CN111724600A - Real-time queuing length analysis method and system based on edge calculation - Google Patents
Real-time queuing length analysis method and system based on edge calculation Download PDFInfo
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- CN111724600A CN111724600A CN202010613000.4A CN202010613000A CN111724600A CN 111724600 A CN111724600 A CN 111724600A CN 202010613000 A CN202010613000 A CN 202010613000A CN 111724600 A CN111724600 A CN 111724600A
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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
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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
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/07—Controlling traffic signals
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/07—Controlling traffic signals
- G08G1/08—Controlling traffic signals according to detected number or speed of vehicles
Abstract
The invention relates to the technical field of road traffic management, and particularly discloses a real-time queuing length analysis method based on edge calculation, which comprises the following steps: acquiring traffic state information of a motor vehicle, wherein the traffic state information comprises speed, flow, occupancy and tail vehicle position; acquiring signal light color information of a road traffic signal lamp, wherein the signal light color information comprises a light color state, phase duration and a signal period; analyzing and processing the traffic state information and the signal light color information, and outputting a real-time queuing length analysis result; and respectively sending the real-time queuing length analysis result to a traffic management platform and a road traffic signal controller. The invention also discloses a real-time queuing length analysis system based on the edge calculation. The real-time queuing length analysis method based on edge calculation can improve the real-time performance of data processing, support the application of intersection signal control and have higher economic value and practical significance.
Description
Technical Field
The invention relates to the technical field of road traffic management, in particular to a real-time queuing length analysis method and system based on edge calculation.
Background
With the rapid development of new technologies such as internet of things, big data, 5G communication, edge calculation and the like, key technologies of intelligent traffic control are innovated, the intelligent and efficient control capability is improved, and the intelligent and efficient control method becomes development power of urban traffic control; in order to reduce the limitation of network bandwidth on high-speed data transmission and reduce the pressure of central cloud data processing, aiming at intersection traffic intelligent processing, more and more areas use powerful edge processing equipment to process data at intersections and end sides, and then report the cloud, so that the processing mode can effectively improve the real-time performance of data processing and reduce network overhead.
The currently used queuing length analysis method is mainly judged according to the position of the tail car of the lane, and the analysis method can generate errors in judging the queuing length when a slow-moving vehicle and a fault vehicle stop in the lane; what is more needed for signal control is the queue length of the signal state, and this data helps the signal system to optimize the release time of the phase in real time, and reduces the waste of time.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides a real-time queuing length analysis method and system based on edge calculation, which can improve the real-time performance of data processing, support the application of intersection signal control and have higher economic value and practical significance.
As a first aspect of the present invention, there is provided a real-time queuing length analysis method based on edge calculation, including:
acquiring traffic state information of a motor vehicle, wherein the traffic state information comprises speed, flow, occupancy and tail vehicle position;
acquiring signal light color information of a road traffic signal lamp, wherein the signal light color information comprises a light color state, phase duration and a signal period;
analyzing and processing the traffic state information and the signal light color information, and outputting a real-time queuing length analysis result;
and respectively sending the real-time queuing length analysis result to a traffic management platform and a road traffic signal controller.
Further, the analyzing and processing the traffic state information and the signal light color information and outputting a real-time queuing length analysis result includes:
screening out target traffic state information which meets a preset range from the traffic state information by adopting a threshold judgment method;
and carrying out traffic flow consistency check on the target traffic state information.
Further, screening out target traffic state information which meets a preset range from the traffic state information; and carrying out traffic flow consistency check on the target traffic state information, wherein the traffic flow consistency check comprises the following steps:
screening out a target speed meeting a first preset range;
screening out target flow meeting a second preset range;
screening out a target occupancy rate meeting a third preset range;
screening out target tail car positions meeting a fourth preset range;
and carrying out traffic flow consistency check on the target speed, the target flow and the target occupancy.
