CN112150813A - Method for controlling entrance ramp of long-distance downstream bottleneck section of expressway - Google Patents
Method for controlling entrance ramp of long-distance downstream bottleneck section of expressway Download PDFInfo
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- G08G1/00—Traffic control systems for road vehicles
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- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
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- G08G1/00—Traffic control systems for road vehicles
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- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
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- G08G1/0145—Measuring and analyzing of parameters relative to traffic conditions for specific applications for active traffic flow control
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Abstract
The invention discloses a method for controlling an entrance ramp of a long-distance downstream bottleneck section of a highway, and belongs to the technical field of intelligent traffic. The method comprises the following steps: and a plurality of traffic flow detectors are arranged on the highway at equal distances, the traffic flow state is judged according to the read traffic flow data of each section of the highway, and the traffic flow data is output in real time. Aiming at the traffic flow characteristics under the scene, a fuzzy self-adaptive proportional-integral-derivative (PID) control strategy is adopted, and the control strategy consists of proportional-integral-derivative (PID) control and fuzzy control. The method solves the problem that the control effect reduction caused by time delay is difficult to eliminate by the existing entrance ramp control strategy, and has important significance for improving the traffic capacity of long-distance downstream bottlenecks and improving the overall operation efficiency of the highway.
Description
Technical Field
The invention relates to the technical field of intelligent traffic, in particular to a method for controlling an entrance ramp of a long-distance downstream bottleneck section of an expressway.
Background
The problems of traffic jam, traffic accidents and the like occur under the conditions that the quantity of motor vehicles is continuously increased and the resources of the expressway are limited, and in order to improve the traffic efficiency of the expressway and reduce the traffic accidents, the research on the active traffic flow control strategy of the expressway is increasingly developed, and the entrance ramp control is widely applied. When the traffic flow on the expressway is congested, the signal timing is calculated through the ramp adjusting rate, the ramp is controlled to enter the traffic flow of the main line, the traffic congestion can be prevented from being aggravated, the congestion can be eliminated quickly, and the delay is reduced.
In recent years, many researches are carried out on bottleneck sections of expressways, mainly on the traffic flow characteristics at the junction of an entrance ramp and a main line, but the researches on long-distance downstream bottlenecks of the expressways are less. The long-distance downstream bottleneck of the expressway refers to that in many practical scenes, due to the fact that bottlenecks such as an ascending slope, a curve, a tunnel, a bridge and a lane are reduced at the downstream of an entrance ramp with a certain distance, the traffic flow efficiency of a road section is limited due to the fact that the downstream traffic capacity is small. In this case, there is a non-negligible time delay between the action of the control area at the junction and its effect on the traffic flow dynamics at the long distance downstream bottleneck position, and the difficulty of control of the controlled system with delay characteristics increases with increasing degree of delay. The situation has high requirements on the capability of a control strategy to cope with time delay, and is worthy of focusing attention in the active traffic flow control of the expressway. The traffic flow control strategy currently studied is a control area as a main consideration, and it is difficult to eliminate the reduction of the control effect due to the time delay.
Disclosure of Invention
[ problem ] to
The existing control method for the long-distance downstream bottleneck traffic flow of the expressway reduces the control effect by considering the influence caused by time delay.
[ solution ]
The invention provides a method for controlling an entrance ramp of a long-distance downstream bottleneck section of an expressway, which comprises the following steps of:
the method comprises the following steps: setting traffic flow detectors, setting the traffic flow detectors on a highway with a long-distance downstream bottleneck at equal distance, recording the pile number of each detector according to the direction from downstream to upstream, and acquiring traffic flow data in a highway section;
step two: selecting feedback control based on traffic flow data acquired by a traffic flow detector; building a fuzzy self-adaptive PID control system according to the hysteresis of the long-distance downstream bottleneck, acquiring traffic flow data in a highway section acquired by a traffic flow detector, and inputting the traffic flow data to the fuzzy self-adaptive PID control system;
step three: expected flow density rho of fuzzy self-adaptive PID control system with input signal of k timed(k) I.e. the input signal rin(k)＝ρd(k) (ii) a The output signal of the system is the actual flow density ρ (k +1), i.e. the output signal yout(k +1) ═ ρ (k +1), the control variable of the system is the ramp regulation rate r (k), the traffic flow of the ramp into the main line is controlled by the ramp regulation rate, r (k) is determined by the following formula:
wherein k isp、ki、kdAre parameters determined by fuzzy control, e (k) rin(k)-yout(k +1) is the error signal, T is the signal period.
