Inrix Inc.

Serena Software, Inc.

Inrix, Inx.

Robert Cahn

Oliver B Downs

Craig H Chapman

Alec Barker

Kush G Parikh

Jesse S Hersch

Christopher Laurence Scofield

Weimin Mark Wang

Robert Frederick Leidle

Todd Asher

Wayne Stoppler

Mitchel A Burns

Jeff Hill

Te-Ming Huang

John Scumniotales

Michael Lester

Mitchel Burns

Valerie Raybold Yakich

Eric Burke

Thomas Fannon

Techniques are described for generating and using information regarding road traffic in various ways, including by obtaining and analyzing road traffic information regarding actual behavior of drivers of vehicles on a network of roads. Obtained actual driver behavior information may in some situations be analyzed to identify decision point locations at which drivers face choices corresponding to possible alternative routes through the network of roads (e.g., intersections, highway exits and/or entrances, etc.), as well as to track the actual use by drivers of particular paths between particular decision points in order to determine preferred compound links between those decision point locations. The identified and determined information from the analysis may then be used in various manners, including in some situations to assist in determining particular recommended or preferred routes of vehicles through the network of roads based at least in part on actual driver behavior information.

Learning road navigation paths based on aggregate driver behavior

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).

Filtering road traffic data from multiple data sources

Techniques are described for generating and using information regarding road traffic in various ways, including by obtaining and analyzing road traffic information regarding actual behavior of drivers of vehicles on a network of roads. Obtained actual driver behavior information may in some situations be analyzed to determine actual delays for vehicles encountering various particular road features in the network of roads, such as for identified decision points at which drivers face choices corresponding to possible alternative routes through the network of roads (e.g., intersections, highway exits and/or entrances, etc.) and/or for other traffic flow impediments. The identified and determined information from the analysis may then be used in various manners, including in some situations to assist in determining particular recommended or preferred routes of vehicles through the network of roads based at least in part on actual driver behavior information.

Learning road feature delay times based on aggregate driver behavior

Techniques are described for determining and using information regarding expected road traffic flow conditions information for vehicles traveling on roads. The expected road traffic flow conditions for a particular portion of a road may be generated by combining historical representative information about road traffic flow conditions for that road portion with current information about actual traffic flow on or near that road portion. The combination may, for example, provide benefits for estimating expected traffic flow conditions information for roads with structural flow obstructions that cause reduced traffic flow at certain road locations and times&#x2014;for example, the expected traffic flow conditions information may be based at least in part on fitting or otherwise adapting partial actual traffic flow information about a vehicle's actual travel path to a historical travel profile for a road that includes representative traffic flow information for various combinations of road locations and time periods.

Predicting expected road traffic conditions based on historical and current data

Assessing road traffic speed using data from multiple data sources

Detecting unrepresentative road traffic condition data

Detecting anomalous road traffic conditions

Techniques are described for displaying or otherwise providing information to users regarding various types of road traffic condition information in various ways. The information may be provided, for example, as part of a user interface (or 'UI'), which may in some situations further include one or more types of user-selectable controls to allow a user to manipulate in various ways what road traffic condition information is displayed and/or how the information is displayed. A variety of types of road traffic condition information may be presented to users in various manners, including by presenting information on graphically displayed maps for geographic areas to indicate various information about road conditions in the geographic area. In addition, provided controls may allow users to select particular times, select particular routes, indicate to perform animation of various types of changing traffic conditions over a sequence of multiple successive times, etc.

Displaying road traffic condition information and user controls

Determining road traffic conditions using multiple data samples

Determining road traffic conditions using data from multiple data sources

Techniques are described for automatically detecting anomalous road traffic conditions and for providing information about the detected anomalies, such as for use in facilitating travel on roads of interest. Anomalous road traffic conditions may be identified using target traffic conditions for a particular road segment at a particular selected time, such as target traffic conditions that reflect actual traffic conditions for a current or past selected time, and/or target traffic conditions that reflect predicted future traffic conditions for a future selected time. Target traffic conditions may be compared to distinct expected road traffic conditions for a road segment at a selected time, with the expected conditions reflecting road traffic conditions that are typical or normal for the road segment at the selected time. Anomalous conditions may be identified based on sufficiently large differences from the expected conditions, and information about the anomalous conditions may be provided in various ways.

Methods and systems for managing and analyzing multi-dimensional data are provided. Example embodiments provide a Meta-Object Data Management System 'MODMS,' which enables users to arrange and to rearrange hierarchical relationships of the data on an ad-hoc basis and allows the data to be analyzed using any set of attributes (dimensions) while the system is running. The MODMS represents heterogeneous data in a normalized (standardized) fashion using an object type management system that allows the arbitrary coercion of one type of object into another different type of object and automatically resolves attribute dependencies. In one embodiment, the MODMS comprises an object type management subsystem; a meta-object instantiation subsystem; one or more data repositories; and an input/output interface. These components cooperate to allow the creation, management, and analysis of relationships between many different types of single and multi-dimensional data. The MODMS may be used to implement an enterprise portfolio management system.

Method and system for object-oriented management of multi-dimensional data

Growing community of inventors

Federal Way, WA, United States of America

Robert Cahn