CN114023065A - Algorithm for intelligently diagnosing intersection service level by utilizing video analysis data - Google Patents
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- 230000001133 acceleration Effects 0.000 claims description 12
- 238000001514 detection method Methods 0.000 claims description 8
- 238000012935 Averaging Methods 0.000 claims description 6
- 230000002159 abnormal effect Effects 0.000 claims description 6
- 238000011835 investigation Methods 0.000 claims description 5
- 238000009825 accumulation Methods 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 abstract description 9
- 238000011156 evaluation Methods 0.000 abstract description 7
- 238000000034 method Methods 0.000 abstract description 2
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
- G08G1/0145—Measuring and analyzing of parameters relative to traffic conditions for specific applications for active traffic flow control
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/052—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/065—Traffic control systems for road vehicles by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/07—Controlling traffic signals
- G08G1/085—Controlling traffic signals using a free-running cyclic timer
Abstract
The invention discloses an algorithm for intelligently diagnosing service level of an intersection by utilizing video analytic data, which comprises the following steps: a: the ABCD is a coordination intersection, the travel time of the vehicles between the coordination intersections is calculated by using the time difference of the vehicles passing through TG1 and TG2, and for each vehicle passing through TG1, the passing time of the vehicle is recorded as T1; b: whether the TG2 passes within three hours is inquired, if the TG2 does not pass, the data is discarded, if the passing time of the TG2 is T2, T2-T1 is the travel time of the bicycle, and the timestamp is marked as T1; c: for this coordination direction, the travel time of the vehicle within a certain time period is extracted. The method has the advantages of enabling the calculation of the traffic jam evaluation index to be more accurate, providing powerful data support for the traffic police to reasonably carry out signal lamp timing and effectively carry out traffic control, and solving the problems that the calculation of the traffic jam evaluation index is not accurate and the signal lamp timing cannot be reasonably carried out for the traffic police.
Description
Technical Field
The invention relates to the technical field of crossing diagnosis application of urban main roads, secondary main roads and the like, in particular to an algorithm for intelligently diagnosing crossing service levels by utilizing video analysis data.
Background
The road traffic capacity refers to the capacity of a road facility to dredge a traffic flow, that is, the capacity of the road facility to pass through a traffic flow point in a certain time period (usually 15min or lh) and under normal road, traffic, control and operation quality requirements, and the traffic capacity is generally expressed by veh/h (vehicle/hour) and pcu/h (equivalent standard passenger car/hour), and the basic unit is: pcu/h/ln (equivalent standard passenger car/hour/lane), the traffic capacity is essentially a measure of the road load performance, which reflects both the maximum capacity of the road to clear traffic and the limit value of the road that can be assumed by the vehicle under the condition of the specified characteristics.
With the continuous development of social economy, the automobile holding capacity is continuously increased, the problem of traffic congestion is increasingly serious, the time of travel of residents is increased due to the traffic congestion, the living quality of the residents is reduced, and the urban environment is destroyed, so that the work of evaluating, managing and the like of the traffic congestion is not slow, accurate intersection diagnosis is the premise that the traffic congestion is effectively managed, and a reasonable, effective and ground-copied intersection service level calculation method is provided for the urban traffic congestion situation.
Disclosure of Invention
The invention aims to provide an algorithm for intelligently diagnosing the service level of an intersection by utilizing video analytic data, which has the advantages of more accurate calculation of a traffic jam evaluation index, and powerful data support for reasonably carrying out signal lamp timing and effectively carrying out traffic control on a traffic police, and solves the problems that the calculation of the traffic jam evaluation index is not accurate and the signal lamp timing cannot be reasonably carried out on the traffic police.
