CN108648472B - Method and system for setting maximum green of signal control intersection - Google Patents
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Abstract
The invention relates to the technical field of urban road traffic signal control, and particularly discloses a method for setting maximum green of a signal control intersection, which comprises the following steps: determining initial maximum green, minimum maximum green and maximum green of each phase of a signal control intersection; acquiring actual green light running time of each phase of a signal control intersection; performing self-learning dynamic optimization adjustment on the initial maximum green of each phase according to the initial maximum green of each phase and the actual green light running time to obtain an adjusted new maximum green of each phase; and repeating the step of self-learning dynamic optimization adjustment, and carrying out periodic iterative update on the adjusted new maximum green. The invention also discloses a system for setting the maximum green of the signalized intersection. The method for setting the maximum green of the signal control intersection can flexibly set the maximum green of the intersection to adapt to the condition of sudden change of green time requirement caused by sudden change of traffic flow.
Description
Technical Field
The invention relates to the technical field of urban road traffic signal control, in particular to a method and a system for setting the maximum green of a signal control intersection.
Background
The induction control is that traffic flow detection equipment is arranged at an intersection, and the current green light release time is prolonged on the basis of minimum green according to the vehicle reaching conditions. Because the green light of the current phase is prolonged, the traffic of the vehicles in the intersecting direction is influenced, the waiting time is increased, and the delay is increased. Therefore, the maximum green setting is one of the key parameters of the induction control, if the setting is large, one maximum green is used all day long, and green light loss caused by inaccurate detection equipment can be generated; if the maximum green is set in different time periods, the situation that the required green light time mutation caused by short-time concentrated trip of the traffic flow cannot be adapted exists.
Due to the sensitivity of the detection equipment, possible faults and other factors, corresponding maximum green can be set respectively by combining the traffic flow change conditions in different periods when induction control is carried out. Generally, the peak period maximum green value is greater than the flat period value. Since the traffic flow is dynamically changed, the set maximum green may not be adapted to the demand of the traffic flow due to a sudden change of the traffic flow. If concentrated vehicle aggregation occurs at a certain intersection in a peak-flat period, the traffic flow even exceeds the peak-flat period, and therefore, the maximum green set in the peak-flat period cannot meet the traffic demand in the peak-flat period.
Therefore, how to flexibly set the maximum green of the intersection to adapt to the sudden change of the green time requirement caused by the sudden change of the traffic flow becomes a technical problem to be solved by the technical personnel in the field.
Disclosure of Invention
The invention aims to at least solve one of the technical problems in the prior art, and provides a method and a system for setting the maximum green of a signalized intersection so as to solve the problems in the prior art.
As a first aspect of the present invention, there is provided a signalized intersection maximum green setting method, wherein the signalized intersection maximum green setting method includes:
determining initial maximum green, minimum maximum green and maximum green of each phase of a signal control intersection;
acquiring actual green light running time of each phase of a signal control intersection;
performing self-learning dynamic optimization adjustment on the initial maximum green of each phase according to the initial maximum green of each phase and the actual green light running time to obtain an adjusted new maximum green of each phase;
and repeating the step of self-learning dynamic optimization adjustment, and carrying out periodic iterative update on the adjusted new maximum green.
Preferably, the determining an initial maximum green for each phase of the signalized intersection comprises:
counting the traffic flow of the signal control intersection;
dividing signal control intersection control time periods according to the signal control intersection traffic flow;
respectively counting the queuing conditions of the released vehicles in each control time period according to different control time periods;
and setting the initial maximum green of each phase of the signalized intersection according to the queuing condition of each released vehicle in each control time period, wherein the difference value between the initial maximum green and the minimum green is not less than 5 s.
Preferably, the calculation formula that the initial maximum green of each phase of the signalized intersection is set according to the queuing condition of each released vehicle in each control period, and the difference between the initial maximum green and the minimum green is not less than 5s is as follows:
wherein,
an initial maximum green, in s, representing each phase of the control period; m represents the average number of vehicles in each phase release direction of the control period, and the unit is a vehicle; h istThe average headway is expressed in units of s; gminDenotes minimum green, in units of s; Δ t represents the increased green time in units of s.
