SYSTEMS AND METHODS FOR RECOMMENDING TRANSPORTATION SERVICES

• US 20200134747A1
• Filed: 12/27/2019
• Published: 04/30/2020
• Est. Priority Date: 02/06/2018
• Status: Active Application

• Current Assignee: Beijing Didi Infinity Technology And Development Co.Ltd.
• Original Assignee: Beijing Didi Infinity Technology And Development Co.Ltd.
• Inventors: ZHANG, Lingyu
• Application Number:
  US16/729,281
• Publication Number:
  US 20200134747A1
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 16/9537 Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
• CPC Class Codes: G06Q 10/02 Reservations, e.g. for tickets, services or events
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/067 Enterprise or organisation modelling
• CPC Class Codes: G06Q 30/0631 Item recommendations
• CPC Class Codes: G06Q 50/14 Travel agencies

Abstract

The present disclosure relates to a system and method for recommending a service type to a user. The method comprises obtaining and storing in the device a plurality of previous service requests placed by the user, wherein each of the plurality of previous service requests comprises order information including the type of requested service and at least one of a service time or a service location. The method also comprises using the processor to generate a service type prediction model based on the order information of the plurality of previous service requests. The method further comprises receiving a service request including at least one of a currently service time or a currently service location from the user and using the service type prediction model to predict the user'"'"'s preferred service type.

Claims

1-15. -15. (canceled)

16. A method implemented on a device having at least one processor and at least one computer-readable storage medium for recommending a transportation mode on a travel itinerary to a user, wherein the travel itinerary comprises a plurality of segments, each having a segment route, the method comprising:

obtaining and storing, in the storage medium, the user'"'"'s previous travel data for each segment route that comprises departure location and time (departure data), arrival location and time (arrival data), and transportation mode used;

retrieving and using, by the processor, the departure data and the arrival data of the segments to determine a correlation between each of the segment routes to generate personalized travel itineraries for the user that each includes one or more segment route;

retrieving and using, by the processor, the transportation mode of each segment to determine, during each personalized travel itinerary, a usage frequency of each transportation mode during each segment route to establish a transportation mode estimating model;

selecting a recommended travel itinerary comprising recommended segments from the personalized travel itineraries based on user'"'"'s current location; and

predicting transportation mode for each recommended segment using the transportation mode estimating model to generate recommended transportation mode for each recommended segment.

17. The method of claim 16, further comprising sending the recommended travel itinerary with its accompany recommended transportation mode for each recommended segment to the user.

18. The method of claim 16 or 17, wherein the personalized travel itinerary is generated by:

determining time gap between each two sequential segments S_i and S_i+1 based on the arrival time of S_i and the departure time of S_i+1;

if the time gap is smaller than or equal to a first time gap threshold, generating a correlation between the two sequential segments; and

connecting correlated segments to generate the personalized travel itinerary.

19. The method of claim 18, further comprising:

if the time gap is larger than the first time gap threshold but smaller than or equal to a second time gap threshold,

determining a distance gap between each two sequential segments S_i and S_i+1 based on the arrival location of S_i and the departure location of S_i+1;

if the distance gap is smaller than or equal to a first distance gap threshold, generating a correlation between the two sequential segments; and

connecting correlated segments to generate the personalized travel itinerary.

20. The method of claim 16, further including:

obtaining a road condition on each segment; and

optimizing the transportation mode estimating model based on the road condition on each segment.

21. A system for recommending a service type to a user, comprising:

at least one storage medium including a set of instructions; and

at least one processor configured to communicate with the at least one storage medium, wherein when executing the set of instructions, the at least one processor is directed to;

obtain and store in the device a plurality of previous service requests placed by the user, wherein each of the plurality of previous service requests comprises order information including the type of requested service and at least one of a service time or a service location;

use the processor to generate a service type prediction model based on the order information of the plurality of previous service requests;

receive a service request including at least one of a currently service time or a currently service location from the user; and

use the service type prediction model to predict the user'"'"'s preferred service type.

22. The system of claim 21, wherein the preferred service type has its own user interface, and at least one processor is further directed to provide the preferred service type user interface to the user.