In order to achieve the purpose, the invention provides the following technical scheme: an algorithm for intelligently diagnosing service levels of intersections by utilizing video analytic data comprises the following steps:
a: the ABCD is a coordination intersection, the travel time of the vehicles between the coordination intersections is calculated by using the time difference of the vehicles passing through TG1 and TG2, and for each vehicle passing through TG1, the passing time of the vehicle is recorded as T1;
b: whether the TG2 passes within three hours is inquired, if the TG2 does not pass, the data is discarded, if the passing time of the TG2 is T2, T2-T1 is the travel time of the bicycle, and the timestamp is marked as T1;
c: for the coordination direction, extracting the travel time of the vehicle in a certain time period, removing abnormal values, and averaging;
d: calculating the free flow running time of the road section, extracting the running time in a time period, such as the running time in one month, sorting the running time from small to large, taking the first 5% of the running time, and then taking the average value to obtain the free flow running time of the road section;
e: road section delay time: the travel time of the current road section subtracts the free passing time of the road section, and the intersection delay time is as follows: extracting delay time of each road section of the intersection, D1 and D2., and corresponding traffic flow Q1 and Q2, and carrying out weighted average on the delay time of each road section according to the traffic flow;
f: service level: and extracting the delay time of the current intersection, and calculating the service level according to the delay time.
Preferably, the free flow velocity: average travel speed of the motor vehicle passing through the road section under the conditions of low traffic volume and low density, flow: and counting the passing vehicles Q in different time periods of 5 minutes, 15 minutes or 2 hours.
Preferably, the section delay time is: the difference between the actual transit time of a vehicle through a stretch compared to the time required to traverse the stretch at the free stream speed, TG: video speed measuring equipment.
Preferably, the dynamic information, the characteristic information, the road condition information and the traffic state information of the running vehicle within the self detection range of the range can be acquired by a plurality of road side sensors installed at the road side.
Preferably, the traffic capacity is: the traffic capacity of the road sections of the team is corrected according to the investigation result of the road section flow and the distance between intersections, the road section grade and the number of lanes, the traffic load of the road sections is analyzed on the basis, and the designed traffic capacity of the motor vehicles on the road sections is calculated as follows:
Nm＝Npackmδ。
preferably, said N ismDesigned traffic capacity pcu/h, N for one-way lane of motor vehicle on road sectionpSection of possible traffic capacity pcu/h, a for a motor vehicle lanecThe score coefficient of the motor vehicle traffic capacity is 0.75 of the expressway classification coefficient, 0.80 of the main road classification coefficient, 0.85 of the secondary road classification coefficient, 0.90 of the road classification coefficient, kmThe first lane reduction coefficient is 1.0, the second lane reduction coefficient is 0.85, the third lane reduction coefficient is 0.75, the fourth lane reduction coefficient is 0.65, and the two lanes k can be taken as the one-way two lanes k after accumulationm1.85, one-way three lane km2.6, one-way four-lane kmThe factor δ is 3.25, and is a reduction factor of the traffic capacity of the intersection, and is a road, an overhead road and a ground express way which are not influenced by the intersection, wherein δ is 1, and the factor is related to the distance between the two intersections, the driving speed and the split ratio, and the average acceleration and deceleration of the vehicle during starting and braking:
preferably, l is the distance m between two intersections, a is the average acceleration of the vehicle when starting, and the average acceleration is taken as 0.8m/s of the car2And b is the average acceleration of the vehicle during braking, here taken to be 1.66m/s for the car2And delta is the average parking time at the vehicle intersection, and is half of the red light time.
Compared with the prior art, the invention has the following beneficial effects:
1. the method has the advantages of enabling the calculation of the traffic jam evaluation index to be more accurate, providing powerful data support for the traffic police to reasonably carry out signal lamp timing and effectively carry out traffic control, and solving the problems that the calculation of the traffic jam evaluation index is not accurate and the signal lamp timing cannot be reasonably carried out for the traffic police.