Preferably, the minimum green is set according to the pedestrian crossing and left-turning non-motor vehicle passing requirements, and the value range of the increased green light time is between 5s and 10 s.
Preferably, the determining the minimum maximum green of each phase of the signalized intersection comprises:
counting the first green light time required for all 15% of vehicles in the 15% position-separated queue at the signalized intersection to pass through the signalized intersection in the current control period;
setting the first green light time as minimum maximum green, wherein the difference value between the minimum maximum green and the minimum green is not less than 3s, and the calculation formula is as follows:
wherein,
represents the minimum maximum green, in units of s; a represents the 15% of the number of vehicles in the position of the queue when the vehicles in the queue are arranged from small to large in the control period, and the unit is a vehicle; h istThe average headway is expressed in units of s; gminRepresents the minimum green, in units of s.
Preferably, the determining the maximum green of each phase of the signalized intersection comprises:
counting second green light time required for the maximum number of queued vehicles of each phase to pass through the signalized intersection in the current control period;
counting the time of a third green light required by all the following vehicles passing through the signal control intersection;
setting the sum of the second green light time and the third green light time as the maximum green, wherein the maximum green is less than the waiting time of the maximum red light of the pedestrian, and the calculation formula is as follows:
wherein,
represents the maximum green maximum in s; b represents the maximum number of queued vehicles in the control period, and the unit is a vehicle; b' represents the number of subsequent arriving vehicles in units of vehicles; h istThe average headway is expressed in units of s; t is tδAnd represents the maximum waiting time of the pedestrian red light, and the unit is s.
Preferably, the pedestrian red light maximum waiting time is 90 s.
Preferably, the performing self-learning dynamic optimization adjustment on the initial maximum green of each phase according to the initial maximum green of each phase and the actual green light running time to obtain an adjusted new maximum green of each phase includes:
taking the initial maximum green of each phase as an initial value of a self-learning system, and continuously operating for n periods;
comparing the magnitude relationship of the n periods of actual operating green time to the initial maximum green for each phase;
if the actual green light running time of n periods is less than the initial maximum green, setting the maximum value of the actual green light running time of the n continuous periods as the new maximum green after adjustment;
if the actual green light running time of n periods is equal to the initial maximum green, increasing the initial maximum green, and selecting the minimum of the increased initial maximum green and the maximum green as the new adjusted maximum green;
if the actual green light operation time of n periods is neither less than the initial maximum green nor equal to the initial maximum green, keeping the initial maximum green unchanged.
Preferably, the value range of n is 3-5, and the green time of the initial maximum rate increase is 5 s.
As a second aspect of the present invention, there is provided a signalized intersection maximum green setting system, wherein the signalized intersection maximum green setting system comprises:
the determining module is used for determining initial maximum green, minimum maximum green and maximum green of each phase of the signalized intersection;
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring the actual green light running time of each phase of a signal control intersection;
the self-learning module is used for carrying out self-learning dynamic optimization adjustment on the initial maximum green of each phase according to the initial maximum green of each phase and the actual green light running time to obtain the new adjusted maximum green of each phase;
and the iteration updating module is used for repeatedly executing the step of self-learning dynamic optimization adjustment and periodically updating the adjusted new maximum green in an iteration mode.
The method for setting the maximum green of the signal control intersection changes the traditional fixed maximum green setting method, adopts a self-learning dynamic optimization method, and can realize dynamic adjustment of the maximum green of the intersection according to the traffic flow condition. On one hand, the condition that the green light time is not matched with the traffic flow caused by the fault of the traffic flow detection equipment can be avoided; on the other hand, the signal timing effect can be improved, and the method is suitable for the situation that the green light time requirement of the traffic flow is suddenly changed due to sudden change.
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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. In the drawings:
fig. 1 is a flowchart of a method for setting maximum green at a signalized intersection according to the present invention.