Further, the first preset range is as follows: v is more than or equal to 0 and less than or equal to fv.v1Wherein V is the target speed; v. of1Limiting the speed for the road; f. ofvThe value is 1.3-1.5 for the correction coefficient;
the second preset range is as follows:
wherein Q is the target flow; c is road traffic capacity; t is a data sampling period; f. ofcThe value is 1.3-1.5 for the correction coefficient;
the third preset range is as follows: o is more than or equal to 0 and less than or equal to 100 percent, wherein O is the target occupancy;
the fourth preset range is as follows: l1≤l≤l2Wherein l is the target tail car position, l1Is the minimum value of the detection range of the vehicle detector,/2The maximum value of the detection range of the vehicle detector.
Further, the performing traffic flow consistency check on the target speed, the target flow and the target occupancy includes:
respectively setting a target speed V, a target flow Q and a target occupancy rate O;
if the target speed V, the target flow Q and the target occupancy O meet preset conditions, the target speed V, the target flow Q and the target occupancy O are abnormal traffic state data;
if the target speed V, the target flow Q and the target occupancy O do not meet preset conditions, the target speed V, the target flow Q and the target occupancy O are normal traffic state data;
carrying out fusion analysis calculation on the normal traffic state data of the motor vehicles and the signal period of the road traffic signal lamp to generate analysis queue length;
and carrying out weighted average calculation on the analysis queuing length and the target tail car position to obtain the real-time queuing length.
Further, the preset condition includes any one or a combination of the following first preset condition, second preset condition, third preset condition and fourth preset condition, wherein:
the first preset condition is as follows: v is 0, and Q is not equal to 0;
the second preset condition is as follows: v ≠ 0, Q ═ 0;
the third preset condition is as follows: v ═ 0, Q ═ 0, 0< O < 95;
the fourth preset condition is as follows: v ≠ 0, Q ≠ 0, and O ═ 0.
Further, after the real-time queuing length analysis result is respectively sent to a traffic management platform and a road traffic signal controller, the method comprises the following steps:
and receiving and storing the real-time queuing length analysis result, and supplying the real-time queuing length analysis result to a vehicle networking platform or a traffic guidance information publishing platform for use.
As a second aspect of the present invention, there is provided a real-time queuing length analysis system based on edge calculation, comprising:
the vehicle detector is used for acquiring traffic state information of the motor vehicle and sending the traffic state information to the edge computing equipment, wherein the traffic state information comprises speed, flow, occupancy and tail vehicle position;
the road traffic signal control machine is used for acquiring signal light color information of a road traffic signal lamp and sending the signal light color information to the edge computing equipment, wherein the signal light color information comprises a light color state, phase duration and a signal period;
and the edge computing equipment is used for analyzing and processing the traffic state information and the signal light color information, outputting a real-time queuing length analysis result, and respectively sending the real-time queuing length analysis result to a traffic management platform and the road traffic signal controller.
And further, the traffic management platform is used for receiving and storing the real-time queuing length analysis result and supplying the real-time queuing length analysis result to an Internet of vehicles platform or a traffic guidance information release platform for use.
Further, the edge computing device, in particular for,
screening target traffic state information which meets a preset range in the traffic state information by adopting a threshold judgment method, wherein the target traffic state information comprises a target speed, a target flow, a target occupancy and a target tail car position;
respectively setting a target speed V, a target flow Q and a target occupancy rate O;
if the target speed V, the target flow Q and the target occupancy O meet preset conditions, the target speed V, the target flow Q and the target occupancy O are abnormal traffic state data;
if the target speed V, the target flow Q and the target occupancy O do not meet preset conditions, the target speed V, the target flow Q and the target occupancy O are normal traffic state data;
carrying out fusion analysis calculation on the normal traffic state data of the motor vehicles and the signal period of the road traffic signal lamp to generate analysis queue length;
and carrying out weighted average calculation on the analysis queuing length and the target tail car position to obtain the real-time queuing length.
The real-time queuing length analysis method and the real-time queuing length analysis system based on the edge calculation have the following advantages that: the real-time performance of data processing can be improved, the application of intersection signal control is supported, and the method has higher economic value and practical significance.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention.
FIG. 1 is a flow chart of a real-time queue length analysis method based on edge calculation according to the present invention.