2. The invention can effectively collect the dynamic information, the characteristic information, the road condition information and the traffic state information of the running vehicles within the detection range of the self-detection range, and is used for providing accurate data so as to facilitate the accuracy of subsequent numerical value calculation, calculating the traffic capacity of each road section so as to reflect the vehicle running speed of each road section and provide various calculated data values so as to reasonably carry out signal lamp timing.
Drawings
FIG. 1 is a schematic view of a coordinated intersection according to the present invention.
Detailed Description
Referring to fig. 1, an algorithm for intelligently diagnosing a grade of intersection service using video parsing data includes the following steps:
a: the ABCD is a coordination intersection, the travel time of the vehicles between the coordination intersections is calculated by using the time difference of the vehicles passing through TG1 and TG2, and for each vehicle passing through TG1, the passing time of the vehicle is recorded as T1;
b: whether the TG2 passes within three hours is inquired, if the TG2 does not pass, the data is discarded, if the passing time of the TG2 is T2, T2-T1 is the travel time of the bicycle, and the timestamp is marked as T1;
c: for the coordination direction, extracting the travel time of the vehicle in a certain time period, removing abnormal values, and averaging;
d: calculating the free flow running time of the road section, extracting the running time in a time period, such as the running time in one month, sorting the running time from small to large, taking the first 5% of the running time, and then taking the average value to obtain the free flow running time of the road section;
e: road section delay time: the travel time of the current road section subtracts the free passing time of the road section, and the intersection delay time is as follows: extracting delay time of each road section of the intersection, D1 and D2., and corresponding traffic flow Q1 and Q2, and carrying out weighted average on the delay time of each road section according to the traffic flow;
f: service level: and extracting the delay time of the current intersection, and calculating the service level according to the delay time.
Free flow velocity: average travel speed of the motor vehicle passing through the road section under the conditions of low traffic volume and low density, flow: and counting the passing vehicles Q in different time periods of 5 minutes, 15 minutes or 2 hours.
Road section delay time: the difference between the actual transit time of a vehicle through a stretch compared to the time required to traverse the stretch at the free stream speed, TG: video speed measuring equipment.
Dynamic information, characteristic information, road condition information and traffic state information of running vehicles in the self detection range of the range can be acquired by utilizing a plurality of road side sensors installed on the road side.
Traffic capacity: the traffic capacity of the road sections of the team is corrected according to the investigation result of the road section flow and the distance between intersections, the road section grade and the number of lanes, the traffic load of the road sections is analyzed on the basis, and the designed traffic capacity of the motor vehicles on the road sections is calculated as follows:
Nm＝Npackmδ。
Nmdesigned traffic capacity pcu/h, N for one-way lane of motor vehicle on road sectionpSection of possible traffic capacity pcu/h, a for a motor vehicle lanecThe score coefficient of the motor vehicle traffic capacity is 0.75 of the expressway classification coefficient, 0.80 of the main road classification coefficient, 0.85 of the secondary road classification coefficient, 0.90 of the road classification coefficient, kmThe first lane reduction coefficient is 1.0, the second lane reduction coefficient is 0.85, the third lane reduction coefficient is 0.75, the fourth lane reduction coefficient is 0.65, and the two lanes k can be taken as the one-way two lanes k after accumulationm1.85, one-way three lane km2.6, one-way four-lane kmThe factor δ is 3.25, and is a reduction factor of the traffic capacity of the intersection, and is a road, an overhead road and a ground express way which are not influenced by the intersection, wherein δ is 1, and the factor is related to the distance between the two intersections, the driving speed and the split ratio, and the average acceleration and deceleration of the vehicle during starting and braking:
l is the distance m between two intersections, a is the average acceleration of the vehicle when starting, and is taken as 0.8m/s of the car2And b is the average acceleration of the vehicle during braking, here taken to be 1.66m/s for the car2And delta is the average parking time at the vehicle intersection, and is half of the red light time.