Fig. 2 is a diagram of a specific embodiment of a method for setting the maximum green of a signalized intersection provided by the present invention.
Fig. 3 is a schematic structural diagram of a system for setting the maximum green color of a signalized intersection provided by the present invention.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present invention, are given by way of illustration and explanation only, not limitation.
As one aspect of the present invention, there is provided a method for setting a maximum green color at a signalized intersection, wherein as shown in fig. 1, the method for setting a maximum green color at a signalized intersection includes:
s110, determining initial maximum green, minimum maximum green and maximum green of each phase of a signal control intersection;
s120, acquiring actual green light running time of each phase of the signalized control intersection;
s130, performing self-learning dynamic optimization adjustment on the initial maximum green of each phase according to the initial maximum green of each phase and the actual green light running time to obtain an adjusted new maximum green of each phase;
and S140, repeatedly executing the step of self-learning dynamic optimization adjustment, and carrying out periodic iterative updating on the adjusted new maximum green.
The method for setting the maximum green of the signal control intersection changes the traditional fixed maximum green setting method, adopts a self-learning dynamic optimization method, and can realize dynamic adjustment of the maximum green of the intersection according to the traffic flow condition. On one hand, the condition that the green light time is not matched with the traffic flow caused by the fault of the traffic flow detection equipment can be avoided; on the other hand, the signal timing effect can be improved, and the method is suitable for the situation that the green light time requirement of the traffic flow is suddenly changed due to sudden change.
As a specific embodiment of determining the initial maximum green, the determining the initial maximum green of each phase of the signally controlled intersection includes:
counting the traffic flow of the signal control intersection;
dividing signal control intersection control time periods according to the signal control intersection traffic flow;
respectively counting the queuing conditions of the released vehicles in each control time period according to different control time periods;
and setting the initial maximum green of each phase of the signalized intersection according to the queuing condition of each released vehicle in each control time period, wherein the difference value between the initial maximum green and the minimum green is not less than 5 s.
Specifically, intersection traffic flow is counted, intersection control time periods are divided according to the traffic flow, and control time periods such as early and late peaks, flat peaks and low valleys are determined. And counting the queuing conditions of vehicles in each release phase in the control time period aiming at different control time periods, and setting the initial maximum green light time of each phase according to the queuing conditions, wherein the difference between the initial maximum green and the minimum green is not less than 5.
Further specifically, the initial maximum green of each phase of the signalized intersection is set according to the queuing condition of each released vehicle in each control period, and a calculation formula that the difference between the initial maximum green and the minimum green is not less than 5s is as follows:
wherein,
an initial maximum green, in s, representing each phase of the control period; m represents the average number of vehicles in each phase release direction of the control period, and the unit is a vehicle; h istThe average headway is expressed in units of s; gminDenotes minimum green, in units of s; Δ t represents the increased green time in units of s.
It can be understood that the minimum green is set according to the pedestrian crossing and left-turning non-motor vehicle passing requirements, and the value range of the increased green time is between 5s and 10 s.
It should be understood that the value of the increased green time can also be set reasonably according to the condition of the signalized intersection.
As a specific embodiment of determining the minimum maximum green, the determining the minimum maximum green of each phase of the signally controlled intersection includes:
counting the first green light time required for all 15% of vehicles in the 15% position-separated queue at the signalized intersection to pass through the signalized intersection in the current control period;
setting the first green light time as minimum maximum green, wherein the difference value between the minimum maximum green and the minimum green is not less than 3s, and the calculation formula is as follows:
wherein,
represents the minimum maximum green, in units of s; a represents the 15% of the number of vehicles in the position of the queue when the vehicles in the queue are arranged from small to large in the control period, and the unit is a vehicle; h istThe average headway is expressed in units of s; gminRepresents the minimum green, in units of s.