FIG. 2 is a flowchart of an embodiment of a real-time queue length analyzing method based on edge calculation according to the present invention.
FIG. 3 is a block diagram of a real-time queue length analysis system based on edge calculation according to the present invention.
Detailed Description
To further illustrate the technical means and effects of the present invention adopted to achieve the predetermined objects, the following detailed description will be given to the embodiments, structures, features and effects of the method and system for real-time queue length analysis based on edge calculation according to the present invention with reference to the accompanying drawings and preferred embodiments. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without any inventive step, are within the scope of the present invention.
In this embodiment, a real-time queuing length analysis method based on edge calculation is provided, as shown in fig. 1, the real-time queuing length analysis method based on edge calculation includes:
step S110: acquiring traffic state information of a motor vehicle, wherein the traffic state information comprises speed, flow, occupancy and tail vehicle position;
step S120: acquiring signal light color information of a road traffic signal lamp, wherein the signal light color information comprises a light color state, phase duration and a signal period;
step S130: analyzing and processing the traffic state information and the signal light color information, and outputting a real-time queuing length analysis result;
step S140: and respectively sending the real-time queuing length analysis result to a traffic management platform and a road traffic signal controller.
Preferably, as shown in fig. 2, the analyzing the traffic status information and the signal light color information and outputting a real-time queue length analysis result includes:
screening out target traffic state information which meets a preset range from the traffic state information by adopting a threshold judgment method, and discarding abnormal data which does not meet the preset range;
and carrying out traffic flow consistency check on the target traffic state information.
Preferably, the screened target traffic state information meeting a preset range in the traffic state information; and carrying out traffic flow consistency check on the target traffic state information, wherein the traffic flow consistency check comprises the following steps:
screening out a target speed meeting a first preset range;
screening out target flow meeting a second preset range;
screening out a target occupancy rate meeting a third preset range;
screening out target tail car positions meeting a fourth preset range;
and carrying out traffic flow consistency check on the target speed, the target flow and the target occupancy.
Preferably, the first preset range is: v is more than or equal to 0 and less than or equal to fv.v1Wherein V is the target speed; v. of1Limiting the speed (km/h) for the road; f. ofvThe value is 1.3-1.5 for the correction coefficient; the speed of the motor vehicle is also completed in a relatively short time interval, and due to the existence of random errors, certain speed adjustment is required, and the reasonable range of the average speed is as follows: v is more than or equal to 0 and less than or equal to fv.v1；
The second preset range is as follows:
wherein Q is the target flow; c is road traffic capacity (vel/h); t is a data sampling period (min); f. ofcThe value is 1.3-1.5 for the correction coefficient; since the target flow rate Q of the motor vehicle is achieved in a relatively short time, the minimum value of Q is 0 and the maximum value is the road traffic capacity C and the correction factor fcThe product of (a);
the third preset range is as follows: o is more than or equal to 0 and less than or equal to 100 percent, wherein O is the target occupancy; the occupancy is generally divided into a time occupancy and a space occupancy, herein referred to as a time occupancy O, which is a ratio of a time that the vehicle occupies the detector to a total operating time of the traffic detector;
the fourth preset range is as follows: l1≤l≤l2Wherein l is the target tail car position, l1Is the minimum value of the detection range of the vehicle detector,/2The maximum value of the detection range of the vehicle detector.
Preferably, as shown in fig. 2, the performing a traffic flow consistency check on the target speed, the target flow rate and the target occupancy includes:
respectively setting a target speed V, a target flow Q and a target occupancy rate O;
if the target speed V, the target flow Q and the target occupancy O meet preset conditions, the target speed V, the target flow Q and the target occupancy O are abnormal traffic state data;
if the target speed V, the target flow Q and the target occupancy O do not meet preset conditions, the target speed V, the target flow Q and the target occupancy O are normal traffic state data;
carrying out fusion analysis calculation on the normal traffic state data of the motor vehicles and the signal period of the road traffic signal lamp to generate analysis queue length; the abnormal traffic state data is discarded;
and carrying out weighted average calculation on the analysis queuing length and the target tail car position to obtain the real-time queuing length.