The first embodiment is as follows:
an algorithm for intelligently diagnosing service levels of intersections by utilizing video analytic data comprises the following steps:
a: the ABCD is a coordination intersection, the travel time of the vehicles between the coordination intersections is calculated by using the time difference of the vehicles passing through TG1 and TG2, and for each vehicle passing through TG1, the passing time of the vehicle is recorded as T1;
b: whether the TG2 passes within three hours is inquired, if the TG2 does not pass, the data is discarded, if the passing time of the TG2 is T2, T2-T1 is the travel time of the bicycle, and the timestamp is marked as T1;
c: for the coordination direction, extracting the travel time of the vehicle in a certain time period, removing abnormal values, and averaging;
d: calculating the free flow running time of the road section, extracting the running time in a time period, such as the running time in one month, sorting the running time from small to large, taking the first 5% of the running time, and then taking the average value to obtain the free flow running time of the road section;
e: road section delay time: the travel time of the current road section subtracts the free passing time of the road section, and the intersection delay time is as follows: extracting delay time of each road section of the intersection, D1 and D2., and corresponding traffic flow Q1 and Q2, and carrying out weighted average on the delay time of each road section according to the traffic flow;
f: service level: and extracting the delay time of the current intersection, and calculating the service level according to the delay time.
Free flow velocity: average travel speed of the motor vehicle passing through the road section under the conditions of low traffic volume and low density, flow: and counting the passing vehicles Q in different time periods of 5 minutes, 15 minutes or 2 hours.
Road section delay time: the difference between the actual transit time of a vehicle through a stretch compared to the time required to traverse the stretch at the free stream speed, TG: video speed measuring equipment.
Dynamic information, characteristic information, road condition information and traffic state information of running vehicles in the self detection range of the range can be acquired by utilizing a plurality of road side sensors installed on the road side.
Traffic capacity: the traffic capacity of the road sections of the team is corrected according to the investigation result of the road section flow and the distance between intersections, the road section grade and the number of lanes, the traffic load of the road sections is analyzed on the basis, and the designed traffic capacity of the motor vehicles on the road sections is calculated as follows:
Nm＝Npackmδ。
Nmdesigned traffic capacity pcu/h, N for one-way lane of motor vehicle on road sectionpSection of possible traffic capacity pcu/h, a for a motor vehicle lanecThe score coefficient of the motor vehicle traffic capacity is 0.75 of the expressway classification coefficient, 0.80 of the main road classification coefficient, 0.85 of the secondary road classification coefficient, 0.90 of the road classification coefficient, kmThe first lane reduction coefficient is 1.0, the second lane reduction coefficient is 0.85, the third lane reduction coefficient is 0.75, the fourth lane reduction coefficient is 0.65, and the two lanes k can be taken as the one-way two lanes k after accumulationm1.85, one-way three lane km2.6, one-way four-lane kmThe factor δ is 3.25, and is a reduction factor of the traffic capacity of the intersection, and is a road, an overhead road and a ground express way which are not influenced by the intersection, wherein δ is 1, and the factor is related to the distance between the two intersections, the driving speed and the split ratio, and the average acceleration and deceleration of the vehicle during starting and braking:
example two:
an algorithm for intelligently diagnosing service levels of intersections by utilizing video analytic data comprises the following steps:
a: the ABCD is a coordination intersection, the travel time of the vehicles between the coordination intersections is calculated by using the time difference of the vehicles passing through TG1 and TG2, and for each vehicle passing through TG1, the passing time of the vehicle is recorded as T1;
b: whether the TG2 passes within three hours is inquired, if the TG2 does not pass, the data is discarded, if the passing time of the TG2 is T2, T2-T1 is the travel time of the bicycle, and the timestamp is marked as T1;
c: for the coordination direction, extracting the travel time of the vehicle in a certain time period, removing abnormal values, and averaging;
d: calculating the free flow running time of the road section, extracting the running time in a time period, such as the running time in one month, sorting the running time from small to large, taking the first 5% of the running time, and then taking the average value to obtain the free flow running time of the road section;
e: road section delay time: the travel time of the current road section subtracts the free passing time of the road section, and the intersection delay time is as follows: extracting delay time of each road section of the intersection, D1 and D2., and corresponding traffic flow Q1 and Q2, and carrying out weighted average on the delay time of each road section according to the traffic flow;
f: service level: and extracting the delay time of the current intersection, and calculating the service level according to the delay time.