It should be noted that the minimum maximum green is the minimum value that the maximum green can be set when the induction control signal controls the intersection to perform the maximum green self-learning dynamic adjustment. The setting method of the minimum maximum green is that the green light time required by all vehicles in the 15% position-by-position queuing to pass through the intersection is controlled at the current time period, and the difference between the minimum maximum green and the minimum green is not less than 3 s.
As a specific embodiment of determining the maximum green, the determining the maximum green of each phase of the signally controlled intersection includes:
counting second green light time required for the maximum number of queued vehicles of each phase to pass through the signalized intersection in the current control period;
counting the time of a third green light required by all the following vehicles passing through the signal control intersection;
setting the sum of the second green light time and the third green light time as the maximum green, wherein the maximum green is less than the waiting time of the maximum red light of the pedestrian, and the calculation formula is as follows:
wherein,
represents the maximum green maximum in s; b represents the maximum number of queued vehicles in the control period, and the unit is a vehicle; b' represents the number of subsequent arriving vehicles in units of vehicles; h istThe average headway is expressed in units of s; t is tδAnd represents the maximum waiting time of the pedestrian red light, and the unit is s.
Preferably, the pedestrian red light maximum waiting time is 90 s.
It should be understood that max green is the maximum value that the inductively controlled intersection can achieve when self-learning dynamically adjusting max green. The calculation method is that the sum of the green light time required by the current control phase for the maximum number of queued vehicles to pass through the intersection and the time required by the subsequent arrival vehicles to pass through the intersection is satisfied, and the limitation of the maximum red light waiting time and the period of pedestrians is satisfied.
It should be noted that the maximum green is the maximum green given for the maximum queuing time of each phase, and generally, for the signalized intersection, the time when the maximum queuing vehicle number of each phase appears is slightly different, so the maximum green of each phase does not appear at the same time.
Specifically, the performing self-learning dynamic optimization adjustment on the initial maximum green of each phase according to the initial maximum green of each phase and the actual green light running time to obtain an adjusted new maximum green of each phase includes:
taking the initial maximum green of each phase as an initial value of a self-learning system, and continuously operating for n periods;
comparing the magnitude relationship of the n periods of actual operating green time to the initial maximum green for each phase;
if the actual green light running time of n periods is less than the initial maximum green, setting the maximum value of the actual green light running time of the n continuous periods as the new maximum green after adjustment;
if the actual green light running time of n periods is equal to the initial maximum green, increasing the initial maximum green, and selecting the minimum of the increased initial maximum green and the maximum green as the new adjusted maximum green;
if the actual green light operation time of n periods is neither less than the initial maximum green nor equal to the initial maximum green, keeping the initial maximum green unchanged.
It should be noted that the phase actual green time is the result of the sensing control signal controlling the intersection phase green time to actually go, and the value range is between the minimum green and the set maximum green. The phase actual green light running time can reflect the traffic flow condition to a certain extent, and if the actual green light running time is closer to the minimum green, the traffic flow is relatively small; if the green color is closer to or equal to the set maximum green, the traffic flow is larger.
Taking initial maximum green as an initial value of system input, continuously operating for n periods, and comparing the actual green lamp operating time g of n period phasesiAnd phase initial maximum green
If the maximum green time is less than the initial maximum green time, setting the maximum value of the phase green time in the continuous n periods as a new maximum green time; if equal to the initial maximum green, then increase the current maximum green to
Otherwise the set maximum green is kept constant. The specific calculation formula is as follows:
wherein, giIndicating that the phase actually runs the green light time with the unit of s under the induction control condition;
an initial maximum green, in units of s, representing the current control phase; n represents the number of cycles of continuous operation, can be set according to the actual situation, generally take 3-5; g'maxRepresents the new maximum green after the maximum green learning adjustment, with the unit of s; t represents an increased green time, typically 5 s;
represents the maximum green maximum in s;
represents the minimum maximum green in s.
It should be understood that f (i) represents the actual operating green time g for n cycle phasesiAnd phase initial maximum green
As a result of the comparison, i represents a period, and μ represents a sum of comparison results for n periods.