It should be noted that, the normal traffic state data of the motor vehicle and the signal period of the road traffic signal lamp are subjected to fusion analysis calculation to generate an analysis queue length, which is specifically as follows:
carrying out fusion analysis calculation on the target flow Q and the signal period T to generate an analysis queue length p (k), wherein the calculation method comprises the following steps:
first, a discrete model of the fleet is calculated as:
QB(k)＝FQA(k′)+(1-F)QB(k-1)
wherein k represents the periodic time, the A road junction is the upstream road junction of the B road junction, and QB(k) Represents the flow at the entrance of the intersection B at the moment k, QA(k ') represents the flow at the outlet of the road junction A at the moment k ', k ' is k-t, t is the average time from the road junction A to the road junction B, and F is a smoothing coefficient (0 < F < 1);
and then according to the data, calculating and analyzing the queuing length p (k):
p(k)＝p(k-1)-tg(k)s+TQB(k-1)
wherein T is the signal period of the intersection, s is the saturation flow, Tg(k) Effective release time;
and carrying out weighted average calculation on the analysis queue length p (k) and the target tail car position L to obtain a real-time queue length L, wherein the calculation method comprises the following steps:
L＝(p(k)+l)/2。
preferably, the preset condition includes any one or a combination of the following first preset condition, second preset condition, third preset condition and fourth preset condition, wherein:
the first preset condition is as follows: v is 0, and Q is not equal to 0;
the second preset condition is as follows: v ≠ 0, Q ═ 0;
the third preset condition is as follows: v ═ 0, Q ═ 0, 0< O < 95;
the fourth preset condition is as follows: v ≠ 0, Q ≠ 0, and O ═ 0.
Preferably, after the real-time queuing length analysis result is respectively sent to a traffic management platform and a road traffic signal controller, the method includes:
and receiving and storing the real-time queuing length analysis result, and supplying the real-time queuing length analysis result to a vehicle networking platform or a traffic guidance information publishing platform for use.
As another embodiment of the present invention, as shown in fig. 3, there is provided a real-time queuing length analysis system based on edge calculation, including:
the vehicle detector is used for acquiring traffic state information of the motor vehicle and sending the traffic state information to the edge computing equipment, wherein the traffic state information comprises speed, flow, occupancy and tail vehicle position;
the road traffic signal control machine is used for acquiring signal light color information of a road traffic signal lamp and sending the signal light color information to the edge computing equipment, wherein the signal light color information comprises a light color state, phase duration and a signal period;
and the edge computing equipment is used for analyzing and processing the traffic state information and the signal light color information, outputting a real-time queuing length analysis result, and respectively sending the real-time queuing length analysis result to a traffic management platform and the road traffic signal controller.
Preferably, the traffic management platform is configured to receive and store the real-time queuing length analysis result, and supply the real-time queuing length analysis result to a vehicle networking platform or a traffic guidance information distribution platform.
Preferably, the edge computing device is, in particular for,
screening target traffic state information which meets a preset range in the traffic state information by adopting a threshold judgment method, wherein the target traffic state information comprises a target speed, a target flow, a target occupancy and a target tail car position;
respectively setting a target speed V, a target flow Q and a target occupancy rate O;
if the target speed V, the target flow Q and the target occupancy O meet preset conditions, the target speed V, the target flow Q and the target occupancy O are abnormal traffic state data;
if the target speed V, the target flow Q and the target occupancy O do not meet preset conditions, the target speed V, the target flow Q and the target occupancy O are normal traffic state data;
carrying out fusion analysis calculation on the normal traffic state data of the motor vehicles and the signal period of the road traffic signal lamp to generate analysis queue length;
and carrying out weighted average calculation on the analysis queuing length and the target tail car position to obtain the real-time queuing length.
The real-time queuing length analysis method and system based on edge calculation can improve the real-time performance of data processing, support the application of intersection signal control, and have higher economic value and practical significance.