Free flow velocity: average travel speed of the motor vehicle passing through the road section under the conditions of low traffic volume and low density, flow: and counting the passing vehicles Q in different time periods of 5 minutes, 15 minutes or 2 hours.
Road section delay time: the difference between the actual transit time of a vehicle through a stretch compared to the time required to traverse the stretch at the free stream speed, TG: video speed measuring equipment.
Dynamic information, characteristic information, road condition information and traffic state information of running vehicles in the self detection range of the range can be acquired by utilizing a plurality of road side sensors installed on the road side.
Traffic capacity: the traffic capacity of the road sections of the team is corrected according to the investigation result of the road section flow and the distance between intersections, the road section grade and the number of lanes, the traffic load of the road sections is analyzed on the basis, and the designed traffic capacity of the motor vehicles on the road sections is calculated as follows:
Nm＝Npackmδ。
example three:
an algorithm for intelligently diagnosing service levels of intersections by utilizing video analytic data comprises the following steps:
a: the ABCD is a coordination intersection, the travel time of the vehicles between the coordination intersections is calculated by using the time difference of the vehicles passing through TG1 and TG2, and for each vehicle passing through TG1, the passing time of the vehicle is recorded as T1;
b: whether the TG2 passes within three hours is inquired, if the TG2 does not pass, the data is discarded, if the passing time of the TG2 is T2, T2-T1 is the travel time of the bicycle, and the timestamp is marked as T1;
c: for the coordination direction, extracting the travel time of the vehicle in a certain time period, removing abnormal values, and averaging;
d: calculating the free flow running time of the road section, extracting the running time in a time period, such as the running time in one month, sorting the running time from small to large, taking the first 5% of the running time, and then taking the average value to obtain the free flow running time of the road section;
e: road section delay time: the travel time of the current road section subtracts the free passing time of the road section, and the intersection delay time is as follows: extracting delay time of each road section of the intersection, D1 and D2., and corresponding traffic flow Q1 and Q2, and carrying out weighted average on the delay time of each road section according to the traffic flow;
f: service level: and extracting the delay time of the current intersection, and calculating the service level according to the delay time.
Free flow velocity: average travel speed of the motor vehicle passing through the road section under the conditions of low traffic volume and low density, flow: and counting the passing vehicles Q in different time periods of 5 minutes, 15 minutes or 2 hours.
Road section delay time: the difference between the actual transit time of a vehicle through a stretch compared to the time required to traverse the stretch at the free stream speed, TG: video speed measuring equipment.
Dynamic information, characteristic information, road condition information and traffic state information of running vehicles in the self detection range of the range can be acquired by utilizing a plurality of road side sensors installed on the road side.
Service class table:
in summary, the following steps: the algorithm for intelligently diagnosing the service level of the intersection by utilizing the video analytic data solves the problems that the calculation of the traffic jam evaluation index is not accurate and the signal lamp timing cannot be reasonably carried out for a traffic police.