It should be noted that the value of n is generally suggested to be 3-5, and can be set according to specific situations. The larger the value of n is, the longer the time required for one adjustment is, i.e., the lower the adjustment frequency of the maximum green is; the smaller the value of n, the shorter the time required for adjustment once, and therefore the higher the adjustment frequency of maximum green.
Inputting the maximum green value after learning adjustment into the system, repeating the adjustment steps after running for n periods, and iteratively generating a new maximum green value so as to achieve the result of dynamic adjustment of the maximum green.
The following describes in detail a specific implementation process of the method for setting the maximum green color of the signalized intersection according to the present invention, with reference to fig. 2.
(1) Determining an initial maximum Green
Intersection control time periods are determined according to traffic flow, and the dividing result of a certain intersection control time period is shown in the following table 1.
TABLE 1 Signal control time interval division at certain intersection
| Serial number | Time period | Time period name |
| 1 | 0:00-6:00 | Low peak |
| 2 | 6:00-7:00 | Early peak transition |
| 3 | 7:00-9:00 | Early peak |
| 4 | 9:00-17:00 | Flat peak |
| 5 | 17:00-19:00 | Late peak |
| 6 | 19:00-22:00 | Evening flat peak |
| 7 | 22:00-24:00 | Low peak |
And counting the queuing condition of vehicles in each release phase in the control time period aiming at different control time periods, and setting the initial maximum green light time of each phase according to the queuing condition. Taking an example of the setting of the initial maximum green of the early peak, the specific setting method is as follows:
wherein,
an initial maximum green, in s, representing each phase of the control period; m represents the average number of vehicles in each phase release direction of the control period, and the unit is a vehicle; h istThe average headway is expressed in units of s; gminDenotes minimum green, in units of s; Δ t represents the increased green time in units of s. In general setting, the maximum green should be larger than the minimum green by more than 5 s; the delta t is generally 5-10s and is reasonably set according to the crossing condition.
According to the statistical result, taking the average headway h of the straight linetFor 2s, and for 3s for left turn, the average number of queued vehicles for each phase and the corresponding initial maximum green result are shown in table 2 below:
TABLE 2 number of queued vehicles and initial maximum green value for each phase
(2) Determining minimum maximum Green
The minimum maximum green is the lower limit of the induction control intersection when the maximum green is dynamically adjusted. Similar to the method for determining the initial maximum green, the green light time required by all vehicles which are queued in 15% positions in the current control period and pass through the intersection is taken as the minimum maximum green, and is not less than the time required by pedestrians to cross the street and non-motor vehicles to cross the street; meanwhile, the difference between the minimum maximum green and the minimum green should be not less than 3 s.
Wherein,
represents the minimum maximum green, in units of s; a represents the 15% of the number of vehicles in the position of the queue when the vehicles in the queue are arranged from small to large in a control time period, the unit is a vehicle, and an integer is taken; h istThe average headway is expressed in units of s; gminRepresents the minimum green, in units of s.
Taking the early peak hours as an example, the 15% quantile value of the number of queued vehicles and the resulting minimum maximum green value for each phase are shown in table 3 below:
TABLE 3 15% fractional queue number and minimum maximum green value for each phase
(3) Determining maximum Green
The maximum green is the upper limit of the induction control intersection during maximum green dynamic adjustment. The calculation method is that the sum of the green light time required by all vehicles in the maximum queue of the current control phase to pass through the intersection and the time required by all the vehicles arriving at the intersection subsequently. And meanwhile, the limitation of the waiting time and the period of the maximum red light of the pedestrian is met.
Wherein,
represents the maximum green maximum in s; b represents the maximum number of queued vehicles in the control period, and the unit is a vehicle; b' represents a subsequent arriving vehicleNumber, unit is vehicle; h istThe average headway is expressed in units of s; t is tδThe unit is s, and 90s is taken as the maximum waiting time of the pedestrian red light.
Taking the early peak as an example, the maximum number of vehicles in line for each phase and the time required for the vehicles to pass through the intersection in the subsequent arrival period of the maximum vehicle in line are counted, and the results are shown in table 4 below.
