigation instructions to the vehicle

Abstract

Techniques are described for assessing road traffic conditions in various ways based on obtained traffic-related data, such as data samples from vehicles and other mobile data sources traveling on the roads and/or from one or more other sources (such as physical sensors near to or embedded in the roads). The road traffic conditions assessment based on obtained data samples may include various filtering and/or conditioning of the data samples, and various inferences and probabilistic determinations of traffic-related characteristics of interest from the data samples. In some situations, the inferences include repeatedly determining current traffic flow characteristics and/or predicted future traffic flow characteristics for road segments of interest during time periods of interest, such as to determine average traffic speed, traffic volume and/or occupancy, and include weighting various data samples in various ways (e.g., based on a latency of the data samples and/or a source of the data samples).

Claims

1-167. -167. (canceled)

168. A computer-implemented method for assessing data samples representing vehicles traveling on roads, the method comprising:

receiving, by one or more computing systems configured to assess traffic information, and for each of one or more road segments of one or more roads, multiple associated data samples that each are reported by one of multiple vehicles and indicate a reported location of the vehicle that corresponds to the road segment; and

for each of at least one of the road segments,

automatically analyzing, by the one or more configured computing systems, the multiple associated data samples for the road segment to determine one or more data samples that are a subset of the multiple data samples and that are unrepresentative of actual vehicle travel on the road segment, at least one of the determined data samples each indicating a reported location for the vehicle reporting the data sample that does not correspond to the actual vehicle travel on the road segment, and at least one of the determined data samples each having an associated heading for the vehicle reporting the data sample that does not correspond to the actual vehicle travel on the road segment; and

providing, by the one or more configured computing systems, one or more indications to exclude the determined data samples from later use, to enable using other of the multiple associated data samples for the road segment in facilitating travel on the road segment.

169. The computer-implemented method of claim 168 wherein, for one of the at least one road segments, the providing of the indications to exclude the determined data samples from later use includes analyzing the associated data samples for the one road segment other than the determined data samples in order to determine an average speed of vehicles traveling on the one road segment, and indicating the determined average speed for use in facilitating travel of other vehicles on the one road segment.

170. The computer-implemented method of claim 168 wherein, for one of the at least one road segments, the providing of the indications to exclude the determined data samples from later use further includes analyzing the associated data samples for the one road segment other than the determined data samples in order to determine traffic flow of vehicles traveling on the one road segment, and indicating the determined traffic flow for use in facilitating travel of other vehicles on the one road segment.

171. The computer-implemented method of claim 168 wherein, for one of the at least one road segments, the determining of the one or more data samples for the one road segment that are unrepresentative of actual vehicle travel on the one road segment includes determining that the reported vehicle locations for those data samples correspond to a section of road that is not of interest for use in representing the actual vehicle travel on the one road segment.

172. The computer-implemented method of claim 171 wherein the section of road that is not of interest is part of a distinct road that is near to the road of the road segment.

173. The computer-implemented method of claim 171 wherein the section of road that is not of interest is a subset of multiple lanes that are part of the road segment.

174. The computer-implemented method of claim 171 wherein the section of road that is not of interest is at least a part of another road segment distinct from the one road segment.

175. The computer-implemented method of claim 168 further comprising, for one of the road segments, automatically determining that the multiple associated data samples for the one road segment are not of interest based at least in part on the one road segment not being of interest, and providing one or more indications to exclude the multiple associated data samples from later use.

176. The computer-implemented method of claim 175 wherein the one road segment is determined to not be of interest based at least in part on a determination of an amount of actual vehicle traffic on the one road segment.

177. The computer-implemented method of claim 168 wherein, for one of the at least one road segments, the determining that the one or more data samples are unrepresentative of actual vehicle travel on the one road segment is based at least in part on the reported locations of the one of more data samples.

178. The computer-implemented method of claim 177 wherein each of the multiple associated data samples for the one road segment indicates a speed of the vehicle reporting the data sample, and wherein the determining that the one or more data samples are unrepresentative is further based at least in part on the speeds indicated by the one or more data samples.

179. The computer-implemented method of claim 177 wherein each of the multiple associated data samples for the one road segment indicates a heading of the vehicle reporting the data sample, and wherein the determining that the one or more data samples are unrepresentative is further based at least in part on the headings indicated by the one or more data samples.

180. The computer-implemented method of claim 179 wherein the one road segment is part of a road that includes vehicles traveling in two opposite directions, wherein the one road segment corresponds to vehicles traveling in one of the two directions, and wherein the determining that one or more data samples for the one road segment are unrepresentative includes determining that data samples whose indicated headings correspond to travel in the other of the two directions are unrepresentative for the one road segment.

181. The computer-implemented method of claim 168 further comprising, for each of at least some of the multiple data samples associated with one of the at least one road segments, estimating a heading associated with the data sample by using reported locations indicated by multiple data samples reported by the vehicle reporting the data sample.

182. The computer-implemented method of claim 168 further comprising receiving one or more indications of a plurality of data samples that each indicate a reported location of a vehicle, and associating each of at least some of the plurality of data samples with at least one of the road segments based at least in part on the reported location of the data sample corresponding to one or more locations associated with each of the at least one road segments.

