System and method for deriving operation information of a fleet and analyzing the same

1. A method for optimizing traveled routes of a fleet, the method comprising:

reconstructing routes traveled by vehicles of the fleet based on historical GPS traces of the fleet;

determining stops made by the vehicles along the travelled routes;

determining a work shift of a fleet by:

using statistical analysis of the historical GPS traces to determine a first time when vehicle activity is at a first level,

using statistical analysis of the historical GPS traces to determine a second time when the vehicle activity is at second level, and

determining the work shift using the first time and the second time;

classifying the stops into work stops and non-work stops;

optimizing routes for covering the work stops,

wherein information associated with the optimized routes is used to redistribute one or more vehicles, of the vehicles, to minimize a quantity of vehicles used to cover the work stops based on the work shift; and

providing the information associated with the optimized routes to at least one of:

selected vehicles of the fleet, or

an identified fleet with similar characteristics to the fleet.

2. The method of claim 1 , further comprising:

determining hotspots from the stops, a hotspot, of the hotspots, representing a recurrent stop made by vehicles of the fleet.

3. The method of claim 2 , further comprising:

determining depots from the hotspots, a depot, of the depots, representing an administrative office or a warehouse of the fleet.

4. The method of claim 3 , further comprising:

determining work hours of the fleet based on the historical GPS traces.

5. The method of claim 1 , further comprising:

obtaining point-of-interests information from a 3 rd party database; and

classifying a stop based on the historical GPS traces and the point-of-interests information.

6. The method of claim 1 , further comprising:

determining whether the fleet is benefited from adopting a fleet management function based on the optimized routes.

7. The method of claim 6 , further comprising:

selecting a plurality of fleets that will be benefited from adopting the fleet management function; and

preparing individualized market materials that include results obtained by determining whether the fleet is benefited from adopting the fleet management function.

8. The method of claim 6 , wherein the fleet management function is a routing algorithm.

9. The method of claim 8 , further comprising:

determining a return of investment for the fleet that is generated by an adoption of the routing algorithm, the return of investment being determined based on the optimized routes.

10. A non-transitory computer-readable medium storing instructions, the instructions comprising:

one or more instructions that, when executed by one or more processors of a device, cause the one or more processors to:

reconstruct routes traveled by vehicles of a fleet based on historical GPS traces of the fleet;

determine a work shift of a fleet,

wherein the one or more instructions, that cause the one or more processors to determine the work shift, cause the one or more processors to:

use statistical analysis of the historical GPS traces to determine a first time when vehicle activity is at a first level,

use statistical analysis of the historical GPS traces to determine a second time when the vehicle activity is at a second level, and

determine the work shift using the first time and the second time;

determine stops made by the vehicles along the travelled routes;

classify the stops into work stops and non-work stops;

optimize routes for the work stops,

wherein information associated with the optimized routes redistribute one or more vehicles, of the vehicles, to minimize a quantity of vehicles used to cover the work stops based on the work shift; and

provide the information associated with the optimized routes to at least one of:

selected vehicles of the fleet, or

an identified fleet with similar characteristics to the fleet.

11. The non-transitory computer-readable medium of claim 10 , wherein the one or more instructions, when executed by the one or more processors, further cause the one or more processors to:

determine hotspots from the stops, a hotspot, of the hotspots, representing a recurrent stop made by vehicles of the fleet.

12. The non-transitory computer-readable medium of claim 11 , wherein the one or more instructions, when executed by the one or more processors, further cause the one or more processors to:

determine depots from the hotspots, a depot, of the depots, representing an administrative office or a warehouse of the fleet.

13. The non-transitory computer-readable medium of claim 12 , wherein the one or more instructions, when executed by the one or more processors, further cause the one or more processors to:

determine work hours of the fleet based on the historical GPS traces.

14. The non-transitory computer-readable medium of claim 10 , wherein the one or more instructions, when executed by the one or more processors, further cause the one or more processors to:

classify point-of-interests information from a 3 rd party database; and

classify a stop based on the historical GPS traces and the point-of-interests information.

15. The non-transitory computer-readable medium of claim 10 , wherein the one or more instructions, when executed by the one or more processors, further cause the one or more processors to:

determine whether the fleet is benefited from adopting a fleet management function.

16. The non-transitory computer-readable medium of claim 15 , wherein the one or more instructions, when executed by the one or more processors, further cause the one or more processors to:

select a plurality of fleets that will be benefited from adopting the fleet management function; and

prepare individualized market materials that include results obtained by determining whether the fleet is benefited from adopting the fleet management function.

17. The non-transitory computer-readable medium of claim 15 , wherein the fleet management function is a routing algorithm.

18. The non-transitory computer-readable storage medium of claim 17 , wherein the one or more instructions, when executed by the one or more processors, further cause the one or more processors to:

determine a return of investment for the fleet that is generated by an adoption of the routing algorithm, the return of investment being determined based on the optimized routes.

19. A device comprising:

one or more memories; and

one or more processors to:

reconstruct routes traveled by vehicles of a fleet based on historical GPS traces of the fleet;

determine a work shift of a fleet, wherein the one or more processors, when determining the work shift, are to:

use statistical analysis of the historical GPS traces to determine a first time when vehicle activity is at a first level,

use statistical analysis of the historical GPS traces to determine a second time when the vehicle activity is at a second level, and

determine the work shift using the first time and the second time;

determine stops made by the vehicles along the travelled routes;

classify the stops into work stops and non-work stops;

optimize routes for the work stops,

wherein information associated with the optimized routes redistribute one or more vehicles, of the vehicles, to minimize a quantity of vehicles used to cover the work stops based on the work shift; and

provide the information associated with the optimized routes to at least one of:

selected vehicles of the fleet, or

an identified fleet with similar characteristics to the fleet.

20. The device of claim 19 , wherein the one or more processors are further configured to at least one of:

determine hotspots from the stops, a hotspot, of the hotspots, representing a recurrent stop made by vehicles of the fleet, or

determine depots from the hotspots, a depot, of the depots, representing an administrative office or a warehouse of the fleet.