CN107680393B - A time-varying universe-based intelligent control method for traffic lights at intersections - Google Patents
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Abstract
本发明公开一种基于时变论域的十字路口交通信号灯智能控制方法。该方法首先利用十字路口的检测器，采集交通数据；以当前绿灯方向相位上的最大排队长度及红灯方向各相位上的排队长度数据为依据，对十字路口拥堵状况进行评估，在当前交通流情况下，得出周期长度所在论域；在所述周期长度论域下，以当前绿灯方向相位上的最大排队长度及每辆车的平均停车次数为输入，当前绿灯方向相位分配的绿灯时长为输出，列出动态模糊规则；应用重心法进行清晰化计算，得出绿灯时长；综合考虑十字路口交通流量与驾驶员通行安全，设置限定条件，得出最终绿灯时长，完成配时方案的优化。本发明的有益效果是，能有效增强道路交叉路口通行能力，并减少车辆延误时间。
The invention discloses an intelligent control method for a traffic signal light at an intersection based on a time-varying universe. The method first uses the detector at the intersection to collect traffic data; based on the current maximum queuing length on the green light direction phase and the queue length data on each phase in the red light direction, it evaluates the congestion situation at the intersection, and evaluates the congestion at the current traffic flow. In this case, the domain of the cycle length is obtained; in the domain of the cycle length, the maximum queuing length on the current green light direction phase and the average number of stops per vehicle are used as inputs, and the green light duration allocated to the current green light direction phase is Output, list the dynamic fuzzy rules; apply the center of gravity method for clear calculation to obtain the green light duration; comprehensively consider the traffic flow at the intersection and the safety of the driver, set the limited conditions, obtain the final green light duration, and complete the optimization of the timing scheme. The beneficial effect of the invention is that the traffic capacity of the road intersection can be effectively enhanced, and the vehicle delay time can be reduced.
Description
Technical Field
The invention belongs to the field of intelligent traffic systems, and particularly relates to a real-time timing method for traffic signal lamps.
Background
A timing signal timing method is mainly a Webster timing method in British in the world, and on the basis of the timing signal timing method, an ARRB (Australian Road Research Board) timing method in Australia considers the super-saturated traffic condition, so that the timing signal timing method is the correction and extension of a Webster delay model. The HCM (high way Capacity Man) timing method in the United states is also widely used. In China, a conflict point method, a critical lane method, an estimation method and a Shanghai city comprehensive algorithm exist. The comprehensive algorithm in Shanghai city is provided based on the existing signal timing method at home and abroad and by combining the traffic characteristics of China, and the timing delay calculation is the same as that of the American HCM algorithm.
With the rapid development of economy, the number of motor vehicles is increased rapidly, the traffic demand is expanded rapidly, and the timing signal timing method cannot meet the requirement of efficiently improving the urban traffic jam condition.
With the expansion of the application range and the increase of the application area of the intelligent traffic system, the real-time control and optimization of traffic signals arouse the attention of more and more scholars at home and abroad. However, because the urban road traffic system has extremely high randomness, the traffic parameters can also change greatly even in a short time, and great difficulty is brought to real-time timing of traffic signal lamps. When the states of objects are described, the problem that people are difficult to define membership functions of fuzzy sets is solved by aiming at the situation that the domains of discourse change along with time change, time-varying domains of discourse and dynamic fuzzy rules, and a method is provided for the analysis of a complex system. The invention is based on the time-varying domain of discourse, utilizes the dynamic fuzzy rule to dynamically adjust the cycle length and the duration of the traffic light in real time, completes the real-time timing of the traffic signal light at the crossroad, effectively enhances the traffic capacity of the crossroad and reduces the vehicle delay time.
Disclosure of Invention
The invention provides an intelligent control method of a crossroad traffic signal lamp based on a time-varying domain, which comprises the following steps:
step S1: collecting required traffic data by using a detector at an intersection;
step S2: evaluating the congestion degree of the intersection by using the obtained data and taking the maximum queuing length in the phase of the current green light direction and the queuing length data in each phase of the current red light direction as the basis to obtain the domain of cycle length under the current traffic flow condition;
step S3: listing a dynamic fuzzy rule by taking the current period length as a domain of discourse, taking the maximum queuing length on the current green light direction phase and the average parking times of each vehicle as input, and taking the green light time length distributed by the current green light direction phase as output;
step S4: performing clearness calculation by using a gravity center method to obtain the duration of a green light;
step S5: and judging the obtained green light time length, setting constraint conditions and finishing the timing scheme.