Step four: real-time traffic flow data of each section of a road section are monitored by a traffic flow detector, when inflection points appear on occupancy inclined accumulated curves corresponding to the traffic flow detectors of two adjacent pile numbers, a congestion queuing phenomenon appears on the highway section, and when the fuzzy self-adaptive PID control system detects the inflection points appear on the occupancy inclined accumulated curves, a parameter k in an entrance turn controller is adjustedp、ki、kdAnd therefore, the ramp regulation rate is output, and the flow of the ramp vehicle entering the main line is controlled through signal lamp regulation of the ramp.
In one embodiment of the invention, the two adjacent traffic flow detectors arranged at equal intervals are not more than 300 meters apart.
In one embodiment of the present invention, the traffic flow data includes: traffic flow, density, speed, queue length and occupancy data for each section.
In an embodiment of the present invention, the third step further includes: obtaining historical traffic jam data of a long-distance downstream bottleneck of the highway after an automatic current-passing detector is installed from a traffic police department, wherein the historical traffic jam data is used for determining a fuzzy self-adaptive PID control logic fuzzy rule; the historical traffic congestion data includes traffic volume, density, speed, and occupancy data for congested road segments.
In one embodiment of the present invention, the method further comprises: adaptive PID control system at parameter k by obfuscationp，ki，kdAnd e (k), ec, where e (k) is the difference between the expected and actual flow density, ec is the error rate, PID control continuously detects e (k) and ec during operation, using the actual flow density ρ (k +1) as the output of the PID control system, and r (k) as the control variable.
In one embodiment of the invention, the first and second signals are generated by applying the first and second signals at e, ec and kp、ki、kdA fuzzy rule is established, and the fuzzy reasoning in the fuzzy adaptive controller is used for adjusting kp、ki、kdA value of (d); taking k into account during the adjustment processp、ki、kdFuzzy logic rules are used by calculating e and ec based on online real-time fuzzy adaptive PID control at different times.
In one embodiment of the invention, k is obtained based on the ranges of e and ecp、ki、kdThe e and ec membership functions are described as large Negative (NB), medium Negative (NM), small Negative (NS), zero (Z), small Positive (PS), medium Positive (PM), large Positive (PB), respectively.
In one embodiment of the present invention, the inflection point determining method is as follows: and within three minutes before and after the inflection point, the linear fitting straight line of the two occupancy inclined accumulation curves is respectively drawn by taking the inflection point as the intersection point, so that the sum of the total variances of all occupancy deviation values deviating from the two straight lines on the inclined accumulation curve is minimum.
In one embodiment of the invention, the line of the linear fit is determined by a least squares method.
The method is applied to the traffic flow control device.
[ advantageous effects ]
In many practical expressway scenes, phenomena such as upstream, curve, tunnel, bridge and lane reduction may exist at the downstream which is a certain distance away from an entrance ramp.
The method is characterized in that a plurality of traffic flow detectors are arranged on the expressway at equal distances, the congestion condition of each section of the expressway is judged based on the traffic flow data of each section of the expressway obtained through real-time automatic detection, the traffic flow of the ramp entering the main line is controlled in real time based on a fuzzy self-adaptive PID (proportion integration differentiation) entrance ramp control strategy, and the method has very important practical significance for relieving the congestion of the expressway, improving the traffic efficiency and guaranteeing the traffic safety.
Drawings
Fig. 1 is a flow chart of highway long distance downstream bottleneck entrance ramp control of embodiment 1;
fig. 2 is a schematic diagram of a setting method of a long-distance downstream bottleneck traffic flow detector and an entrance ramp signal control according to embodiment 1;
FIG. 3 is a block diagram of the fuzzy adaptive PID inlet wrap controller of embodiment 1.