Although the present invention has been described with reference to a preferred embodiment, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A real-time queuing length analysis method based on edge calculation is characterized by comprising the following steps:
acquiring traffic state information of a motor vehicle, wherein the traffic state information comprises speed, flow, occupancy and tail vehicle position;
acquiring signal light color information of a road traffic signal lamp, wherein the signal light color information comprises a light color state, phase duration and a signal period;
analyzing and processing the traffic state information and the signal light color information, and outputting a real-time queuing length analysis result;
and respectively sending the real-time queuing length analysis result to a traffic management platform and a road traffic signal controller.
2. The method for analyzing the real-time queuing length based on the edge calculation of claim 1, wherein the analyzing the traffic status information and the signal light color information and outputting the real-time queuing length analysis result comprises:
screening out target traffic state information which meets a preset range from the traffic state information by adopting a threshold judgment method;
and carrying out traffic flow consistency check on the target traffic state information.
3. The real-time queuing length analysis method based on edge calculation according to claim 2, characterized in that the target traffic state information satisfying a preset range among the traffic state information is screened out; and carrying out traffic flow consistency check on the target traffic state information, wherein the traffic flow consistency check comprises the following steps:
screening out a target speed meeting a first preset range;
screening out target flow meeting a second preset range;
screening out a target occupancy rate meeting a third preset range;
screening out target tail car positions meeting a fourth preset range;
and carrying out traffic flow consistency check on the target speed, the target flow and the target occupancy.
4. The method according to claim 3, wherein the first preset range is: v is more than or equal to 0 and less than or equal to fv.v1Wherein V is the target speed; v. of1Limiting the speed for the road; f. ofvThe value is 1.3-1.5 for the correction coefficient;
the second preset range is as follows:
wherein Q is the target flow; c is road traffic capacity; t is a data sampling period; f. ofcThe value is 1.3-1.5 for the correction coefficient;
the third preset range is as follows: o is more than or equal to 0 and less than or equal to 100 percent, wherein O is the target occupancy;
the fourth preset range is as follows: l1≤l≤l2Wherein l is the target tail car position, l1Is the minimum value of the detection range of the vehicle detector,/2The maximum value of the detection range of the vehicle detector.
5. The method for analyzing the real-time queuing length based on the edge calculation in claim 3 is characterized in that the step of carrying out traffic flow consistency check on the target speed, the target flow and the target occupancy comprises the following steps:
respectively setting a target speed V, a target flow Q and a target occupancy rate O;
if the target speed V, the target flow Q and the target occupancy O meet preset conditions, the target speed V, the target flow Q and the target occupancy O are abnormal traffic state data;
if the target speed V, the target flow Q and the target occupancy O do not meet preset conditions, the target speed V, the target flow Q and the target occupancy O are normal traffic state data;
carrying out fusion analysis calculation on the normal traffic state data of the motor vehicles and the signal period of the road traffic signal lamp to generate analysis queue length;
and carrying out weighted average calculation on the analysis queuing length and the target tail car position to obtain the real-time queuing length.
6. The real-time queuing length analyzing method based on edge calculation according to claim 5, wherein the preset condition comprises any one or combination of the following first preset condition, second preset condition, third preset condition and fourth preset condition, wherein:
the first preset condition is as follows: v is 0, and Q is not equal to 0;
the second preset condition is as follows: v ≠ 0, Q ═ 0;
the third preset condition is as follows: v ═ 0, Q ═ 0, 0< O < 95;
the fourth preset condition is as follows: v ≠ 0, Q ≠ 0, and O ═ 0.
7. The method for analyzing the real-time queuing length based on the edge calculation as claimed in claim 1, wherein after the real-time queuing length analysis result is respectively sent to a traffic management platform and a road traffic signal controller, the method comprises:
and receiving and storing the real-time queuing length analysis result, and supplying the real-time queuing length analysis result to a vehicle networking platform or a traffic guidance information publishing platform for use.