Wherein the step S1 is further: the required traffic data comprises the maximum queue length gL in the phase of the current green light direction and the queue length rL in each phase of the current red light directionjAnd average number of stops per vehicle
The average stopping times of each vehicle are the average times of the vehicles which exit the intersection in the current green light direction phase and meet the red light.
Wherein the step S2 further comprises the steps of:
step S21: the proper range of the period length c is 40-180 s, the domain of the period length is set to be omega (t), and the domain of the period length c under the time-varying domain is divided into continuous time-varying domain sequences: { omegak(t)},(k∈N)，Ωk(t)＝[0,40+20k]Wherein k is 1, 2.. 6, 7;
step S22: and evaluating the congestion degree of the intersection according to the average queuing length item in red light in the level-4 service level suggested by the Beijing city planning and design institute. According to its proposed level 4 service level, a queue length L at red light of less than 50 meters is unobstructed VS, between 50 meters and 100 meters is unobstructed NS, between 100 meters and 150 meters is congested NJ, over 150 meters is congested VJ; the obtained gL and rLjThe digital form of the data, according to the proposed level 4 service level, is converted into the form of words VS, NS, NJ, VJ;
step S23: and obtaining the discourse domain of the cycle length under the current traffic flow condition according to the combination condition of the base words.
Wherein the step S3 further comprises the steps of:
step S31: the input gL,
Output quantity TlFuzzification;
step S32: the cycle length discourse domain is a continuous time-varying discourse domain sequence: { omegak(t) }, (k ∈ N), a membership function that defines a fuzzy set over the domain of interest as it changes over timeAlso, changes occur with time, respectively at each omegak(t) input quantities gL and rL are setjAnd an output amount
Membership function corresponding to each linguistic value;
step S33: considering all possible cases, listing the argument field of the period length to be omega under the time-varying argument fieldkDynamic fuzzy rule R at (t)k。
Wherein the step S4 further comprises the steps of:
step S41: solving a fuzzy relation matrix R by a dynamic fuzzy ruleiThe overall fuzzy rule is R;
step S42: obtaining a fuzzy set corresponding to the output variable according to the total fuzzy relation and the reasoning and synthesizing rule;
step S43: and converting the fuzzy quantity obtained by fuzzy reasoning into a clear quantity in the current theoretical domain, and carrying out clear calculation to obtain the green light duration.
Wherein the step S5 further comprises the steps of:
step S51: comprehensively considering intersection traffic flow and driver traffic safety, relieving traffic jam and reducing red light running phenomena, setting reminding, and if the red light duration of a certain phase exceeds 130s, receiving reminding 1 by a system;
step S52: when the system receives the prompt 1, the system adjusts the passing time of the current green light phase according to the predicted change trend of the traffic flow through the prediction of the short-time traffic flow;
step S53: t isl≤Nmax，NmaxSetting the maximum value of the passing time according to the traffic flow of the actual intersection, setting the value not to exceed 80s, setting a prompt, and if the duration of the green light exceeds NmaxThen the system receives reminder 2;
step S54: and when the system receives the prompt 2, adjusting the current green light phase passing time, and setting the current green light phase passing time as the maximum value of the passing time.
The invention has the beneficial effects that: by dynamically adjusting the period length of the traffic lights and distributing the length of the green light time in real time, the number of vehicles passing through the intersection is increased, the maximum queuing length and the average queuing length are reduced, the average delay of each time interval is reduced, and the timing scheme effectively relieves the intersection congestion.
Drawings
FIG. 1 is a block diagram of a cross traffic signal lamp real-time timing method of the present invention;
FIG. 2 is a flowchart of step S2 of the present invention;
FIG. 3 is a schematic diagram of four-phase control at the intersection;
FIG. 4 shows that gL is Ω in the cycle length domain1(t) and Ω2(t) membership function;
FIG. 5 shows that gL is Ω in the cycle length domain3(t) and Ω4(t) membership function.
Detailed Description
The present invention will be described in detail below with reference to the attached drawings, and it should be noted that the described embodiments are only intended to facilitate understanding of the present invention, and do not have any limiting effect thereon.
The invention provides a real-time timing method for traffic lights at an intersection. As shown in fig. 1, in particular, the method comprises the steps of:
step S1: collecting required traffic data by using a detector at an intersection;
the data to be collected by the method comprises the maximum queue length gL in the phase position of the current green light direction and the queue length rL in each phase position of the current red light directionjAnd average number of stops per vehicle
The average stopping times of each vehicle are the average times of the vehicles which exit the intersection in the current green light direction phase and meet the red light.
