Method and device for processing traffic road information

• US 10,339,800 B2
• Filed: 05/25/2016
• Issued: 07/02/2019
• Est. Priority Date: 09/11/2015
• Status: Active Grant

• Current Assignee: Hangzhou Hikvision Digital Technology Company Limited
• Original Assignee: Hangzhou Hikvision Digital Technology Company Limited
• Inventors: Shen, Yefeng
• Primary Examiner(s): Camby, Richard M
• Application Number:
  US15/759,445
• Publication Number:
  US 20180261082A1
• Time in Patent Office: 1,133 Days
• Field of Search: None
• CPC Class Codes: G08G 1/0116 from roadside infrastructure, e.g. beacons
• CPC Class Codes: G08G 1/0133 for classifying traffic situation
• CPC Class Codes: G08G 1/0141 for traffic information dissemination

Abstract

The present application discloses a method and an apparatus for processing traffic road information. The method includes: obtaining acquired traffic parameters of a first target road section and/or the reliability of the traffic parameters within a first preset period; selecting a first fuzzy rule matrix table from a pre-stored set of fuzzy rule matrix tables based on the number of the traffic parameters and/or the reliability of the traffic parameters of the first target road section; determining the membership degree of each type of traffic conditions contained in the first fuzzy rule matrix table by calling a membership function; comparing the membership degrees of all types of traffic conditions contained in the first fuzzy rule matrix table to determine the real-time traffic condition for the first target road section within the first preset period. The present application solves technical problems in solutions of computing traffic states of a road by using a fuzzy rule in the prior art that analysis results for traffic road information are inaccurate due to a single fuzzy rule.

Claims

1. A method for processing traffic road information, comprising:

obtaining acquired traffic parameters of a first target road section and/or the reliability of the traffic parameters within a first preset period, wherein the traffic parameters at least comprise any one or more of the following parameters;

a vehicle time occupancy rate, flow saturation of vehicle flow, and a vehicle speed;

selecting a first fuzzy rule matrix table from a pre-stored set of fuzzy rule matrix tables based on the number of the traffic parameters of the first target road section and/or the reliability of the traffic parameters, wherein the fuzzy rule matrix tables comprise any one of the following types of matrix tables;

a one-dimensional fuzzy rule matrix table, a two-dimensional fuzzy rule matrix table, and a three-dimensional fuzzy rule matrix table;

determining a membership degree for each type of traffic conditions contained in the first fuzzy rule matrix table by calling a membership function, wherein the traffic conditions at least comprise the following types;

Unblocked, Slow and Congested; and

comparing the membership degrees of all types of traffic conditions contained in the first fuzzy rule matrix table to determine a real-time traffic condition for the first target road section within the first preset period.

2. The method of claim 1, wherein in the case that there are at least two traffic parameters of the first target road section, the reliability of the traffic parameters of the first target road section is a combination of the reliability of each of the parameters, wherein selecting a first fuzzy rule matrix table from a pre-stored set of fuzzy rule matrix tables based on the number of the traffic parameters of the first target road section and/or the reliability of the traffic parameters comprises:

obtaining a group of fuzzy rule matrix tables from the pre-stored set of fuzzy rule matrix tables based on the number of the traffic parameters of the first target road section, wherein the dimension of each fuzzy rule matrix table contained in the group of fuzzy rule matrix tables is the same as the number of the parameters; and

selecting a fuzzy rule matrix table that matches with the reliability of the traffic parameters of the first target road section from the group of fuzzy rule matrix tables to obtain the first fuzzy rule matrix table.

3. The method of claim 1, wherein before obtaining acquired traffic parameters of a first target road section and/or the reliability of the traffic parameters within a first preset period, the method further comprises:

acquiring traffic data of the first target road section by using a plurality of traffic detection devices within the first preset period, wherein the plurality of traffic devices at least comprise a combination of any of the following devices;

a magnetic frequency vehicle detector, a wave frequency vehicle detector, a video vehicle detector, a coil vehicle detector, a microwave vehicle detector, a geomagnetic vehicle detector and a SCATS vehicle detector;

preprocessing the traffic data to obtain traffic parameters of the first target road section, wherein the preprocessing comprises at least one or more of the following processings;

filtering of the traffic data, time-space conversion of the traffic data, and data conversion of the traffic data.