Fig. 4 is a traffic flow diagram in the non-control case of embodiment 2.
Fig. 5 is a schematic view of traffic flow under fuzzy adaptive PID entrance ramp control of embodiment 2.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
Example 1
As shown in fig. 1 to 3, the present embodiment provides a method for controlling an entrance ramp of a long-distance downstream bottleneck section of an expressway, including the following steps:
the method comprises the following steps: the method comprises the following steps of setting a traffic flow detector, and acquiring traffic flow data in a highway section, wherein the specific steps are as follows:
a1, arranging traffic flow detectors on a highway with a long distance downstream bottleneck at equal distance, wherein the distance between the front traffic flow detector and the rear traffic flow detector is not more than 300 m, and recording the stake number of each detector in the direction from downstream to upstream;
a2, detecting data such as traffic flow, density, speed, queue length, occupancy and the like of each section at the same time every 30 seconds by a traffic flow detector, and outputting the data to a control platform;
a3, obtaining the historical traffic jam data of the long-distance downstream bottleneck of the expressway after the self-current detector is installed, and the data of the flow, the density, the speed, the occupancy rate and the like of the jammed road section from the traffic police department.
Step two: the construction of the fuzzy self-adaptive PID entrance ramp control algorithm comprises the following specific steps:
a1, in the control of the long-distance downstream bottleneck of the expressway, the optimal solution algorithm of the entrance ramp is mainly aimed at a local area, so that the feedback control is selected, the measurement value of the traffic flow detector is based on, and the traffic condition of the expressway is kept close to the preset value;
a2, selecting fuzzy adaptive PID control capable of adapting to nonlinearity and hysteresis according to the hysteresis of a long-distance downstream bottleneck, and applying the fuzzy adaptive PID control to an entrance ramp control strategy;
a3, determining the structure of the PID controller, linearly combining the proportion (P), integral (I) and derivative (D) of the deviation of the given input from the actual output, and then controlling the object by the combined control amount.
a4, determining a control strategy of the fuzzy adaptive PID controller, wherein the control strategy comprises the following parameters: mu is a control variable of the control object, e is an error signal, ec is an error rate, kp，ki，kdIs a PID parameter. Fuzzy adaptive PID controller at parameter kp，ki，kdAnd e, ec. It continuously detects e and ec during operation and uses the PID increments Δ kp,ΔkiAnd Δ kdAs an output of the controller, and outputs u to the subject.In determining the fuzzy logic rules, a fuzzy set also needs to be defined: in the range from U to [0,1 ]]Any graph of the interval maps to μAIn the above-mentioned manner,
μA:U→[0，1]
a is the fuzzy subset of U, μAIs a membership function of A, muA(x) Called membership of x to a. Mu.sA(x) Indicating the degree or level to which the elements x in the set U belong to the fuzzy subset a. It can be in [0,1 ]]And continuously obtaining values in the closed interval. Mu.sA(x) The closer to 1 the value of (b), the higher the degree to which x belongs to a; mu.sA(x) The closer to 0, the lower the degree of belonging to a.
a5, determining the integral structure of fuzzy self-adapting PID entrance ramp control, and the input signal of the system is the expected flow density rhod(k) I.e. rin(k)＝ρd(k) In that respect Its output signal is the actual flow density ρ (k +1), yout(k +1) ═ ρ (k +1), and the control variable of the system is a ramp adjustment rate r (k) (i.e., the control variable μ of the control object), and the traffic flow of the ramp into the main line is controlled by the ramp adjustment rate, where r (k) is determined by the following formula:
wherein k isp、ki、kdAre parameters determined by fuzzy control, e (k) rin-youtIs an error signal.
