Method and apparatus for recommending services based on map-based dynamic location sampling

1. A method comprising:

determining a predicted location of a vehicle on a road segment based on an estimated time of arrival at an end node of the road segment;

calculating a difference between the predicted location and an actual location sensed by a location sensor of the vehicle; and

recommending a service for installation on or within a threshold distance of the road segment based on the difference.

2. The method of claim 1 , wherein the difference indicates an amount of traffic greater than a threshold value, the method further comprising:

recommending an installation of road lights as the service.

3. The method of claim 1 , further comprising:

correlating the difference with a respective location of a mobile urban service; and

adjusting a sampling rate for the location sensor of the vehicle based on a dynamic location of the mobile urban service.

4. The method of claim 1 , further comprising:

recommending the service based on determining that the vehicle has stopped or has not stopped on the road segment based on the difference.

5. The method of claim 4 , further comprising:

determining that the service is needed on the road segment based on determining that the difference indicates that the vehicle has not stopped on the road segment.

6. The method of claim 4 , further comprising:

determining that the service is not needed on the road segment based on determining that the difference indicates that the vehicle has stopped on the road segment.

7. The method of claim 4 , further comprising:

identifying a location at which the vehicle has stopped as a semantic node,

wherein the service is recommended for installation at or within the threshold distance of the semantic node.

8. The method of claim 1 , wherein the location sensor is configured to collect location data using a sampling rate, and wherein the sampling rate is determined based on the estimated time of arrival calculated from historical traversal time data for the road segment.

9. The method of claim 8 , wherein the sampling rate is dynamically increased based on detecting the vehicle within a threshold distance of a node of the road segment.

10. An apparatus comprising:

at least one processor; and

at least one non-transitory memory including computer program code for one or more programs,

the at least one non-transitory memory and the computer program code configured to, with the at least one processor, cause the apparatus to perform at least the following,

determine a predicted location of a vehicle on a road segment based on an estimated time of arrival at an end node of the road segment;

calculate a difference between the predicted location and an actual location sensed by a location sensor of the vehicle; and

recommend a service for installation on or within a threshold distance of the road segment based on the difference.

11. The apparatus of claim 10 , wherein the difference indicates an amount of traffic greater than a threshold value, and wherein the apparatus is further caused to:

recommend an installation of road lights as the service.

12. The apparatus of claim 10 , wherein the apparatus is further caused to:

correlate the difference with a respective location of a mobile urban service; and

adjust a sampling rate for the location sensor of the vehicle based on a dynamic location of the mobile urban service.

13. The apparatus of claim 10 , wherein the apparatus is further caused to:

recommend the service based on determining that the vehicle has stopped or has not stopped on the road segment based on the difference.

14. The apparatus of claim 13 , wherein the apparatus is further caused to:

determine that the service is needed on the road segment based on determining that the difference indicates that the vehicle has not stopped on the road segment.

15. The apparatus of claim 13 , wherein the apparatus is further caused to:

determine that the service is not needed on the road segment based on determining that the difference indicates that the vehicle has stopped on the road segment.

16. A non-transitory computer-readable storage medium, carrying one or more sequences of one or more instructions which, when executed by one or more processors, cause an apparatus to perform:

determining a predicted location of a vehicle on a road segment based on an estimated time of arrival at an end node of the road segment;

calculating a difference between the predicted location and an actual location sensed by a location sensor of the vehicle; and

recommending a service for installation on or within a threshold distance of the road segment based on the difference.

17. The non-transitory computer-readable storage medium of claim 16 , wherein the difference indicates an amount of traffic greater than a threshold value, and wherein the apparatus is caused to further perform:

recommending an installation of road lights as the service.

18. The non-transitory computer-readable storage medium of claim 16 , wherein the apparatus is caused to further perform:

correlating the difference with a respective location of a mobile urban service; and

adjusting a sampling rate for the location sensor of the vehicle based on a dynamic location of the mobile urban service.

19. The non-transitory computer-readable storage medium of claim 16 , wherein the apparatus is caused to further perform:

recommending the service based on determining that the vehicle has stopped or has not stopped on the road segment based on the difference.

20. The non-transitory computer-readable storage medium of claim 19 , wherein the apparatus is caused to further perform:

determining that the service is needed on the road segment based on determining that the difference indicates that the vehicle has not stopped on the road segment.