Systems and methods for recommending an estimated time of arrival

• US 10,254,119 B2
• Filed: 05/15/2018
• Issued: 04/09/2019
• Est. Priority Date: 04/18/2016
• Status: Active Grant

• Current Assignee: Beijing Didi Infinity Technology And Development Co.Ltd.
• Original Assignee: Beijing Didi Infinity Technology And Development Co.Ltd.
• Inventors: Wang, Zheng, Sun, Shujuan, Ye, Jieping
• Primary Examiner(s): Cheung, Mary
• Application Number:
  US15/980,643
• Publication Number:
  US 20180259345A1
• Time in Patent Office: 329 Days
• CPC Class Codes: G01C 21/26 specially adapted for navigation in a road network
• CPC Class Codes: G01C 21/3446 Details of route searching algorithms, e.g. Dijkstra, A*, arc-flags, using precalculated routes
• CPC Class Codes: G06F 3/00 Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
• 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 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 50/40 Business processes related to the transportation industry shipping G06Q10/083
• CPC Class Codes: G08G 1/0112 from the vehicle, e.g. floating car data [FCD]
• CPC Class Codes: G08G 1/012 from other sources than vehicle or roadside beacons, e.g. mobile networks
• CPC Class Codes: G08G 1/0129 for creating historical data or processing based on historical data
• CPC Class Codes: G08G 1/0141 for traffic information dissemination
• CPC Class Codes: G08G 1/0968 Systems involving transmission of navigation instructions to the vehicle
• CPC Class Codes: G08G 1/202 Dispatching vehicles on the basis of a location, e.g. taxi dispatching
• CPC Class Codes: H04W 4/029 Location-based management or tracking services

Abstract

The present disclosure relates to systems and methods for determining an estimated time of arrival (ETA) for a route, based on a model of ETA. The systems may perform the methods to obtain a first electrical signal associated with at least one route having at least one road section; generate and save first structured data of at least one global feature vector and at least one historical duration associated with the at least one route based on the first electrical signal; generate second structured data of a model of ETA by training the model based on the at least one global feature vector and the at least one historical duration; and save the second structured data of the model of ETA in the at least one non-transitory computer-readable storage medium.

Claims

1. A system configured to operate an online on-demand transportation service, comprising:

a bus;

logic circuits electronically connected to at least one storage medium via the bus, wherein during operation, the logic circuits load a set of instructions from the at least one storage medium and;

obtain a first electrical signal from the bus, the first electrical signal being associated with at least one route having a plurality of road sections;

generate and save first structured data of at least one global feature vector and at least one historical duration associated with the at least one route based on the first electrical signal, the at least one global feature vector incorporating interactions between the plurality of road sections;

generate second structured data of a model of estimated time of arrival (ETA) by training the model based on the at least one global feature vector and the at least one historical duration; and

save the second structured data of the model of ETA in the at least one storage medium.

2. The system of claim 1, wherein at least two of the at least one global feature vector are integrated into a determinant.

3. The system of claim 2, wherein the at least one global feature vector includes an overall feature vector, wherein the overall feature vector includes a feature indicating a relationship between at least two of the at least one road section.

4. The system of claim 3, wherein the at least two of the at least one road section are apart from each other.

5. The system of claim 3, wherein the overall feature vector includes at least one of traffic status, the traffic status including a real-time road speed or an estimated road speed.

6. The system of claim 1, wherein the model of ETA includes at least one of a regression model, a decision tree model, an artificial neural network model, a support vector regression model, a restricted Boltzmann Machine model, or a genetic algorithm.

7. The system of claim 1, wherein the model of ETA includes a plurality of sub-models of ETA, the plurality of sub-models of ETA corresponding to at least one of time interval or region in the map.

8. The system of claim 1, wherein the logic circuits further:

receive a second electrical signal from the bus, the second electrical signal being associated with an intended route having at least one road section;

generate and save third structured data of at least one intended global feature vector associated with the intended route based on the second electrical signal; and

execute the second structured data of the model of ETA to determine an ETA corresponding to the intended route, based on the intended feature vector and the model of ETA.

9. The system of claim 8, wherein to determine the ETA corresponding to the intended route, the logic circuits further:

determine a time interval of a starting time of the intended route; and

select, based on the starting time, the sub-model of ETA corresponding to time interval.

10. The system of claim 8, wherein to determine the ETA corresponding to the intended route, the logic circuits further:

determine a region of a starting location or an ending location of the intended route; and

select, based on the starting location and the ending location, the sub-model of ETA corresponding to the region in the map.

11. A method configured to operate an online on-demand transportation service on at least one electronic device having logic circuits, at least one storage medium, and a communication platform connected to a network, comprising:

obtaining, by the logic circuits, a first electrical signal associated with at least one route having a plurality of road sections;

generating and saving, by the logic circuits, first structured data of at least one global feature vector and at least one historical duration associated with the at least one route based on the first electrical signal, the at least one global feature vector incorporating interactions between the plurality of road sections;

generating, by the logic circuits, second structured data of a model of estimated time of arrival (ETA) by training the model based on the at least one global feature vector and the at least one historical duration; and

saving, by the logic circuits, the second structured data of the model of ETA in the at least one storage medium.

12. The method of claim 11, wherein at least two of the at least one global feature vector are integrated into a determinant.

13. The method of claim 12, wherein the at least one global feature vector includes an overall feature vector, wherein the overall feature vector includes a feature indicating a relationship between at least two of the at least one road section.

14. The method of claim 13, wherein the at least two of the at least one road section are apart from each other.

15. The method of claim 13, wherein the overall feature vector includes at least one of traffic status, the traffic status including a real-time road speed or an estimated road speed.

16. The method of claim 11, wherein the model of ETA includes at least one of a regression model, a decision tree model, an artificial neural network model, a support vector regression model, a restricted Boltzmann Machine model, or a genetic algorithm.

17. The method of claim 11, wherein the model of ETA includes a plurality of sub-models of ETA, the plurality of sub-models of ETA corresponding to at least one of time interval or region in the map.

18. The method of claim 11, further comprising:

receiving, by the logic circuits, a second electrical signal associated with an intended route having at least one road section;

generating and saving, by the logic circuits, third structured data of at least one intended global feature vector associated with the intended route based on the second electrical signal; and

executing, by the logic circuits, the second structured data of the model of ETA to determine an ETA corresponding to the intended route, based on the intended feature vector and the model of ETA.

19. The method of claim 18, wherein determining the ETA corresponding to the intended route comprising:

determining, by the logic circuits, a time interval of a starting time of the intended route; and

selecting, by the logic circuits, based on the starting time, the sub-model of ETA corresponding to time interval.

20. A non-transitory computer-readable storage medium including instructions that, when accessed by at least one processor of an online on-demand service platform from a requestor terminal, causes the at least one processor to:

obtain a first electrical signal associated with at least one route having a plurality of road sections;

generate and save first structured data of at least one global feature vector and at least one historical duration associated with the at least one route based on the first electrical signal, the at least one global feature vector incorporating interactions between the plurality of road sections;

generate second structured data of a model of estimated time of arrival (ETA) by training the model based on the at least one global feature vector and the at least one historical duration; and

save the second structured data of the model of ETA in the non-transitory computer-readable storage medium.