4. The method of claim 3, wherein preprocessing the traffic data to obtain traffic parameters of the first target road section comprises:

filtering the traffic data of the first target road section acquired by each of the traffic detection devices respectively according to preset filter conditions to obtain the filtered traffic data acquired by each of the traffic detection devices, wherein the filter conditions at least comprise one or more of the following conditions;

device parameters of the traffic detection devices, vehicle speed limits for different traffic conditions, vehicle flow limits for different types of roads, the vehicle time occupancy rate, correlations between different types of traffic parameters;

performing the time-space conversion and/or data conversion on the filtered traffic data acquired by each of the traffic detection devices to obtain the traffic parameters of the first target road section.

5. The method of claim 4, wherein the traffic data at least comprise one or more types of the following parameters:

a vehicle time occupancy rate, flow saturation of vehicle flow, and a vehicle speed, wherein performing the data conversion on the filtered traffic data acquired by each of the traffic detection devices to obtain the traffic parameters of the first target road section comprises;

calculating the reliability of each type of parameters detected by each of the traffic detection devices within the first preset period based on detection accuracy of each of the traffic detection devices and the data amount of each type of the parameters actually acquired within the first preset period; and

calculating weighted average of each type of the parameters actually acquired by using the reliability of each type of parameters as weighting factors to obtain the traffic parameters of the first target road section within the first preset period;

wherein the reliability of the traffic parameters is obtained by averaging the reliability of a same type of parameters detected by each of the traffic detection devices.

6. The method of claim 1, wherein in the case that a traffic data release period comprises a plurality of time periods, each of which has a same duration as the first preset period, after comparing the membership degrees of all types of traffic conditions contained in the first fuzzy rule matrix table to determine a real-time traffic condition for the first target road section within the first preset period, the method further comprises:

obtaining the reliability of real-time traffic conditions for the first target road section within each of the time periods of the traffic data release period;

accumulating the reliability of traffic conditions of a same type within each of the time periods to obtain a accumulated reliability value for each type of traffic conditions;

determining a traffic condition with the highest accumulated reliability value as the real-time traffic condition for the first target road section within the traffic data release period.

7. The method of claim 6, wherein obtaining the reliability of real-time traffic conditions for the first target road section within each of the time periods of the traffic data release period comprises:

calculating, for each of the time periods, a proportion of time in which the traffic on the first target road section is in a passing state;

calculating the reliability of the real-time traffic conditions for the first target section within each of the time periods based on the proportion of time in which the traffic is in the passing state and the reliability of the acquired traffic parameters of the first target road section within each of the time periods.

8. The method of claim 6, wherein in the case that a second target road section comprises a plurality of spatially discontinuous road sections comprising the first target road section, wherein after determining a traffic condition with the highest accumulated reliability value as the real-time traffic condition for the first target road section within the traffic data release period, the method further comprises:

reading a plurality of road section weighting factors corresponding to the plurality of road sections;

calculating the product of the weighting factor for each of the plurality of road sections and the reliability of the real-time traffic condition for a corresponding road section within the traffic data release period;

accumulating the products of the road sections with a same type of traffic conditions to obtain a accumulated value for each type of traffic conditions; and

determining a traffic condition with the highest accumulated value as the real-time traffic condition for the second target road section within the traffic data release period.

9. The method of claim 1, wherein in the case that the traffic data release period comprises a plurality of time periods, each of which has a same duration as the first preset period, after comparing the membership degrees of all types of traffic conditions contained in the first fuzzy rule matrix table to determine a real-time traffic condition for the first target road section within the first preset period, the method further comprises:

reading a priority for each type of traffic conditions; and

determining a traffic condition with a high priority among the real-time traffic conditions for the first target road section within each of the time periods as the real-time traffic condition for the first target road section within the traffic data release period.