8. A real-time queuing length analysis system based on edge calculation, comprising:
the vehicle detector is used for acquiring traffic state information of the motor vehicle and sending the traffic state information to the edge computing equipment, wherein the traffic state information comprises speed, flow, occupancy and tail vehicle position;
the road traffic signal control machine is used for acquiring signal light color information of a road traffic signal lamp and sending the signal light color information to the edge computing equipment, wherein the signal light color information comprises a light color state, phase duration and a signal period;
and the edge computing equipment is used for analyzing and processing the traffic state information and the signal light color information, outputting a real-time queuing length analysis result, and respectively sending the real-time queuing length analysis result to a traffic management platform and the road traffic signal controller.
9. The real-time queuing length analysis system based on edge calculation as claimed in claim 8, wherein the traffic management platform is configured to receive and store the real-time queuing length analysis result, and provide the real-time queuing length analysis result for the car networking platform or the traffic guidance information distribution platform.
10. Real-time queuing length analysis system based on edge calculation according to claim 8, wherein the edge calculation device, in particular for,
screening target traffic state information which meets a preset range in the traffic state information by adopting a threshold judgment method, wherein the target traffic state information comprises a target speed, a target flow, a target occupancy and a target tail car position;
respectively setting a target speed V, a target flow Q and a target occupancy rate O;
if the target speed V, the target flow Q and the target occupancy O meet preset conditions, the target speed V, the target flow Q and the target occupancy O are abnormal traffic state data;
if the target speed V, the target flow Q and the target occupancy O do not meet preset conditions, the target speed V, the target flow Q and the target occupancy O are normal traffic state data;
carrying out fusion analysis calculation on the normal traffic state data of the motor vehicles and the signal period of the road traffic signal lamp to generate analysis queue length;
and carrying out weighted average calculation on the analysis queuing length and the target tail car position to obtain the real-time queuing length.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113643537A (en) * | 2021-08-11 | 2021-11-12 | 公安部交通管理科学研究所 | Method and system for detecting characteristic parameters of traffic state of import lane |
| CN114220211A (en) * | 2021-12-27 | 2022-03-22 | 咪咕音乐有限公司 | Video color ring back tone ranking prompt method, device, equipment and computer storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104392607A (en) * | 2014-11-18 | 2015-03-04 | 青岛海信网络科技股份有限公司 | Method and device for identifying traffic conditions |
| CN106548632A (en) * | 2016-10-17 | 2017-03-29 | 同济大学 | A kind of adjacent upstream and downstream signalized intersections queue length method of estimation |
| WO2018115511A1 (en) * | 2016-12-22 | 2018-06-28 | Luxembourg Institute Of Science And Technology (List) | Method and system for enhanced traffic light signaling and for computing a target speed of an automotive vehicle |
| CN111341095A (en) * | 2018-12-19 | 2020-06-26 | 中国航天系统工程有限公司 | Traffic signal control system and method based on edge side online calculation |
-
2020
- 2020-06-30 CN CN202010613000.4A patent/CN111724600A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104392607A (en) * | 2014-11-18 | 2015-03-04 | 青岛海信网络科技股份有限公司 | Method and device for identifying traffic conditions |
| CN106548632A (en) * | 2016-10-17 | 2017-03-29 | 同济大学 | A kind of adjacent upstream and downstream signalized intersections queue length method of estimation |
| WO2018115511A1 (en) * | 2016-12-22 | 2018-06-28 | Luxembourg Institute Of Science And Technology (List) | Method and system for enhanced traffic light signaling and for computing a target speed of an automotive vehicle |
| CN111341095A (en) * | 2018-12-19 | 2020-06-26 | 中国航天系统工程有限公司 | Traffic signal control system and method based on edge side online calculation |
Non-Patent Citations (1)
| Title |
|---|
| 徐建闽，等: "《交通管理与控制》", 30 November 2007 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113643537A (en) * | 2021-08-11 | 2021-11-12 | 公安部交通管理科学研究所 | Method and system for detecting characteristic parameters of traffic state of import lane |
| CN113643537B (en) * | 2021-08-11 | 2022-08-05 | 公安部交通管理科学研究所 | Method and system for detecting characteristic parameters of traffic state of import lane |
| CN114220211A (en) * | 2021-12-27 | 2022-03-22 | 咪咕音乐有限公司 | Video color ring back tone ranking prompt method, device, equipment and computer storage medium |
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