TABLE 4 maximum number of queued vehicles and maximum green value for each phase
(4) Self-learning maximum green adjustment optimization
Taking the initial maximum green as the initial value of the system input in the time interval, continuously operating n cycles, and comparing the actual green lamp operating time g of n cycle phasesiAnd phase initial maximum green
If the maximum green time is less than the initial maximum green time, setting the maximum value of the phase green time in the continuous n periods as a new maximum green time; if equal to the initial maximum green, then increase the current maximum green to
Otherwise the set maximum green is kept constant.
Wherein, giIndicating that the phase actually runs the green light time with the unit of s under the induction control condition;
an initial maximum green, in units of s, representing the current control phase; n represents the number of cycles of continuous operation, can be set according to the actual situation, generally take 3-5; g'maxRepresents the new maximum green after the maximum green learning adjustment, with the unit of s; t represents an increased green time, typically 5 s;
represents the maximum green maximum in s;
represents the minimum maximum green in s.
The self-learning optimization adjustment is carried out on the maximum green of the early peak of the intersection, and the system can adjust the maximum green according to the operation result. If the system is operated as follows, n is selected to be 3, namely, the maximum green is adjusted once every 3 operating periods.
TABLE 5 maximum number of queued vehicles and maximum green value for each phase
(5) Maximum green iterative update
And taking the new maximum green obtained by learning as the maximum green of each phase of the next period, and after n periods of operation, carrying out optimization adjustment on the maximum green to obtain the new maximum green.
As a second aspect of the present invention, there is provided a signalized intersection maximum green setting system, wherein, as shown in fig. 3, the signalized intersection maximum green setting system 10 includes:
a determining module 110, where the determining module 110 is configured to determine an initial maximum green, a minimum maximum green, and a maximum green of each phase of the signalized intersection;
the acquisition module 120 is configured to acquire actual green light operation time of each phase of the signalized intersection;
a self-learning module 130, where the self-learning module 130 is configured to perform self-learning dynamic optimization adjustment on the initial maximum green of each phase according to the initial maximum green of each phase and the actual green light running time to obtain an adjusted new maximum green of each phase;
and the iteration updating module 140, wherein the iteration updating module 140 is configured to repeatedly perform the step of self-learning dynamic optimization adjustment, and perform periodic iteration updating on the adjusted new maximum green.
The signal control intersection maximum green setting system provided by the invention can realize dynamic adjustment of the intersection maximum green according to the traffic flow condition. On one hand, the condition that the green light time is not matched with the traffic flow caused by the fault of the traffic flow detection equipment can be avoided; on the other hand, the signal timing effect can be improved, and the method is suitable for the situation that the green light time requirement of the traffic flow is suddenly changed due to sudden change.
For a specific working process of the system for setting maximum green at a signalized intersection provided by the present invention, reference may be made to the description of the method for setting maximum green at a signalized intersection in the foregoing, and details are not described here.
It will be understood that the above embodiments are merely exemplary embodiments taken to illustrate the principles of the present invention, which is not limited thereto. It will be apparent to those skilled in the art that various modifications and improvements can be made without departing from the spirit and substance of the invention, and these modifications and improvements are also considered to be within the scope of the invention.