10. The method of claim 1, wherein determining a membership degree for each type of traffic conditions contained in the first fuzzy rule matrix table by calling a membership function comprises:

determining the membership degrees for the traffic parameters in the fuzzy rule matrix table by calling the membership function; and

determining the membership degree of each type of traffic conditions contained in the fuzzy rule matrix based on the membership degrees of the traffic parameters in the fuzzy rule matrix table.

20. A storage medium, which is used for storing application program configured for carrying out the method for processing traffic road information of claim 1.

11. An apparatus for processing traffic road information, comprising:

a first obtaining unit, configured for obtaining traffic parameters of a first target road section and/or the reliability of the traffic parameters within a first preset period acquired by traffic detection devices, wherein the traffic parameters at least comprise any one or more of the parameters;

a vehicle time occupancy rate, flow saturation of vehicle flow, and a vehicle speed;

a matching unit, configured for selecting a first fuzzy rule matrix table from a pre-stored set of fuzzy rule matrix tables based on the number of the traffic parameters of the first target road section and/or the reliability of the traffic parameters, wherein the fuzzy rule matrix tables comprise any one of the following types of matrix tables;

a one-dimensional fuzzy rule matrix table, a two-dimensional fuzzy rule matrix table, and a three-dimensional fuzzy rule matrix table;

a determining unit, configured for determining a membership degree for each type of traffic conditions contained in the first fuzzy rule matrix table by calling a membership function, wherein the traffic conditions at least comprise the following types;

Unblocked, Slow and Congested; and

a comparing unit, configured for comparing the membership degrees of all types of traffic conditions contained in the first fuzzy rule matrix table to determine a real-time traffic condition for the first target road section within the first preset period.

12. The apparatus of claim 11, wherein in the case that there are at least two traffic parameters of the first target road section, the reliability of the traffic parameters of the first target road section is a combination of the reliability of each of the parameters, the matching unit comprises:

an obtaining module, configured for obtaining a group of fuzzy rule matrix tables from the pre-stored set of fuzzy rule matrix tables based on the number of the traffic parameters of the first target road section, wherein the dimension of each fuzzy rule matrix table contained in the group of fuzzy rule matrix tables is the same as the number of the parameters; and

a matching module, configured for selecting a fuzzy rule matrix table that matches with the reliability of the traffic parameters of the first target road section from the group of fuzzy rule matrix tables to obtain the first fuzzy rule matrix table.

13. The apparatus according to claim 11, wherein the apparatus further comprises:

an acquiring unit, configured for acquiring traffic data of the first target road section by using a plurality of traffic detection devices within the first preset period, wherein the plurality of traffic devices at least comprise a combination of any of the following devices;

a magnetic frequency vehicle detector, a wave frequency vehicle detector, a video vehicle detector, a coil vehicle detector, a microwave vehicle detector, a geomagnetic vehicle detector and a SCATS vehicle detector;

a processing unit, configured for preprocessing the traffic data to obtain traffic parameters of the first target road section, wherein the preprocessing comprises at least one or more of the following processings;

filtering of the traffic data, time-space conversion of the traffic data, and data conversion of the traffic data.

14. The apparatus of claim 13, wherein the processing unit comprises:

a first processing module, configured for filtering the traffic data of the first target road section acquired by each of the traffic detection devices respectively according to preset filter conditions to obtain the filtered traffic data acquired by each of the traffic detection devices, wherein the filter conditions at least comprise one or more of the following conditions;

device parameters of the traffic detection devices, vehicle speed limits for different traffic conditions, vehicle flow limits for different types of roads, the vehicle time occupancy rate, correlations between different types of traffic parameters; and

a second processing module, configured for performing the time-space conversion and/or data conversion on the filtered traffic data acquired by each of the traffic detection devices to obtain the traffic parameters of the first target road section.