23. The system of claim 21 or 22, wherein the service location includes a specific location or a statistical geographic area including the specific location.

24. The system of claim 21, wherein the service time includes a specific time point or a targeted statistical time period including the specific time point.

25. The system of claim 21, wherein the service type prediction model is based on at least one of Markov model, Gaussian model, mixed Gaussian model, Bayesian model.

26. The system of claim 21, wherein the service type prediction model is generated by:

performing clustering analysis on the types of the services requested on the basis of corresponding at least one of the service time or the service location to obtain a mixed Gaussian model for each service type, and

determining, according to the mixed Gaussian model, a probability density value for each type of service, wherein the user'"'"'s preferred service type is predicted based on the probability density values.

27. The system of claim 26, wherein the user'"'"'s preferred service type is predicted by:

using a Bayesian model and the probability density value to determine a first probability value for each type of service; and

designating the type of service that has the highest first probability value as the user'"'"'s preferred service type.

28. The system of claim 27, wherein designating the type of service that has the highest first probability value as the user'"'"'s preferred service type includes:

determining the type of service that has the highest first probability value and if the highest first probability value is larger than or equal to a first preset threshold; and

if the highest first probability value is larger than or equal to the first preset threshold, designating and recommending the type of service that has the highest first probability value as the user'"'"'s preferred service type.

29. The system of claim 26, before implementing the steps of claim 26, the at least one processor is further directed to:

determine the frequency of each service type based on the order information of the plurality of previous service requests placed by the user to generate a Markov model;

obtain a second probability value for each service type by inputting the immediate previous service type requested by the user into the Markov model;

determine the service type that has the highest second probability value and if the highest second probability value is larger than or equal to a second preset threshold; and

if the highest second probability value is larger than or equal to the second preset threshold, designate and recommend the service type that has the highest second probability value as the user'"'"'s preferred service type, or

if the highest second probability value is smaller than the second preset threshold, perform the steps of claim 26.

30-35. -35. (canceled)

36. A system for recommending a transportation mode on a travel itinerary to a user, wherein the travel itinerary comprises a plurality of segments, each having a segment route, comprising:

at least one storage medium including a set of instructions; and

at least one processor configured to communicate with the at least one storage medium, wherein when executing the set of instructions, the at least one processor is directed to;

obtain and store, in the storage medium, the user'"'"'s previous travel data for each segment route that comprises departure location and time (departure data), arrival location and time (arrival data), and transportation mode used;

retrieve and use, by the processor, the departure data and the arrival data of the segments to determine a correlation between each of the segment routes to generate personalized travel itineraries for the user that each includes one or more segment route;

retrieve and use, by the processor, the transportation mode of each segment to determine, during each personalized travel itinerary, a usage frequency of each transportation mode during each segment route to establish a transportation mode estimating model;

select a recommended travel itinerary comprising recommended segments from the personalized travel itineraries based on user'"'"'s current location; and

predict transportation mode for each recommended segment using the transportation mode estimating model to generate recommended transportation mode for each recommended segment.

37. The system of claim 36, the at least one processor is further directed to send the recommended travel itinerary with its accompany recommended transportation mode for each recommended segment to the user.

38. The system of claim 36 or 37, wherein the personalized travel itinerary is generated by:

determining time gap between each two sequential segments S_i and S_i+1 based on the arrival time of S_i and the departure time of S_i+1;

if the time gap is smaller than or equal to a first time gap threshold, generating a correlation between the two sequential segments; and

connecting correlated segments to generate the personalized travel itinerary.

39. The system of claim 38, further comprising:

if the time gap is larger than the first time gap threshold but smaller than or equal to a second time gap threshold,

determining a distance gap between each two sequential segments S_i and S_i+1 based on the arrival location of S_i and the departure location of S_i+1;

if the distance gap is smaller than or equal to a first distance gap threshold, generating a correlation between the two sequential segments; and

connecting correlated segments to generate the personalized travel itinerary.

40. The system of claim 36, the at least one processor is further directed to:

obtain a road condition on each segment; and

optimize the transportation mode estimating model based on the road condition on each segment.

41-43. -43. (canceled)