Parking identification and availability prediction

• US 10,446,028 B2
• Filed: 09/12/2018
• Issued: 10/15/2019
• Est. Priority Date: 11/19/2014
• Status: Active Grant

• Current Assignee: Uber Technologies, Inc.
• Original Assignee: Uber Technologies, Inc.
• Inventors: Agrawal, Laxmikant, Pratipati, Sudheer, Colle, Audrey, De Oliveira, Jose, Couckuyt, Jeff
• Primary Examiner(s): Trieu, Van T
• Application Number:
  US16/128,809
• Publication Number:
  US 20190012914A1
• Time in Patent Office: 398 Days
• 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 1/14 indicating individual free spaces in parking areas
• CPC Class Codes: G08G 1/141 with means giving the indication of available parking spaces
• CPC Class Codes: G08G 1/143 inside the vehicles
• CPC Class Codes: G08G 1/144 on portable or mobile units, e.g. personal digital assistant [PDA]
• CPC Class Codes: G08G 1/146 where the parking area is a limited parking space, e.g. parking garage, restricted space
• CPC Class Codes: G08G 1/147 where the parking area is within an open public zone, e.g. city centre
• CPC Class Codes: G08G 1/148 Management of a network of parking areas

Abstract

A system includes a model generating component to generate a prediction tree model based on training data and an input component to receive input data including a destination in a geographical area. A computation component identifies at least one parking venue or at least one parking space near the destination in the geographical area and to generate at least one parking prediction corresponding to the at least one parking venue or the at least one parking space based at least in part on applying the input data to the prediction tree model. A presentation component presents the at least one parking venue or the at least one parking space and to present the at least one parking prediction to a user.

Claims

1. A system, comprising:

a model generating component including a parking prediction model trained on training data;

an input component to receive input data, the input data including a destination;

a computation component configured to;

identify at least one parking venue near the destination;

identify an event occurring at an event venue near the destination;

retrieve a capacity for the event venue and an event type for the identified event;

calculate a crowd index based on the retrieved capacity and event type, wherein the crowd index is indicative of an estimate of a crowd size at the destination; and

generate at least one parking prediction corresponding to the at least one parking venue based at least in part on applying the input data and the calculated crowd index to the parking prediction model;

a presentation component to present the at least one parking venue and the at least one parking prediction to a user; and

a microprocessor to execute computer-executable instructions associated with at least one of the model generating component, the input component, the computation component, or the presentation component.

2. The system of claim 1, wherein the event venue near the destination is within a threshold distance of the destination.

3. The system of claim 1, wherein the training data is from at least one data source.

4. The system of claim 1, wherein the training data comprises records for each of a plurality of parking venues, each parking venue having associated therewith an address, a number of parking spaces, an indoor or outdoor designation, a type of parking service offered, a size of each of the number of parking spaces, fee structure, hours of operation, on-site equipment, limitations, or payment options.

5. The system of claim 1, wherein the input data further comprises distance data, vehicle data, calendar data, or preference data.

6. The system of claim 5, wherein the distance data comprises walking distance, driving distance, or geographical distance between the destination and a parking venue.

7. The system of claim 5, wherein the vehicle data comprises type of vehicle, make of the vehicle, or dimensions of the vehicle.

8. The system of claim 5, wherein the preference data comprises a fee structure preference, an hours of operation preference, a parking space size preference, or an equipment preference.

9. The system of claim 5,

wherein identifying the event is based on the calendar data.

10. The system of claim 1, wherein the presentation component is further configured to present the at least one parking prediction sorted based upon the crowd index.

11. A computer-implemented method, comprising:

maintaining a parking prediction model trained on training data;

receiving input data, the input data including a destination;

identifying at least one parking venue near the destination;

identifying an event occurring at an event venue near the destination;

retrieving a capacity for the event venue and an event type for the identified event;

calculating a crowd index based on the retrieved capacity and the event type, wherein the crowd index is indicative of an estimate of a crowd size at the destination;

determining at least one parking prediction corresponding to the identified at least one parking venue based at least in part on applying the input data and the calculated crowd index to the parking prediction model; and

presenting the identified at least one parking venue and the at least one parking prediction to a user.

12. The method of claim 11, wherein the event venue near the destination is within a threshold distance of the destination.

13. The method of claim 11,

wherein the training data comprises a plurality of records corresponding to a plurality of parking venues, each parking venue having associated therewith an address, a number of parking spaces, an indoor or outdoor designation, a type of parking service offered, a size of each of the number of parking spaces, fee structure, hours of operation, on-site equipment, limitations, or payment options;

and

wherein the input data comprises calendar data, distance data, vehicle data, or preference data.

14. The method of claim 13,

wherein the calendar data comprises time of day, the day of a week, the day of a month, or the month of a year;

wherein the distance data comprises walking distance, driving distance, or geographical distance between the destination and a parking venue;

wherein the vehicle data comprises type of vehicle, make of the vehicle, or dimensions of the vehicle; and

wherein the preference data comprises a fee structure preference, an hours of operation preference, a parking space size preference, or an equipment preference.

15. The method of claim 14, further comprising:

presenting the at least one parking prediction sorted based upon the crowd index.

16. A computer program product, the computer program product stored on a non-transitory computer-readable medium and including instructions configured to cause a processor to execute steps comprising:

maintaining a parking prediction model trained on training data;

receiving input data, the input data including a destination;

identifying at least one parking venue near the destination;

identifying an event occurring at an event venue near the destination;

retrieving a capacity for the event venue and an event type for the identified event;

calculating a crowd index based on the retrieved capacity and the event type, wherein the crowd index is indicative of an estimate of a crowd size at the destination;

determining at least one parking prediction corresponding to the identified at least one parking venue based at least in part on applying the input data and the calculated crowd index to the parking prediction model; and

presenting the identified at least one parking venue and the at least one parking prediction to a user.

17. The computer program product of claim 16 wherein the event venue near the destination is within a threshold distance of the destination.

18. The computer program product of claim 16,

wherein the training data comprises a plurality of records corresponding to a plurality of parking venues, each parking venue having associated therewith an address, a number of parking spaces, an indoor or outdoor designation, a type of parking service offered, a size of each of the number of parking spaces, fee structure, hours of operation, on-site equipment, limitations, or payment options;

and

wherein the input data comprises calendar data, distance data, vehicle data, or preference data.

19. The computer program product of claim 18,

wherein the calendar data comprises time of day, the day of a week, the day of a month, or the month of a year;

wherein the distance data comprises walking distance, driving distance, or geographical distance between the destination and a parking venue;

wherein the vehicle data comprises type of vehicle, make of the vehicle, or dimensions of the vehicle; and

wherein the preference data comprises a fee structure preference, an hours of operation preference, a parking space size preference, or an equipment preference.

20. The computer program product of claim 19, further comprising:

presenting the at least one parking prediction sorted based upon the crowd index.