Expected traffic density ρd(k) Is an input and e is the difference between the desired density and the actual density. The membership functions for e and ec are described as large Negative (NB), medium Negative (NM), small Negative (NS), zero (Z), small Positive (PS), medium Positive (PM), large Positive (PB). The use of fuzzy sets provides a systematic way to deal with the concepts of ambiguity and inaccuracy. In particular, fuzzy sets may be used to represent linguistic variables. The assignment of fuzzy logic rules is based on expert experience, operating experience and system knowledge; are described as large Negative (NB), medium Negative (NM), small Negative (NS), zero (Z), small positive (NB), (NS), (NmPS), medium positive value (PM), large positive value (PB). k is a radical ofp，ki，kdThe fuzzy logic rules of (1) are shown in table one.
Watch 1
a6, adjusting the entrance ramp control parameters in real time through the interaction with the data platform of the traffic flow detector, thereby providing platform support for the later establishment of a control strategy.
Step three: monitoring real-time traffic flow data of each section of a road section, and when inflection points appear on occupancy oblique accumulated curves corresponding to traffic flow detectors of two adjacent pile numbers, the phenomenon of congestion and queuing of the highway section occurs; otherwise, continuing monitoring;
step four: and if the congestion queuing phenomenon occurs in the third step, immediately feeding back to the control platform, adjusting parameters in the entrance ramp controller, thereby outputting the ramp regulation rate, and controlling the flow of ramp vehicles entering the main line through signal lamp regulation.
Optionally, the design of the fuzzy adaptive PID controller is implemented in Matlab, and the parameter K of the PID controller is matched by using fuzzy rulesp、KiAnd KdAnd carrying out self-adaptive setting.
Example 2
Firstly, the technology is simulated in real time in a cellular transmission model, and firstly, the parameters in the cellular transmission model are calibrated by using loop detector data collected on a real expressway, so that the simulation result is close to the actual situation. Through calibration, the speed of free flow in the cellular transmission model is 105 kilometers per hour, the congestion density is 137 vehicles/kilometers per lane, and the critical density is 16.63 vehicles/kilometers per lane.
Expected traffic density ρd(k) Is an input and e is the difference between the desired density and the actual density. In the example, the congestion density is 137 vehicles/km/lane. To control density and simplify calculations, both e and ec select values of-69 to +69 vehicles/km/lane. Membership functions for e and ec are described as being more negativeValue (NB), medium negative value (NM), small negative value (NS), zero value (Z), small positive value (PS), medium positive value (PM), large positive value (PB).
In fuzzy adaptive PID control based on entrance ramp control, the fuzzy adaptive PID control is realized by the following steps of e, ec and kp、ki、kdA fuzzy rule is established, and the fuzzy reasoning in the fuzzy adaptive controller is used for adjusting kp、ki、kdThe value of (c). The adjustment process should take k into accountp、ki、kdFuzzy logic rules can be used by calculating the current system error e and ec based on online real-time fuzzy adaptive PID control at different times.
According to the rule, k can be found based on the ranges of e and ecp、ki、kdThe incremental changes of (c) are between-510 and +510, and their membership functions are described as "NB", "NM", "NS", "Z", "PS" and "PB".
The congestion situation is judged by an occupancy rate accumulation curve, the curve is drawn based on the occupancy rate data acquired by the k and k +1 traffic flow detectors, and the inflection point is determined according to the following principle: and within three minutes before and after the inflection point, respectively drawing linear fitting straight lines of the two occupancy oblique cumulative curves by taking the inflection point as the intersection point, so that the sum of the total variances of all occupancy deviation values deviating from the two straight lines on the oblique cumulative curves is minimum, and the linear fitting straight lines are determined by a least square method.
When congestion occurs, feeding back to a fuzzy self-adaptive PID entrance ramp control platform, and determining k according to the ranges of e and ecp、ki、kdAnd adjusting iteration parameters and calculating the ramp regulation rate according to the change of each increment, and controlling the traffic flow of the ramp entering the main line by arranging a signal lamp on the entrance ramp.
The lane reduction is arranged at the position 1.6 kilometers away from the junction of the downstream of the expressway, 2-hour simulation is carried out, and the simulation result is shown in fig. 4-5. Congestion occurs without control, and traffic flow runs steadily under fuzzy adaptive PID on-ramp control.
The scope of the present invention is not limited to the above embodiments, and any modifications, equivalent substitutions, improvements, etc. that can be made by those skilled in the art within the spirit and principle of the inventive concept should be included in the scope of the present invention.
