Intelligent on-demand management of ride sharing in a transportation system

Patent metadata

Field	Value
Publication number	US-11574377-B2
Application number	US-201916429273-A
Country	US
Kind code	B2
Filing date	Jun 3, 2019
Priority date	Jun 3, 2019
Publication date	Feb 7, 2023
Grant date	Feb 7, 2023

Abstract

Embodiments for providing intelligent transportation service management in a transportation system by a processor. Transportation service requests may be assigned amongst multiple transportation service providers according to one or more transportation service request distribution models and various parameters and preferences for each user. The transportation service request distribution models may protect information relation to each of the transportation service providers and suggests a selected order for distributing the plurality of transportation service requests.

First claim

The invention claimed is:

1. A method, by a processor, for providing intelligent transportation service management in a transportation system, comprising:
receiving, by the processor, a plurality of data representative of transportation service information associated with a plurality of transportation service providers and a plurality of users, wherein each of the transportation service providers are an independent ride sharing company having a plurality of vehicles-for-hire associated therewith;
executing machine learning logic, by the processor, using a machine learning operation to generate one or more transportation service request distribution models according to the plurality of data, wherein executing the machine learning logic includes analyzing and correlating historical transportation service request data, one or more parameters and preferences for each of the plurality of users, and one or more contextual factors associated with a journey relating to a plurality of transportation service requests submitted by the plurality of users;
assigning, by the processor via a transportation broker, the plurality of transportation service requests amongst the plurality of transportation service providers according to the one or more transportation service request distribution models and the one or more parameters and preferences of each of the plurality of users such that the transportation broker centralizes coordination of distribution of the transportation service requests amongst each independent ride sharing company, wherein the one or more transportation service request distribution models protect consumer and business information inclusive of business-specific vehicle identifications and proprietary pricing strategies relating to each of the plurality of transportation service providers and suggest a selected order for distributing the plurality of transportation service requests to the plurality of transportation service providers; and
in conjunction with the assigning, assigning the plurality of transportation service requests amongst the plurality of transportation service providers in batches over a selected time period according to a distributed auction operation, wherein the distributed auction operation uses the transportation broker to iteratively relay one of the batches having a predetermined number of the transportation service requests to each of the transportation service providers, receive a top k number of offerings of bids for providing service from the transportation service providers for each iteration, perform the assigning of accepted offers of the plurality of transportation service requests, and require unassigned transportation service providers to raise the bids to lower a profit for the unassigned transport service providers during a subsequent iteration.

2. The method of claim 1 , further including receiving the plurality of transportation service requests from the plurality of users over the selected time period, wherein each transportation service request includes the one or more parameters and preferences of a respective user of the plurality of users.

3. The method of claim 1 , further including:
forecasting transportation service request demands for the plurality of users over a selected period of time; and
estimating a number of transportation service provider vehicles required to service the forecasted number transportation service requests.

4. The method of claim 1 , further including:
learning, by the machine learning operation, the parameters and preferences of the plurality of users;
learning, by the machine learning operation, transportation service request demand for the plurality of users based on the historical transportation service request data; and
learning, by the machine learning operation, the one or more contextual factors associated with the journey relating to the plurality of transportation service requests, wherein the one or more contextual factors include traffic data, weather data, road conditions, road types, or a combination thereof.

5. The method of claim 1 , further including suggesting both the selected order for distributing the plurality of transportation service requests and a minimum number of the transportation service providers for servicing the plurality of transportation service requests.

6. The method of claim 1 , further including maintaining a degree of fairness between the plurality of transportation service providers for servicing the plurality of transportation service requests over a selected period of time.

7. The method of claim 6 , wherein maintaining the degree of fairness includes prioritizing the one or more parameters and preferences of the plurality of users and minimizing transportation related time and costs.

8. A system for providing intelligent transportation service management in a transportation system, comprising:
one or more computers with executable instructions that when executed cause the system to:
receive, by a processor executing the executable instructions, a plurality of data representative of transportation service information associated with a plurality of transportation service providers and a plurality of users, wherein each of the transportation service providers are an independent ride sharing company having a plurality of vehicles-for-hire associated therewith;
execute machine learning logic, by the processor, using a machine learning operation to generate one or more transportation service request distribution models according to the plurality of data, wherein executing the machine learning logic includes analyzing and correlating historical transportation service request data, one or more parameters and preferences for each of the plurality of users, and one or more contextual factors associated with a journey relating to a plurality of transportation service requests submitted by the plurality of users;
assign, by the processor via a transportation broker, the plurality of transportation service requests amongst the plurality of transportation service providers according to the one or more transportation service request distribution models and the one or more parameters and preferences of each of the plurality of users such that the transportation broker centralizes coordination of distribution of the transportation service requests amongst each independent ride sharing company, wherein the one or more transportation service request distribution models protect consumer and business information inclusive of business-specific vehicle identifications and proprietary pricing strategies relating to each of the plurality of transportation service providers and suggest a selected order for distributing the plurality of transportation service requests to the plurality of transportation service providers; and
in conjunction with the assigning, assign the plurality of transportation service requests amongst the plurality of transportation service providers in batches over a selected time period according to a distributed auction operation, wherein the distributed auction operation uses the transportation broker to iteratively relay one of the batches having a predetermined number of the transportation service requests to each of the transportation service providers, receive a top k number of offerings of bids for providing service from the transportation service providers for each iteration, perform the assigning of accepted offers of the plurality of transportation service requests, and require unassigned transportation service providers to raise the bids to lower a profit for the unassigned transport service providers during a subsequent iteration.

9. The system of claim 8 , wherein the executable instructions further receive the pl

Assignees

• IBM

Inventors

• Monteil Julien
• Gambella Claudio
• SIMONETTO Andrea
• LASSOUED Yassine
• Dekusar Anton
• Mevissen Martin

Classifications

• G06Q10/06315 Primary

  Needs-based resource requirements planning or analysis · CPC title

• G06Q10/067 Primary

  Enterprise or organisation modelling · CPC title

• G06Q50/30 Primary

  Physics · mapped topic

• G06Q50/40 Primary

  Business processes related to the transportation industry (shipping G06Q10/083) · CPC title