Claims (9)
1. A method for setting the maximum green of a signalized intersection is characterized by comprising the following steps:
determining initial maximum green, minimum maximum green and maximum green of each phase of a signal control intersection;
acquiring actual green light running time of each phase of a signal control intersection;
performing self-learning dynamic optimization adjustment on the initial maximum green of each phase according to the initial maximum green of each phase and the actual green light running time to obtain an adjusted new maximum green of each phase;
repeatedly executing the step of self-learning dynamic optimization adjustment, and carrying out periodic iterative update on the adjusted new maximum green;
wherein, the self-learning dynamic optimization adjustment of the initial maximum green of each phase according to the initial maximum green of each phase and the actual green light running time to obtain the adjusted new maximum green of each phase comprises:
taking the initial maximum green of each phase as an initial value of a self-learning system, and continuously operating for n periods;
comparing the magnitude relationship of the n periods of actual operating green time to the initial maximum green for each phase;
if the actual green light running time of n periods is less than the initial maximum green, setting the maximum value of the actual green light running time of the n continuous periods as the new maximum green after adjustment;
if the actual green light running time of n periods is equal to the initial maximum green, increasing the initial maximum green, and selecting the minimum of the increased initial maximum green and the maximum green as the new adjusted maximum green;
if the actual green light operation time of n periods is neither less than the initial maximum green nor equal to the initial maximum green, keeping the initial maximum green unchanged.
2. The method of claim 1, wherein determining an initial maximum green for each phase of a signalized intersection comprises:
counting the traffic flow of the signal control intersection;
dividing signal control intersection control time periods according to the signal control intersection traffic flow;
respectively counting the queuing conditions of the released vehicles in each control time period according to different control time periods;
and setting the initial maximum green of each phase of the signalized intersection according to the queuing condition of each released vehicle in each control time period, wherein the difference value between the initial maximum green and the minimum green is not less than 5 s.
3. The method for setting the maximum green at a signalized intersection according to claim 2, wherein the initial maximum green at each phase of the signalized intersection is set according to the queuing condition of each released vehicle in each control period, and the difference between the initial maximum green and the minimum green is not less than 5s according to a calculation formula:
wherein,
an initial maximum green, in s, representing each phase of the control period; m represents the average number of vehicles in each phase release direction of the control period, and the unit is a vehicle; h istThe average headway is expressed in units of s; gminDenotes minimum green, in units of s; Δ t represents the increased green time in units of s.
4. The method for setting the maximum green at a signalized intersection according to claim 3, wherein the minimum green is set according to pedestrian street crossing and left-turn non-motor vehicle traffic demands, and the value range of the increased green time is between 5s and 10 s.
5. The method for setting maximum green at a signalized intersection according to claim 2, wherein the determining minimum maximum green for each phase of the signalized intersection comprises:
counting the first green light time required for all 15% of vehicles in the 15% position-separated queue at the signalized intersection to pass through the signalized intersection in the current control period;
setting the first green light time as minimum maximum green, wherein the difference value between the minimum maximum green and the minimum green is not less than 3s, and the calculation formula is as follows:
wherein,
represents the minimum maximum green, in units of s; a represents the 15% of the number of vehicles in the position of the queue when the vehicles in the queue are arranged from small to large in the control period, and the unit is a vehicle; h istThe average headway is expressed in units of s; gminRepresents the minimum green, in units of s.
6. The method for setting the maximum green at a signalized intersection according to claim 2, wherein the determining the maximum green at each phase of the signalized intersection comprises:
counting second green light time required for the maximum number of queued vehicles of each phase to pass through the signalized intersection in the current control period;
counting the time of a third green light required by all the following vehicles passing through the signal control intersection;
setting the sum of the second green light time and the third green light time as the maximum green, wherein the maximum green is less than the waiting time of the maximum red light of the pedestrian, and the calculation formula is as follows:
wherein,
represents the maximum green maximum in s; b represents the maximum number of queued vehicles in the control period, and the unit is a vehicle; b' represents the number of subsequent arriving vehicles in units of vehicles; h istThe average headway is expressed in units of s; t is tδAnd represents the maximum waiting time of the pedestrian red light, and the unit is s.
7. The method of claim 6, wherein the pedestrian red maximum waiting time is 90 s.
8. The method for setting the maximum green of the signalized intersection according to claim 1, wherein a value range of n is 3-5, and the green time of the initial maximum rate increase is 5 s.