Claims (10)
1. A method for controlling an entrance ramp of a long-distance downstream bottleneck section of a highway is characterized by comprising the following steps: the method comprises the following steps:
the method comprises the following steps: setting traffic flow detectors on a highway with a long downstream bottleneck at equal distance, recording the pile number of each detector according to the direction from downstream to upstream, collecting traffic flow data in a highway section, and arranging a signal lamp on one side of a ramp;
step two: selecting feedback control based on traffic flow data acquired by a traffic flow detector; building a fuzzy self-adaptive PID control system according to the hysteresis of the long-distance downstream bottleneck, acquiring traffic flow data in a highway section acquired by a traffic flow detector, and inputting the traffic flow data to the fuzzy self-adaptive PID control system;
step three: expected flow density rho of fuzzy self-adaptive PID control system with input signal of k timed(k) I.e. the input signal rin(k)＝ρd(k) (ii) a The output signal of the system is the actual flow density ρ (k +1), i.e. the output signal yout(k +1) ═ ρ (k +1), the control variable of the system is the ramp regulation rate r (k), the traffic flow of the ramp into the main line is controlled by the ramp regulation rate, r (k) is determined by the following formula:
wherein k isp、ki、kdAre parameters determined by fuzzy control, e (k) rin(k)-yout(k +1) is the error signal, T is the signal period.
Step four: monitoring real-time traffic flow data of each section of the road section through a traffic flow detector, and obtaining the real-time traffic flow data when occupancy inclined cumulative curves corresponding to the traffic flow detectors of two adjacent pile numbersWhen the inflection point appears, the congestion queuing phenomenon appears on the highway section, and when the fuzzy self-adaptive PID control system detects that the inflection point appears on the occupancy cumulative curve, the parameter k in the entrance turn-road controller is adjustedp、ki、kdAnd therefore, the ramp regulation rate is output, and the flow of the ramp vehicle entering the main line is controlled through signal lamp regulation of the ramp.
2. The method of claim 1, wherein two adjacent traffic flow detectors positioned equidistantly are spaced no more than 300 meters apart.
3. The method of claim 1, wherein the traffic flow data comprises: traffic flow, density, speed, queue length and occupancy data for each section.
4. The method of claim 1, wherein step three further comprises: obtaining historical traffic jam data of a long-distance downstream bottleneck of the highway after an automatic current-passing detector is installed from a traffic police department, wherein the historical traffic jam data is used for determining a fuzzy self-adaptive PID control logic fuzzy rule; the historical traffic congestion data includes traffic volume, density, speed, and occupancy data for congested road segments.
5. The method of claim 1, further comprising: adaptive PID control system at parameter k by obfuscationp，ki，kdAnd e (k), ec, where e (k) is the difference between the expected and actual flow density, ec is the error rate, PID control continuously detects e (k) and ec during operation, using the actual flow density ρ (k +1) as the output of the PID control system, and r (k) as the control variable.
6. The method of claim 5, wherein the method is performed by applying a voltage at e, ec, and kp、ki、kdWith fuzzy rules, in fuzzy adaptive controllersTo adjust k by fuzzy inferencep、ki、kdA value of (d); taking k into account during the adjustment processp、ki、kdFuzzy logic rules are used by calculating e and ec based on online real-time fuzzy adaptive PID control at different times.
7. The method of claim 6, wherein k is derived based on a range of e and ecp、ki、kdThe e and ec membership functions are described as large negative, medium negative, small negative, zero, small positive, medium positive, large positive, respectively.
8. The method of claim 1, wherein the inflection point determination method is as follows: and within three minutes before and after the inflection point, the linear fitting straight line of the two occupancy inclined accumulation curves is respectively drawn by taking the inflection point as the intersection point, so that the sum of the total variances of all occupancy deviation values deviating from the two straight lines on the inclined accumulation curve is minimum.
9. The method of claim 8, wherein the line of linear fit is determined by a least squares method.
10. The method of any one of claims 1 to 9, wherein the method is applied to a traffic flow control device.
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