Systems and methods for classifying vehicle trips
Systems and methods in accordance with embodiments of the invention can obtain and use a variety of telematics data to classify trips taken by a vehicle. Trip models can be generated based on telematics data captured during the operation of a vehicle. A variety of features of the trip model, such as the timing and/or location of stops made by the vehicle during one or more trips, can be used to classify the trip as a business trip or a personal trip. In several embodiments, machine classifiers are trained to classify features within the trip models based on historical trips that have been classified as business trips or personal trips. A number of trip models can be combined with other driver attributes to classify a particular vehicle and/or driver as engaged with a transportation network company.
1. A method comprising:
obtaining telematics data comprising a set of geographic locations at a classification server system, a set of trips for a driver of one or more vehicles determined based on the telematics data; determining a starting location and an ending location of each trip of the set of trips using the set of geographic locations;
determining a starting time at which the vehicle was at the starting location and an ending time at which the vehicle was at the ending location;
determining a set of features for each trip in the set of trips;
generating a classification for each trip in the set of trips based on the set of features, the classification generated using the classification server system; and
classifying the driver based on the classification of each trip in the set of trips.
2. The method of claim 1 , wherein the telematics data is obtained via a telematics device.
3. The method of claim 2 , wherein the telematics device is at least one of a vehicle or a mobile device associated with one or more persons in the vehicle.
4. The method of claim 3 , wherein the telematics data includes at least one of acceleration data, speed data, braking data, heading data, impact data, data identifying the vehicle, or data identifying one or more persons in the vehicle.
5. The method of claim 1 , wherein the driver is classified as a commercial driver or a personal driver.
6. The method of claim 1 , wherein the set of features include at least one of an intermediate stop, an acceleration at a particular time, a speed at a particular time, or a point of interest visited during a trip.
7. The method of claim 1 , wherein the classification includes at least one of a label indicating if each trip in the set of trips is a business trip or a personal trip or a confidence metric.
8. A system comprising:
one or more telematics devices in communication with a classification server system over a network, the one or more telematics devices configured to capture telematics data comprising a set of geographic locations at a classification server system, a set of trips for a driver of one or more vehicles determined based on the telematics data, a classification for each trip in the set of trips generated based on the set of features using the classification server system, the driver classified based on the classification of each trip in the set of trips.
9. The system of claim 8 , wherein at least one of the one or more of telematics devices is a mobile device.
10. The system of claim 9 , wherein at least one of the one or more of telematics devices is a vehicle.
11. The system of claim 8 , wherein the telematics data includes at least one of acceleration data, speed data, braking data, heading data, impact data, data identifying a vehicle, or data identifying the driver.
12. The system of claim 8 , wherein the driver is classified as a commercial driver or a personal driver.
13. The system of claim 8 , wherein the set of features include at least one of an intermediate stop, an acceleration at a particular time, a speed at a particular time, or a point of interest visited.
14. The system of claim 8 , wherein the classification includes at least one of a label indicating if each trip in the set of trips is a business trip or a personal trip or a confidence metric.
15. A non-transitory machine-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform steps comprising:
obtaining telematics data comprising a set of geographic locations, a set of trips for a driver of one or more vehicles determined based on the telematics data;
determining a starting location and an ending location of each trip of the set of trips using the set of geographic locations;
determining a starting time at which the vehicle was at the starting location and an ending time at which the vehicle was at the ending location;
determining a set of features for each trip in the set of trips;
generating a classification for each trip in the set of trips based on the set of features, the classification generated; and
classifying the driver based on the classification of each trip in the set of trips.
16. The non-transitory machine-readable medium of claim 15 , wherein the telematics data is obtained via a telematics device.
17. The non-transitory machine-readable medium of claim 16 , wherein the telematics device is at least one of a vehicle or a mobile device associated with one or more persons in the vehicle.
18. The non-transitory machine-readable medium of claim 17 , wherein the telematics data includes at least one of acceleration data, speed data, braking data, heading data, impact data, data identifying the vehicle, or a driver identifier.
19. The non-transitory machine-readable medium of claim 15 , wherein the driver is classified as a commercial driver or a personal driver.
20. The non-transitory machine-readable medium of claim 15 , wherein the set of features include at least one of an intermediate stop, an acceleration at a particular time, a speed at a particular time, or a point of interest visited.