Here Global B.v.

Caterpillar Inc.

Navteq B.v.

Toby Tennent

Anatoly S Belkin

Oliver B Downs

George Filley

James Adeyemi Fowe

Matthew G Lindsay

Praveen Arcot

Charles Smolich

Michael L Aper

Gary R Rockwood

Leon Stenneth

Bruce A Bernhardt

Gavril Adrian Giurgiu

Filippo Pellolio

Kevin Moran

Daniela Radakovic

Justin M Spelbrink

Robert Camp

Tony Belkin

Mark Pundurs

Timur Perelmutov

Digan Conor

Eric J Philips

Brandon Hootman

Dereje Befecadu

The present disclosure is directed to systems and methods for updating asset ownership for telematics-enabled machinery. In some embodiments, the updating asset ownership system comprises asset entities that transmit telemetry data. A communication interface of the updating asset ownership system can receive the telemetry data. An asset ownership data store of the updating asset ownership system can be configured to store asset owner data that identify designated owners associated with the asset entities. An asset ownership comparator of the updating asset ownership system can process the received telemetry data. Based on the processing, the asset ownership comparator can update designated owners of asset entities stored in the asset ownership data store.

Updating asset ownership systems and methods

Methods, apparatus, computer program products, and systems for presenting a trip cost determination and/or a representation thereof to a user. An example method comprises receiving a pricing request comprising a user location and determining one or more cost model parameters, the one or more cost model parameters comprising at least one of current real-world conditions, predicted real-world conditions, user behavior characteristics, vehicle characteristics, and roadway information. The example method further comprises providing cost model information comprising at least one of (a) one or more cost model parameters, (b) a route, and (c) the user location and receiving a trip cost determination. The trip cost determination is determined based at least in part on the cost model information. The example method further comprises providing the trip cost determination and causing presentation of a representation of the trip cost determination through a user interface of a user apparatus.

Vehicle usage-based pricing alerts

A method, apparatus and computer program product are described so as to provide more additional information regarding vehicular behavior. In the context of a method, information is received regarding a location of the vehicle at a plurality of instances in time. The plurality of instances in time define a time period. The method also includes determining an environmental condition at the location during the time period and comparing the behavior of the vehicle to that of other vehicles at the location that are also subjected to the environmental condition. Additionally, the method may determine a score for the vehicle in relation to a risk factor based upon the behavior of the vehicle and the comparison to other vehicles at the location that are also subjected to the environmental condition.

Method and apparatus for determining location-based vehicle behavior

An approach is provided for determining at least one distribution of a plurality of current values for at least one dynamic content parameter associated with a plurality of points of interest within a predetermined proximity to at least one target point of interest. The approach involves determining at least one distribution mean and at least one distribution standard deviation for the at least one distribution of the plurality of current values. The approach also involves determining at least one set of historical values for the at least one dynamic content parameter for the at least one target point of interest. The approach further involves determining at least one estimated current value for the at least one dynamic content parameter associated with the at least one target point of interest based, at least in part, on the at least one set of historical values, the at least one distribution mean, and the at least one distribution standard deviation.

Method and apparatus for causing a recommendation of a point of interest

In one embodiment, an incident report including a path segment identifier and an incident identifier is received at a computing device. The incident identifier is sent to a traffic prediction model. The traffic prediction model returns a traffic distribution value. The traffic distribution value identifies a portion of a traffic prediction distribution derived from historical data. The computing device accesses a lookup table according to traffic distribution value and the path segment identifier to receive a speed prediction.

Predictive incident aggregation

A method is disclosed comprising: obtaining data associated with each road segment of at least one road segment, said data comprising: a representative of at least one link associated with the respective road segment; obtaining probe data associated with the respective road segment, the probe data comprising: at least one piece of position information; determining a sinuous driving metric, which is a value being indicative of a sinuosity of driving on the respective road segment based at least partially on the probe data and its allocation with respect to the respective road segment. It is further disclosed an according apparatus, computer program and system.

Navigation driving metric

A method and apparatus for traffic modeling comprising: receiving by a server, traffic data generated by one or more devices configured to record data indicating speeds of vehicles traveling a road segment. The server separates, with a processor, the traffic data into zero speed data and non-zero speed data. The server determines, with the processor, a zero speed data characteristic value of the zero speed data. The server determines, with the processor, at least one non-zero speed data characteristic value of the non-zero speed data. The server generates, with the processor, a representative traffic value for the road segment as a function of the zero speed characteristic value and the non-zero speed characteristic value.

Traffic speed modeling

An approach is provided for creating an origin-destination matrix from probe trajectory data. The approach includes receiving probe trajectory data, wherein the probe trajectory data is associated with at least one subset of a plurality of travel nodes. The approach further includes processing and/or facilitating a processing of the probe trajectory data to construct one or more microscopic origin-destination matrices, wherein the at least one microscopic origin-destination matrix represents one or more preferred travel paths through the subset of the plurality of travel nodes. The approach also involves causing, at least in part, an aggregation of the one or more microscopic origin-destination matrices to construct at least one aggregated origin-destination matrix to represent the plurality of travel nodes.

Method and apparatus for creating an origin-destination matrix from probe trajectory data

A speed profile dictionary and associated lookup tables are disclosed. A set of distinct speed profiles is defined using a statistical analysis routine. Preferably, the statistical analysis routine uses clustering. The speed profiles are then matched to location codes identifying physical locations on a road network and days of the week. Applications using historic traffic data may use the speed profile dictionary and one or more lookup tables instead of a complete historic traffic database, thereby reducing the amount of memory needed to store historic traffic data.

Speed profile dictionary

Growing community of inventors

Chicago, IL, United States of America

Toby Tennent