183. The computer-implemented method of claim 182 wherein each of the plurality of data samples indicates a heading of the vehicle reporting the data sample, and wherein the associating of a data sample with a road segment is further based at least in part on the heading indicated by the data sample corresponding to one or more headings associated with the road segment.

184. The computer-implemented method of claim 182 wherein the associating of a data sample with a road segment is further based at least in part on one or more travel characteristics of the vehicle reporting the data sample other than the reported location.

185. The computer-implemented method of claim 168 further comprising, for one of the at least some road segments, receiving a plurality of data samples for the one road segment that are reported from one or more traffic sensors monitoring the one road segment and that reflect one or more locations on the one road segment corresponding to the one or more traffic sensors, and generating, by the one or more configured computing systems, one or more assessments of road traffic on the one road segment based on a combination of the plurality of data samples reported from the traffic sensors and of the multiple associated data samples for the one road segment other than the determined data samples, and wherein the providing of the one or more indications for the one road segment includes providing the generated one or more assessments.

186. The computer-implemented method of claim 168 further comprising, for each of multiple distinct periods of time, receiving multiple associated data samples for one of the at least one road segments that each reflects a reported location of a vehicle at a reported time on the one road segment during the period of time, and wherein the automatic analyzing is performed for the one road segment for each of the distinct periods of time based on the data samples whose reported times are during the period of time.

187. The computer-implemented method of claim 168 wherein, for one of the at least one road segments, the determining of the one or more data samples for the one road segment that are unrepresentative of actual vehicle travel on the one road segment is performed in a substantially realtime manner.

188. A non-transitory computer-readable medium having stored contents that configure a computing device to assess data samples representing traveling vehicles, by performing a method comprising:

receiving, by the configured computing device, an indication of multiple data samples that each reflects one or more reported travel characteristics of one of multiple vehicles traveling on one or more roads, the reported travel characteristics for the data samples reflecting locations of the vehicles;

automatically determining, by the configured computing device, that one or more of the multiple data samples are unrepresentative of actual vehicle travel of interest on the one or more roads, the determining being based at least in part on the reported travel characteristics of the one or more data samples; and

providing, by the configured computing device, one or more indications of the data samples that are not determined to be unrepresentative, to enable use of the indicated data samples in facilitating travel on the one or more roads.

189. The non-transitory computer-readable medium of claim 188 wherein the reported travel characteristics of each data sample include a reported location of the vehicle to which the data sample corresponds, and wherein the determining that one or more data samples are unrepresentative of actual vehicle travel of interest on the one or more roads is based at least in part on the reported vehicle locations of those data samples not corresponding to locations for actual vehicle travel on the one or more roads.

190. The non-transitory computer-readable medium of claim 189 wherein the determining that the one or more data samples are unrepresentative based at least in part on the reported vehicle locations of those data samples includes determining that the reported vehicle locations of those data samples do not correspond to predetermined locations of interest for the one or more roads.

191. The non-transitory computer-readable medium of claim 188 wherein the reported travel characteristics of each data sample include a reported heading of travel of the vehicle to which the data sample corresponds, and wherein the determining that one or more data samples are unrepresentative of actual vehicle travel of interest on the one or more roads is based at least in part on the reported vehicle travel headings of those data samples not corresponding to one or more headings for actual vehicle travel on the one or more roads.

192. The non-transitory computer-readable medium of claim 188 wherein the determining that one or more data samples are unrepresentative of actual vehicle travel of interest on the one or more roads includes identifying one or more activities in which one or more vehicles are engaged based on the reported travel characteristics from at least one data sample for each of those one or more vehicles, and includes determining that the at least one data samples are unrepresentative based at least in part on the identified activities being activities that are not of interest, wherein the computer-readable medium is a memory of the configured computing device, and wherein the stored contents are instructions that, when executed, program the computing device to perform the method.

193. A computing system configured to assess data samples representing traveling vehicles, comprising:

one or more processors; and

one or more components that, when executed by at least one of the one or more processors, configure the at least one processor to assess multiple data samples for a road by;

receiving an indication of the multiple data samples, wherein each of the multiple data samples reflects a location of a vehicle traveling near the road;

automatically determining one or more of the multiple data samples whose reflected vehicle locations do not correspond to vehicle travel of interest on the road; and

providing one or more indications of the multiple data samples for the road other than the determined data samples, to enable use of the indicated data samples in facilitating travel on the road.

194. The computing system of claim 193 wherein the determining of the one or more data samples includes determining that the reflected vehicle locations for the one or more data samples do not match predefined locations for the road.

195. The computing system of claim 193 wherein the one or more components include instructions that, when executed, further configure the at least one processor to automatically determine at least one of the multiple data samples that reflects at least one vehicle travel heading that does not correspond to vehicle travel of interest on the road based at least in part on one or more predefined headings for the road.

196. The computing system of claim 193 wherein the one or more components consist of one or more means for performing the assessing of the multiple data samples for the road.