Step S2: evaluating the congestion degree of the intersection by using the obtained data and taking the maximum queuing length in the phase of the current green light direction and the queuing length data in each phase of the current red light direction as the basis to obtain the domain of cycle length under the current traffic flow condition, wherein the flow chart of the process is shown in FIG. 2;
step S22: and evaluating the congestion degree of the intersection according to the average queuing length item in red light in the level-4 service level suggested by the Beijing city planning and design institute. According to its proposed level 4 service level, a queue length L at red light of less than 50 meters is unobstructed VS, between 50 meters and 100 meters is unobstructed NS, between 100 meters and 150 meters is congested NJ, over 150 meters is congested VJ; the obtained gL and rLjThe digital form of the data, according to the proposed level 4 service level, is converted into the form of words VS, NS, NJ, VJ;
step S23: obtaining a discourse domain of the cycle length under the current traffic flow condition according to the combination condition of the base words; taking four-phase control at an intersection as an example, fig. 3 shows. The four-phase base word combination has 35 cases, when the base word combination form is VS, VS// VS, NS, the cycle length domain is omega1(t); the cycle length domain is omega when the combination is VS, NJ// VS, VJ// VS, NS// VS, NS, NJ// VS, NS, VJ// VS, NJ, VJ2(t); and analogizing in turn to obtain the corresponding length discourse domain under each combination condition of the base words.
Step S3: under the cycle length domain, taking the maximum queuing length in the phase of the current green light direction and the average parking times of each vehicle as input, taking the green light time length distributed by the phase of the current green light direction as output, and listing dynamic fuzzy rules;
step S31: the input gL,
Output quantity TlFuzzification of (1); the range of variation of gL is determined from experience with actual traffic control. Setting the variation range of the input quantity gL as 0-200, the domain of discourse as {0,1,2,3,4,5,6,7,8,9,10}, defining 7 fuzzy subsets on the domain of discourse, and setting the corresponding language value as { l }1(very short), l2(short,. l)3(shorter,. l)4(intermediate) l5(longer,. l)6(length,. l)7(very long) }; set the input quantity
The variation range of (1) is 0-4, the domain of discourse is {0,1,2,3,4,5}, 5 fuzzy subsets are defined on the domain of discourse, and the corresponding language value is { n }1(small), n2(smaller), n3(intermediate), n4(larger), n5(large) }; setting output TlIs 0 to 40+20k, k is 1,2,3,4,5,6,7, the domain of discourse is {0,1,2,3,4,5,6,7,8,9,10} and defines 5 fuzzy subsets on the domain of discourse, the corresponding linguistic value is { t } t1(short), t2(shorter), t3(intermediate), t4(longer), t5(long) };
step S32: the cycle length discourse domain is a continuous time-varying discourse domain sequence: { omegak(t) }, (k ∈ N), when the domain of discourse changes with time, the membership function of the fuzzy set defined on the domain of discourse also changes with time, and is respectively at each omegak(t) the input quantity gL,
Output quantity TlThe membership function corresponding to each linguistic value; FIG. 4 shows that the domain of the current cycle length is Ω1(t) and Ω2(t) inputting membership functions corresponding to the linguistic values of the quantity gL; FIG. 5 shows that the clock domain is Ω3(t) and Ω4(t) inputting membership functions corresponding to the linguistic values of the quantity gL; similarly when the cycle length is argued as Ω5(t)、Ω6(t) and Ω7(t), the membership functions corresponding to the linguistic values of the input quantity gL are similar to those in fig. 4 and 5, respectively; similarly, an input quantity is defined
Output quantity TlAt each omegak(t) membership functions corresponding to the linguistic values;
step S33: considering all possible cases, listing the argument field of the period length to be omega under the time-varying argument fieldkDynamic fuzzy rule R at (t)k。
Step S4: performing clearness calculation by using a gravity center method to obtain the duration of a green light;
step S41: by dynamic stateFuzzy rule solving fuzzy relation matrix RiThe overall fuzzy rule is R; in the syntax of the fuzzy language, the rules are expressed in a schema of if (rule antecedent) then (conclusion). Each language control rule corresponds to a fuzzy relation ri k＝(li×ni)T×tiThe fuzzy relation matrix R can be obtained by the fuzzy control ruleiThe overall fuzzy relationship is
Step S42: obtaining a fuzzy set corresponding to the output variables according to the total fuzzy relation and the reasoning and synthesizing rule:
step S43: and converting the fuzzy quantity obtained by fuzzy reasoning into a clear quantity in the current theoretical domain, and carrying out clear calculation to obtain the green light duration.
Step S5: and judging the obtained green light time length, setting constraint conditions and finishing the timing scheme.
