Traffic prediction and real time analysis system

• US 9,754,485 B2
• Filed: 06/16/2015
• Issued: 09/05/2017
• Est. Priority Date: 06/16/2015
• Status: Active Grant

• Current Assignee: DataSpark Pte. Ltd.
• Original Assignee: DataSpark Pte. Ltd.
• Inventors: Holleczek, Thomas Martin, Jin, Yunye, Dang, The Anh, Jayakumaran, Deepak, Shi-Nash, Amy Xuemei
• Primary Examiner(s): Edwards, Jerrah
• Assistant Examiner(s): Williams, Kelly D
• Application Number:
  US14/741,009
• Publication Number:
  US 20160371973A1
• Time in Patent Office: 812 Days
• CPC Class Codes: G06F 9/547 Remote procedure calls [RPC]; Web services
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• 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: H04W 4/021 Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
• CPC Class Codes: H04W 4/029 Location-based management or tracking services

Abstract

A traffic routing and analysis system uses data from individual cellular or mobile devices to determine traffic density within a transportation network, such as subways, busses, roads, pedestrian walkways, or other networks. The system may use historical data derived from monitoring people'"'"'s travel patterns, and may compare historical data to real time or near real time data to detect abnormalities. The system may be used for policy analysis, predicted commute times and route selection based on traffic patterns, as well as broadcast statistics that may be displayed to commuters. The system may be accessed through an application programming interface (API) for various applications, which may include applications that run on mobile devices, desktop or cloud based computers, or other devices.

Claims

1. A method performed by at least one computer processor, said method comprising:

receiving a plurality of location data points, each of said location data points being generated from a mobile device carried by individual users;

for each of said individual users, determining a mode of transportation at least in part from said location data points associated with said individual users;

determining a density of users from said plurality of location data points;

displaying said density of users for at least one mode of transportation on a visual representation of a transportation system;

storing said density of users in a historical database;

determining a transportation model comprising a baseline density profile for each of a plurality of locations within said transportation system;

identifying a real time abnormality in said transportation system from analysis of said plurality of location data points; and

predicting a future abnormality based on said real time abnormality.

2. The method of claim 1 further comprising:

adjusting a portion of said transportation system in response to said future abnormality.

3. The method of claim 1, said mode of transportation is determined by analyzing movement behavior.

4. The method of claim 3, said movement behavior is analyzed using heuristics.

5. The method of claim 3, said movement behavior is analyzed using historical movement behavior for a subset of said individual users.

6. The method of claim 1 further comprising:

determining a demographic profile for a subset of said individual users.

7. The method of claim 6, said demographic profile being derived in part from an external data source.

8. The method of claim 1 further comprising:

receiving said location data points for a first user and determining a transportation mode for said first user.

9. The method of claim 3 further comprising:

said movement behavior being analyzed using a transportation model comprising historical movement behavior for a subset of said individual users.

10. The method of claim 9, said historical movement behavior comprising a set of movement segments.

11. The method of claim 9, said transportation model further comprising a variance for each of said movement segments.

12. The method of claim 11, said transportation model further comprising a baseline density profile for each of a plurality of locations within said transportation system.

13. A hardware platform comprising a programmable computer processor;

an interface comprising;

an input mechanism that receives location data points for individual users;

an output mechanism that transmits traffic density data;

a database of historical location data;

an analysis system operable on said computer processor, said analysis system performing operations comprising;

receiving a plurality of location data points, each of said location data points being generated from a mobile device carried by individual users;

for each of said individual users, determining a mode of transportation at least in part from said location data points associated with said individual users;

determining a density of users from said plurality of location points;

displaying said density of users for at least one mode of transportation on a visual representation of a transportation system;

storing said density of users in a historical database;

determining a transportation model comprising a baseline density profile for each of a plurality of locations within said transportation system;

identifying a real time abnormality in said transportation system from analysis of said plurality of location data points; and

predicting a future abnormality based on said real time abnormality.

14. The system of claim 13, said operations further comprising:

adjusting a portion of said transportation system in response to said future abnormality.

15. The system of claim 13, said mode of transportation is determined by analyzing movement behavior.

16. The system of claim 15, said movement behavior is analyzed using heuristics.

17. The system of claim 15, said movement behavior is analyzed using historical movement behavior for a subset of said individual users.

18. The system of claim 13 said operations further comprising:

determining a demographic profile for a subset of said individual users.

19. The system of claim 18, said demographic profile being derived in part from an external data source.

20. The system of claim 13 said operations further comprising:

receiving said location data points for a first user and determining a transportation mode for said first user.

21. The system of claim 15 said operations further comprising:

said movement behavior being analyzed using a transportation model comprising historical movement behavior for a subset of said individual users.

22. The system of claim 21, said historical movement behavior comprising a set of movement segments.

23. The system of claim 21, said transportation model further comprising a variance for each of said movement segments.

24. The system of claim 23, said transportation model further comprising a baseline density profile for each of a plurality of locations within said transportation system.