15. The apparatus of claim 14, wherein the traffic data at least comprise one or more types of the following parameters:

a vehicle time occupancy rate, flow saturation of vehicle flow, and a vehicle speed, the second processing module comprises;

a first processing sub-module, configured for calculating the reliability of each type of parameters detected by each of the traffic detection devices within the first preset period based on detection accuracy of each of the traffic detection devices and the data amount of each type of the parameters actually acquired within the first preset period;

a second processing sub-module, configured for calculating weighted average of each type of the parameters actually acquired by using the reliability of each type of parameters as weighting factors to obtain the traffic parameters of the first target road section within the first preset period; and

a third processing sub-module, configured for obtaining the reliability of the traffic parameters by averaging the reliability of a same type of parameters detected by each of the traffic detection devices.

16. The apparatus of claim 11, wherein in the case that a traffic data release period comprises a plurality of time periods, each of which has a same duration as the first preset period, the apparatus further comprises:

a second obtaining unit, configured for obtaining the reliability of real-time traffic conditions for the first target road section within each of the time periods of the traffic data release period;

a first accumulation unit, configured for accumulating the reliability of traffic conditions of a same type within each of the time periods to obtain a accumulated reliability value for each type of traffic conditions;

a first selecting unit, configured for determining a traffic condition with the highest accumulated reliability value as the real-time traffic condition for the first target road section within the traffic data release period.

17. The apparatus of claim 16, wherein the second obtaining unit comprises:

a first calculation module, configured for calculating, for each of the time periods, a proportion of time in which the traffic on the first target road section is in a passing state;

a second calculation module, configured for calculating the reliability of the real-time traffic conditions for the first target section within each of the time periods based on the proportion of time in which the traffic is in the passing state and the reliability of the acquired traffic parameters of the first target road section within each of the time periods.

18. The apparatus of claim 16, wherein in the case that a second target road section comprises a plurality of spatially discontinuous road sections comprising the first target road section, the apparatus further comprises:

a third obtaining unit, configured for reading a plurality of road section weighting factors corresponding to the plurality of road sections;

a calculation unit, configured for calculating the product of the weighting factor for each of the plurality of road sections and the reliability of the real-time traffic condition for a corresponding road section within the traffic data release period;

a second accumulation unit, configured for accumulating the products of the road sections with a same type of traffic conditions to obtain a accumulated value for each type of traffic conditions; and

a second selecting unit, configured for determining a traffic condition with the highest accumulated value as the real-time traffic condition for the second target road section within the traffic data release period.

19. A terminal, comprising:

a processor, a memory, communication interfaces and a bus;

the processor, the memory and the communication interfaces are connected and communicate with each other via the bus;

the memory is configured to store executable program codes; and

the processor is configured to execute programs corresponding to the executable program codes by reading the executable program codes stored in the memory for;

obtaining acquired traffic parameters of a first target road section and/or the reliability of the traffic parameters within a first preset period, wherein the traffic parameters at least comprise any one or more of the following parameters;

a vehicle time occupancy rate, flow saturation of vehicle flow, and a vehicle speed;

selecting a first fuzzy rule matrix table from a pre-stored set of fuzzy rule matrix tables based on the number of the traffic parameters of the first target road section and/or the reliability of the traffic parameters, wherein the fuzzy rule matrix tables comprise any one of the following types of matrix tables;

a one-dimensional fuzzy rule matrix table, a two-dimensional fuzzy rule matrix table, and a three-dimensional fuzzy rule matrix table;

determining a membership degree for each type of traffic conditions contained in the first fuzzy rule matrix table by calling a membership function, wherein the traffic conditions at least comprise the following types;

Unblocked, Slow and Congested; and

comparing the membership degrees of all types of traffic conditions contained in the first fuzzy rule matrix table to determine a real-time traffic condition for the first target road section within the first preset period.