Identifying significant locations based on vehicle probe data

1. A vehicle, comprising:

a processor configured to execute instructions stored on a non-transitory computer readable medium to:

identify vehicle transportation network information representing a vehicle transportation network, the vehicle transportation network information including destinations of a vehicle obtained by spatially clustering vehicle probe data;

for at least some of the destinations, perform density-based clustering using temporal data associated with the at least some of the destinations to form at least two spatio-temporal clusters; and

assign a contextual label to a location associated with a first spatio-temporal cluster of the at least two spatio-temporal clusters based on the temporal data associated with the first spatio-temporal cluster, the location being a labeled location; and

a vehicle operation device configured to operate the vehicle using the labeled location.

2. The vehicle of claim 1 , wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to perform density-based clustering by:

performing two-dimensional density-based clustering using an arrival time of day and a stay time of trip data associated with the at least some of the destinations.

3. The vehicle of claim 2 , wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to perform density-based clustering by:

grouping trip data associated with each destination of the at least some of the destinations by stay type; and

performing the two-dimensional density-based clustering by the stay type.

4. The vehicle of claim 3 , wherein the stay type comprises at least two of:

a weekday stay type having an arrival day of a week and a departure day of the week both falling on a weekday;

a weekend stay type having an arrival day of the week and a departure day of the week both falling on a weekend; or

a mixed stay type having an arrival day of the week falling on a weekday and a departure day of the week falling on a weekend or having an arrival day of the week falling on a weekend and a departure day of the week falling on a weekday.

5. The vehicle of claim 1 , wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to identify vehicle transportation network information representing a vehicle transportation network by, before the density-based clustering:

transforming point data from the vehicle probe data into trip data, each trip forming the trip data comprising a start point with a start timestamp and a stop point with a stop timestamp; and

performing the spatial clustering using the trip data to form spatial clusters to identify the destinations of the vehicle.

6. The vehicle of claim 5 , wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to identify vehicle transportation network information representing a vehicle transportation network by, before the density-based clustering:

storing the trip data to form stored trip data;

assigning a cluster identifier to each unique vehicle transportation network location resulting from performing the spatial clustering; and

appending the cluster identifier to any of the start points and any of the stop points forming a respective one of the spatial clusters within the stored trip data.

7. The vehicle of claim 6 , wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to identify vehicle transportation network information representing a vehicle transportation network by, before the density-based clustering:

determining a stay time for a trip of the trip data as a difference between a stop timestamp of the trip and a start timestamp of a subsequent trip;

filtering continuous records by comparing a cluster identifier of the trip with a cluster identifier of the subsequent trip to generate a location record for the unique vehicle transportation network location; and

adding an arrival timestamp at the unique vehicle transportation network location and a departure timestamp from the unique vehicle transportation network location to the location record.

8. The vehicle of claim 5 , wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to identify vehicle transportation network information representing a vehicle transportation network by, before the density-based clustering:

sorting each entry within the vehicle probe data based on a measurement timestamp to form a sequence of records;

determining a time difference of consecutive records within the sequence;

identifying the start of a trip when the time difference exceeds a threshold value; and

identifying the start point, the start timestamp, the stop point, and the stop timestamp for the trip.

9. The vehicle of claim 1 , wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to identify vehicle transportation network information representing a vehicle transportation network by, before the density-based clustering:

performing the spatially clustering using a plurality of trips comprising GPS coordinates associated with each of a start location and an end location from the vehicle probe data to form spatial clusters; and

assigning a cluster identifier to each location associated with a spatial cluster; and wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to perform density-based clustering by:

for each location assigned to a cluster identifier, performing two-dimensional density-based clustering of arrival time of day at location versus a stay time at the location to form temporal clusters; and

forming the at least two spatio-temporal clusters by combining the spatial clusters with the temporal clusters.

10. The vehicle of claim 1 , wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to assign a contextual label to a location by:

selecting records from the first spatio-temporal cluster within a defined time window that have an overnight stay;

identifying the location that is the most common within the records; and

assigning the contextual label of home to the location.

11. The vehicle of claim 1 , wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to assign a contextual label to a location by:

selecting records from the first spatio-temporal cluster within a defined time window that have no overnight stay;

identifying the location that is the most common within the records; and

assigning the contextual label of work to the location.

12. The vehicle of claim 1 , wherein the vehicle operation device is a navigation system; and wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to:

generate a route to the labeled location using the navigation system.

13. The vehicle of claim 12 , wherein the vehicle is an autonomous vehicle; and wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to:

operate the vehicle to traverse the route.

14. A system for identifying and using a location significant to a person associated with a vehicle based on vehicle probe data, the system including:

a memory including a non-transitory computer readable medium; and

a processor configured to execute instructions stored on the non-transitory computer readable medium to:

identify vehicle transportation network information representing a vehicle transportation network, the vehicle transportation network information including destinations of a vehicle obtained by spatially clustering vehicle probe data;

for at least some of the destinations, perform density-based clustering using temporal data associated with the at least some of the destinations to form at least two spatio-temporal clusters;

assign a contextual label to a location associated with a first spatio-temporal cluster of the at least two spatio-temporal clusters based on the temporal data associated with the first spatio-temporal cluster, the location being a labeled location; and

use the labeled location for vehicle operation.

15. The system of claim 14 , wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to use the labeled location for vehicle operation by:

performing density-based clustering using temporal data associated with destinations determined over a period of time subsequent to the assigning the contextual label to form at least two updated spatio-temporal clusters;

assigning the contextual label to an updated location associated with a first updated spatio-temporal cluster;

comparing the location with the updated location; and

changing the labeled location to the updated location when the updated location is different from the location.

16. The system of claim 14 , wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to perform density-based clustering using temporal data by:

performing two-dimensional density-based clustering using an arrival time of day and a stay time of trip data associated with those of the destinations having a same stay type based on an arrival day of a week and a departure day of the week.

17. The system of claim 16 , wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to identify vehicle transportation network information by, before the density-based clustering:

performing the spatially clustering using location data within the vehicle probe data to identify the destinations.

18. A system for identifying and using a location significant to a person associated with a vehicle based on vehicle probe data, the system including:

a memory including a non-transitory computer readable medium; and

a processor configured to execute instructions stored on the non-transitory computer readable medium to:

identify vehicle transportation network information representing a vehicle transportation network by performing spatial clustering of vehicle location information obtained from vehicle probe data of a vehicle, the vehicle transportation network information including destinations of the vehicle;

perform two-dimensional density-based clustering using an arrival time of day and a stay time associated with at least some of the destinations to form a spatio-temporal cluster;

assign a contextual label to a location associated with the spatio-temporal cluster based on temporal data associated with the spatio-temporal cluster, the location being a labeled location; and

use the labeled location for vehicle operation.

19. The system of claim 18 , wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to:

group trip data associated with the destinations by stay type; and wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to perform two-dimensional density-based clustering by:

performing the two-dimensional density-based clustering for the at least some of the destinations associated with a single stay type.

20. The system of claim 19 , wherein the single stay type comprises one of:

a weekday stay type having an arrival day of a week and a departure day of the week both falling on a weekday; or

a weekend stay type having an arrival day of the week and a departure day of the week both falling on a weekend.