Step S51: comprehensively considering intersection traffic flow and driver traffic safety, relieving traffic jam and reducing red light running phenomena, setting reminding, and if the red light duration of a certain phase exceeds 130s, receiving reminding 1 by a system;
step S52: when the system receives the prompt 1, the system adjusts the passing time of the current green light phase according to the predicted change trend of the traffic flow through the prediction of the short-time traffic flow;
step S53: t isl≤Nmax，NmaxSetting the maximum value of the passing time according to the traffic flow of the actual intersection, setting the value not to exceed 80s, setting a prompt, and if the duration of the green light exceeds NmaxThen the system receives reminder 2;
step S54: and when the system receives the prompt 2, adjusting the current green light phase passing time, and setting the current green light phase passing time as the maximum value of the passing time.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can understand that the modifications or substitutions within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (4)
1. An intelligent control method for traffic signal lamps at crossroads based on time-varying discourse domain is characterized by comprising the following steps:
step S1: collecting required traffic data by using a detector of the crossroad;
step S2: evaluating the congestion degree of the intersection by using the obtained data and taking the maximum queuing length in the phase of the current green light direction and the queuing length data in each phase of the current red light direction as the basis to obtain the domain of cycle length under the current traffic flow condition;
step S3: under the cycle length domain, taking the maximum queuing length in the phase of the current green light direction and the average parking times of each vehicle as input, taking the green light time length distributed by the phase of the current green light direction as output, and listing dynamic fuzzy rules;
step S4: performing clearness calculation by using a gravity center method to obtain the duration of a green light;
step S5: judging the obtained green light duration, setting constraint conditions and completing a timing scheme;
the step S2 includes the steps of:
step S21: the proper range of the period length c is 40-180 s, the domain of the period length is set to be omega (t), and the domain of the period length c under the time-varying domain is divided into continuous time-varying domain sequences: { omegak(t)},(k∈N)；
Step S22: evaluating the congestion degree of the intersection by adopting the level-4 service level suggested by the Beijing city planning and design institute and taking the average queuing length item in red light as the basis; the obtained gL and rLjThe digital form of the data, according to the proposed level 4 service level, is converted into the form of words VS, NS, NJ, VJ;
step S23: obtaining a discourse domain of the cycle length under the current traffic flow condition according to the combination condition of the base words;
the step S3 includes the steps of:
step S31: the input gL,
Output quantity TlFuzzification;
step S32: the cycle length discourse domain is a continuous time-varying discourse domain sequence: { omegak(t) }, (k ∈ N), when the domain of discourse changes with time, the membership function of the fuzzy set defined on the domain of discourse also changes with time, and is respectively at each omegak(t) input quantities gL and rL are setjAnd an output amount
Membership function corresponding to each linguistic value;
step S33: considering all possible cases, listing the argument field of the period length to be omega under the time-varying argument fieldkDynamic fuzzy rule R at (t)k；
2. The method according to claim 1, wherein the step S1 is further comprising: the required traffic data comprises the maximum queue length gL in the phase of the current green light direction and the queue length rL in each phase of the current red light directionjAnd average number of stops per vehicle
3. The method according to claim 1, wherein the step S4 further comprises the steps of:
step S41: solving a fuzzy relation matrix R by a dynamic fuzzy ruleiThe overall fuzzy rule is R;
step S42: obtaining a fuzzy set corresponding to the output variable according to the total fuzzy relation and the reasoning and synthesizing rule;
step S43: and converting the fuzzy quantity obtained by fuzzy reasoning into a clear quantity in the current theoretical domain, and carrying out clear calculation to obtain the green light duration.
4. The method according to claim 1, wherein the step S5 further comprises the steps of:
step S51: comprehensively considering the traffic flow of the crossroad and the traffic safety of drivers, relieving traffic jam and reducing the phenomenon of running red light, setting a reminder, and if the red light duration of a certain phase exceeds 130s, receiving the reminder 1 by the system;
step S52: when the system receives the prompt 1, the system adjusts the passing time of the current green light phase according to the predicted change trend of the traffic flow through the prediction of the short-time traffic flow;
step S53: t isl≤Nmax，NmaxSetting the maximum value of the passing time according to the traffic flow of the actual intersection, setting the value not to exceed 80s, setting a prompt, and if the duration of the green light exceeds NmaxThen the system receives reminder 2;
step S54: and when the system receives the prompt 2, adjusting the current green light phase passing time, and setting the current green light phase passing time as the maximum value of the passing time.
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| CN111951576A (en) * | 2020-08-17 | 2020-11-17 | 国为(南京)软件科技有限公司 | Traffic light control system based on vehicle identification and method thereof |
| CN112581778B (en) * | 2020-12-02 | 2023-03-14 | 张鹏 | Traffic signal intelligent guiding system based on bus priority mechanism |
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