Here Global B.v.

Navteq B.v.

Praveen Arcot

Matthew G Lindsay

Oliver B Downs

Dongwook Jang

Toby Tennent

Finn Swingley

Justin M Spelbrink

Timothy A McGrath

Steven P Devries

Marcus Ernster

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

In one embodiment, traffic data that originates from sensors, cameras, or observations is analyzed. The traffic data is associated with multiple repeating time epochs or intervals. The traffic data is divided into clusters using a clustering technique. The clustering technique may include clusters of variable sizes. Each of the clusters is analyzed to calculate statistical parameters including but not limited to an average value for one or more clusters and a standard deviation value for one or more clusters. In response to a request for traffic data, simulated traffic data may be generated by providing the average value and the standard deviation value for one or more of the clusters.

Traffic data simulator

In one embodiment, traffic data that originates from sensors, cameras, or observations is analyzed. The traffic data is associated with multiple repeating time epochs or intervals. The traffic data is divided into clusters using a clustering technique. The traffic data may be collected on specific days such as holidays. The holiday traffic data may be divided data into clusters and a dominant traffic pattern cluster that represents a holiday classification is identified. The dominant traffic pattern cluster is stored in a traffic prediction model.

A method and system for calculating an energy efficient route is disclosed. A route calculation application calculates one or more routes from an origin to a destination. For each of the routes, the route calculation application uses impedance factor data associated with each segment in the route. The impedance factor is calculated using probe data when the probe data is available for a road segment. When probe data is unavailable, the impedance factor is calculated using machine learning techniques that analyze the results of the impedance factor classifications for road segments having probe data.

Energy efficient routing using an impedance factor

A method for predicting traffic wherein the method is a trend based extrapolation method that uses real time traffic data and historic traffic data to generate a predictive traffic product. The predictive traffic product provides expected traffic speeds for the short term future, for example, between two to twelve hours into the future.

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Naperville, IL, United States of America

Praveen Arcot