Claims (7)
1. An algorithm for intelligently diagnosing service levels of intersections by utilizing video analytic data comprises the following steps:
a: the ABCD is a coordination intersection, the travel time of the vehicles between the coordination intersections is calculated by using the time difference of the vehicles passing through TG1 and TG2, and for each vehicle passing through TG1, the passing time of the vehicle is recorded as T1;
b: whether the TG2 passes within three hours is inquired, if the TG2 does not pass, the data is discarded, if the passing time of the TG2 is T2, T2-T1 is the travel time of the bicycle, and the timestamp is marked as T1;
c: for the coordination direction, extracting the travel time of the vehicle in a certain time period, removing abnormal values, and averaging;
d: calculating the free flow running time of the road section, extracting the running time in a time period, such as the running time in one month, sorting the running time from small to large, taking the first 5% of the running time, and then taking the average value to obtain the free flow running time of the road section;
e: road section delay time: the travel time of the current road section subtracts the free passing time of the road section, and the intersection delay time is as follows: extracting delay time of each road section of the intersection, D1 and D2., and corresponding traffic flow Q1 and Q2, and carrying out weighted average on the delay time of each road section according to the traffic flow;
f: service level: and extracting the delay time of the current intersection, and calculating the service level according to the delay time.
2. The algorithm for intelligently diagnosing service levels of intersections using video analytics data as claimed in claim 1, wherein: the free flow velocity: average travel speed of the motor vehicle passing through the road section under the conditions of low traffic volume and low density, flow: and counting the passing vehicles Q in different time periods of 5 minutes, 15 minutes or 2 hours.
3. The algorithm for intelligently diagnosing service levels of intersections using video analytics data as claimed in claim 1, wherein: the section delay time: the difference between the actual transit time of a vehicle through a stretch compared to the time required to traverse the stretch at the free stream speed, TG: video speed measuring equipment.
4. The algorithm for intelligently diagnosing service levels of intersections using video analytics data as claimed in claim 1, wherein: the dynamic information, the characteristic information, the road condition information and the traffic state information of the running vehicles in the self detection range of the range can be acquired by utilizing a plurality of road side sensors installed on the road side.
5. The algorithm for intelligently diagnosing service levels of intersections using video analytics data as claimed in claim 1, wherein: the traffic capacity is as follows: the traffic capacity of the road sections of the team is corrected according to the investigation result of the road section flow and the distance between intersections, the road section grade and the number of lanes, the traffic load of the road sections is analyzed on the basis, and the designed traffic capacity of the motor vehicles on the road sections is calculated as follows:
Nm＝Npackmδ。
6. a utilization vision according to claim 5The algorithm for intelligently diagnosing the service level of the intersection by frequency analysis data is characterized in that: said N ismDesigned traffic capacity pcu/h, N for one-way lane of motor vehicle on road sectionpSection of possible traffic capacity pcu/h, a for a motor vehicle lanecThe score coefficient of the motor vehicle traffic capacity is 0.75 of the expressway classification coefficient, 0.80 of the main road classification coefficient, 0.85 of the secondary road classification coefficient, 0.90 of the road classification coefficient, kmThe first lane reduction coefficient is 1.0, the second lane reduction coefficient is 0.85, the third lane reduction coefficient is 0.75, the fourth lane reduction coefficient is 0.65, and the two lanes k can be taken as the one-way two lanes k after accumulationm1.85, one-way three lane km2.6, one-way four-lane kmThe factor δ is 3.25, and is a reduction factor of the traffic capacity of the intersection, and is a road, an overhead road and a ground express way which are not influenced by the intersection, wherein δ is 1, and the factor is related to the distance between the two intersections, the driving speed and the split ratio, and the average acceleration and deceleration of the vehicle during starting and braking:
7. the algorithm for intelligently diagnosing service levels of intersections using video analytics data as claimed in claim 6, wherein: l is the distance m between two intersections, a is the average acceleration of the vehicle when starting, and the average acceleration is 0.8m/s2And b is the average acceleration of the vehicle during braking, here taken to be 1.66m/s for the car2And delta is the average parking time at the vehicle intersection, and is half of the red light time.
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Cited By (2)
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| CN115019525A (en) * | 2022-06-20 | 2022-09-06 | 杭州海康威视数字技术股份有限公司 | Travel time data screening method and traffic signal control method |
| CN117037498A (en) * | 2023-10-08 | 2023-11-10 | 武汉中科通达高新技术股份有限公司 | Real-time road condition analysis method and system |
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