MONITORING TRAFFIC CONGESTION

• US 20180165952A1
• Filed: 12/13/2016
• Published: 06/14/2018
• Est. Priority Date: 12/13/2016
• Status: Active Grant

• Current Assignee: SAP SE
• Original Assignee: SAP SE
• Inventors: LIU, Jing, ZHANG, Qiwei, LIU, Jun, NI, Yinling
• Granted Patent: US 10,163,339 B2
• Time in Patent Office: Days
• CPC Class Codes: G08G 1/0112 from the vehicle, e.g. floating car data [FCD]
• CPC Class Codes: G08G 1/0133 for classifying traffic situation

Abstract

Described herein is a framework to monitor traffic congestion. In accordance with one aspect of the framework, the framework receives vehicle data from vehicle data sources located in a region of interest. The framework may determine a sample size and an average speed for an edge of the region of interest based on the vehicle data. Congestion probability may then be determined based on the sample size and average speed. A report may be presented based on the congestion probability.

Claims

1. A computer system for monitoring traffic congestion, comprising:

a non-transitory memory device for storing computer-readable program code; and

a processor in communication with the memory device, the processor being operative with the computer-readable program code to perform operations including

receiving vehicle data from vehicle data sources located in a region of interest,

determining a sample size and an average speed for an edge of the region of interest based on the vehicle data, wherein inverse of a variance of the average speed is linearly related to the sample size within a range of sample sizes,

determining congestion probability based on the sample size and the average speed, and

presenting a report based on the congestion probability.

2. The computer system of claim 1 wherein the vehicle data comprises sequential data records the vehicle data sources, wherein at least one of the sequential data records comprises a device identifier, localization data, speed, time, or a combination thereof.

3. The computer system of claim 1 wherein the vehicle data sources comprise onboard mobile devices capable of continuously streaming the vehicle data.

4. The computer system of claim 1 wherein the report comprises a map of the region of interest indicating different levels of congestion based on the congestion probability.

5. A method of monitoring traffic congestion, comprising:

receiving vehicle data from vehicle data sources located in a region of interest;

determining a sample size and an average speed for an edge of the region of interest based on the vehicle data;

determining congestion probability based on the sample size and the average speed; and

presenting a report based on the congestion probability.

6. The method of claim 5 wherein determining the congestion probability comprises determining a variance of the average speed.

7. The method of claim 6 wherein determining the congestion probability comprises determining a slope of a graph of the inverse of the variance against the sample size.

8. The method of claim 7 wherein determining the slope of the graph comprises determining the slope based on historical vehicle data.

9. The method of claim 7 wherein determining the congestion probability comprises modeling distribution of true speed using a normal probability model, wherein a mean of the normal probability model is the average speed and a variance of the normal probability model is based on the slope.

10. The method of claim 9 wherein determining the congestion probability comprises determining a cumulative distribution of the normal probability model.

11. The method of claim 10 wherein determining the cumulative distribution Pr comprises determining

12. The method of claim 11 wherein the congestion threshold speed is selected based on a standardized grade level of the edge.

13. The method of claim 5 wherein presenting the report based on the congestion probability comprises displaying a map of the region of interest indicating different levels of congestion based on the congestion probability.

14. The method of claim 13 further comprises updating the map in real-time in response to receiving new vehicle data.

15. The method of claim 13 wherein the map indicates the different levels of congestion with different colors.

16. The method of claim 13 wherein displaying the map comprises marking all severely congested edges in the map.

17. The method of claim 13 wherein displaying the map comprises marking edges with confidence levels greater than a predetermined confidence level.

18. One or more non-transitory computer-readable media having stored thereon program code, the program code executable by a computer to perform steps comprising:

receiving vehicle data from vehicle data sources located in a region of interest;

determining a sample size and an average speed for an edge of the region of interest based on the vehicle data;

determining congestion probability based on the sample size and the average speed; and

presenting a report based on the congestion probability.

19. The one or more non-transitory computer-readable media of claim 18 wherein the program code executable by the computer to determine the congestion probability by determining a slope of a graph of the inverse of the variance against the sample size.

20. The one or more non-transitory computer-readable media of claim 19 wherein the program code executable by the computer to determine the congestion probability by modeling distribution of true speed using a normal probability model, wherein a mean of the normal probability model is the average speed and a variance of the normal probability model is based on the slope.