Navigation System

A computer program product comprising a non-transitory computer readable medium encoding instructions that, in response to execution by a computing device, cause the computing device to perform operations comprising:

• retrieving a set of learning parameters including driver history data describing one or more past journeys;
• analyzing the set of learning parameters to determine a candidate set including data describing frequent start locations and frequent end locations for one or more potential journeys to one or more destinations;
• measuring a current time of day;
• estimating, based at least in part on the current time of day, a set of journey duration data for the one or more potential journeys described by the candidate set;
• segmenting the journey duration data into non-uniform segments;
• estimating direction data describing one or more directions for the one or more potential journeys to the one or more destinations;
• determining one or more metrics for the one or more potential journeys using a metric estimation network that indicates one or more associations between the one or more destinations and one or more journey variables;
• determining one or more quality scores for the one or more potential journeys based at least in part on the one or more metrics;
• storing the journey duration data in a memory accessible by one or more system modules;
• and
• deleting destination data describing the one or more destinations and the one or more journey variables associated with the one or more destinations based on a trigger event triggering data deletion.

A navigation system comprising:

• a non-transitory computer-readable medium storing computer-executable code, the computer-readable medium comprising:
• a driving history module retrieving a set of learning parameters including driver history data describing one or more past journeys;
• a frequency module communicatively coupled to the driving history module, the frequency module analyzing the set of learning parameters to determine a candidate set including data describing frequent start locations and frequent end locations for one or more potential journeys to one or more destinations;
• a timestamp generator measuring a current time of day;
• a duration module communicatively coupled to the frequency module and the timestamp generator, the duration module estimating, based at least in part on the current time of day, a set of journey duration data for the one or more potential journeys described by the candidate set, the duration module storing the journey duration data in a memory accessible by one or more system modules;
• a conversion module communicatively coupled to the duration module, the conversion module segmenting the journey duration data into non-uniform segments;
• a direction module communicatively coupled to the frequency module, the direction module estimating direction data describing one or more directions for the one or more potential journeys to the one or more destinations;
• a metric module communicatively coupled to the frequency module, the duration module, the conversion module and the direction module, the metric module determining one or more metrics for the one or more potential journeys using a metric estimation network that indicates one or more associations between the one or more destinations and one or more journey variables;
• and
• a quality module communicatively coupled to the metric module, the quality module determining one or more quality scores for the one or more potential journeys based at least in part on the one or more metrics;
• a forgetting module communicatively coupled to the direction module and the metric module, the forgetting module deleting destination data describing the one or more destinations and the one or more journey variables associated with the one or more destinations based on a trigger event triggering data deletion.

A method for a navigation system comprising:

• retrieving, with one or more processors, a set of learning parameters including driver history data describing one or more past journeys;
• analyzing, with the one or more processors, the set of learning parameters to determine a candidate set including data describing frequent start locations and frequent end locations for one or more potential journeys to one or more destinations;
• measuring, with the one or more processors, a current time of day;
• estimating with the one or more processors, based at least in part on the current time of day, a set of journey duration data for the one or more potential journeys described by the candidate set;
• segmenting, with the one or more processors, the journey duration data into non-uniform segments;
• estimating, with the one or more processors, direction data describing one or more directions for the one or more potential journeys to the one or more destinations;
• determining, with the one or more processors, one or more metrics for the one or more potential journeys based at least in part on the non-uniformly segmented journey duration data;
• determining, with the one or more processors, one or more quality scores for the one or more potential journeys based at least in part on the one or more metrics;
• storing, with the one or more processors, the journey duration data in a memory accessible by one or more system modules;
• and
• deleting, with the one or more processors, destination data describing the one or more destinations and the one or more journey variables associated with the one or more destinations based on a trigger event triggering data deletion.