Transportation prediction with machine learning model
AssigneeSap SE
InventorsJing Gu, Mingjie Dong, Jie Zhao
Life of this patent
Estimating the term…
Abstract
A machine learning model is trained using past estimations of transit time and actual observed transit times. Other training features can include day-of-the-week of load, hour-of-the-day of load, load locations, and carrier identifier. Subsequent planning can invoke the machine learning model, which can predict an actual transit time. The technologies can be integrated into track and trace functionality. Useful for improving planning and assisting inexperienced users.
Claims20
A computer-implemented method comprising:
- receiving a set of attributes for a planned load, wherein the attributes comprise an estimated transit time attribute and day-of-the-week attribute;
- applying the attributes to a machine learning model, wherein the machine learning model is trained with training attributes comprising the day-of-the-week attribute and the estimated transit time attribute and is trained to predict actual transit time difference based on observed transit time differences calculated from observed actual transit timestamps of past loads;
- and
- with the machine learning model, based on the attributes comprising the day-of-the-week attribute and the estimated transit time attribute, generating a predicted actual transit time difference.
A computing system comprising:
- at least one hardware processor;
- at least one memory coupled to the at least one hardware processor;
- and
- one or more non-transitory computer-readable media having stored therein computer-executable instructions that, when executed by the computing system, cause the computing system to perform:
- receiving a set of attributes for a planned load, wherein the attributes comprise an estimated transit time attribute and day-of-the-week attribute;
- applying the attributes to a machine learning model, wherein the machine learning model is trained with training attributes comprising the day-of-the-week attribute and the estimated transit time attribute and is trained to predict actual transit time difference based on observed transit time differences calculated from observed actual transit timestamps of past loads;
- and
- with the machine learning model, based on the attributes comprising the day-of-the-week attribute and the estimated transit time attribute, generating a predicted actual transit time difference.
One or more non-transitory computer-readable media having stored therein computer-executable instructions that when executed by a computing system, cause the computing system to perform:
- receiving a set of attributes for a load, wherein the attributes comprise an estimated transit time of the load attribute, a day-of-the-week of the load attribute, an hour-of-the-day of the load attribute, and a carrier identifier of the load attribute;
- applying the attributes to a machine learning model, wherein the machine learning model is trained with training attributes comprising the estimated transit time of the load attribute, the day-of-the-week attribute, the hour-of-the-day of the load attribute, and the carrier identifier attribute and the machine learning model is trained to predict observed transit time difference calculated from observed actual transit timestamps of past loads;
- and
- with the machine learning model, based on the attributes, generating a predicted actual transit time difference indicating a prediction of an actual transit time.
Dependencies are inferred from the claim text.