9. A signalized intersection maximum green setting system for implementing the signalized intersection maximum green setting method according to any one of claims 1 to 8, the signalized intersection maximum green setting system comprising:
the determining module is used for determining initial maximum green, minimum maximum green and maximum green of each phase of the signalized intersection;
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring the actual green light running time of each phase of a signal control intersection;
the self-learning module is used for carrying out self-learning dynamic optimization adjustment on the initial maximum green of each phase according to the initial maximum green of each phase and the actual green light running time to obtain the new adjusted maximum green of each phase;
and the iteration updating module is used for repeatedly executing the step of self-learning dynamic optimization adjustment and periodically updating the adjusted new maximum green in an iteration mode.
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Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB488796A (en) * | 1937-01-13 | 1938-07-13 | Automatic Telephone & Elect | Improvements in or relating to traffic control systems |
| CN1564222A (en) * | 2004-04-02 | 2005-01-12 | 张铃 | Double-period traffic light optimized configuration method having segmental optimization |
| CN1845202A (en) * | 2005-04-08 | 2006-10-11 | 王大海 | Traffic coordination control system between crossings |
| CN101325008A (en) * | 2008-07-25 | 2008-12-17 | 浙江大学 | Dynamic bidirectional green wave band intelligent coordination control method for urban traffic trunk line |
| CN101968930A (en) * | 2010-11-02 | 2011-02-09 | 徐笑晓 | Crossing signal light control method |
| CN102682613A (en) * | 2012-05-14 | 2012-09-19 | 邓湘 | Method for coordinately controlling sub road section green wave induction and distribution |
| JP2014507040A (en) * | 2011-03-03 | 2014-03-20 | ユーリエビッチ マツル，イゴール | Traffic control method and apparatus for executing the traffic control method |
| CN104036646A (en) * | 2014-06-26 | 2014-09-10 | 公安部交通管理科学研究所 | Method for dividing signal-timing periods of intersections |
| CN105206070A (en) * | 2015-08-14 | 2015-12-30 | 公安部交通管理科学研究所 | Real-time road traffic signal coordination optimization control method and control system thereof |
| CN105788302A (en) * | 2016-04-08 | 2016-07-20 | 华北电力大学（保定） | Dual-target-optimization-based dynamic timing method for urban traffic signal lamp |
| CN105957371A (en) * | 2016-06-21 | 2016-09-21 | 同济大学 | Control apparatus, and traffic light control method and system applied thereby |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB1315525A (en) * | 1970-05-14 | 1973-05-02 | Minnesota Mining & Mfg | Traffic signal remote control system |
| US3881169A (en) * | 1973-06-01 | 1975-04-29 | Traffic Control Products Inc | Emergency vehicle traffic controller |
| CN100492435C (en) * | 2007-03-09 | 2009-05-27 | 吉林大学 | Control method for single crossing mixed traffic signal |
| CN102298846B (en) * | 2010-06-25 | 2014-06-25 | 建程科技股份有限公司 | Real-time traffic sign control method of group intersection and method for predicting needed green time |
| CN102034359B (en) * | 2010-12-23 | 2013-03-20 | 中国科学院自动化研究所 | Networked hierarchical bus priority signal coordination control method |
| CN102646338B (en) * | 2012-04-27 | 2014-09-17 | 同济大学 | Priority control method for bus signal based on green light demand |
| CN103810865A (en) * | 2012-11-06 | 2014-05-21 | 上海宝康电子控制工程有限公司 | System and method for controlling artery green wave traffic signal based on Internet of Things |
-
2018
- 2018-03-23 CN CN201810251277.XA patent/CN108648472B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB488796A (en) * | 1937-01-13 | 1938-07-13 | Automatic Telephone & Elect | Improvements in or relating to traffic control systems |
| CN1564222A (en) * | 2004-04-02 | 2005-01-12 | 张铃 | Double-period traffic light optimized configuration method having segmental optimization |
| CN1845202A (en) * | 2005-04-08 | 2006-10-11 | 王大海 | Traffic coordination control system between crossings |
| CN101325008A (en) * | 2008-07-25 | 2008-12-17 | 浙江大学 | Dynamic bidirectional green wave band intelligent coordination control method for urban traffic trunk line |
| CN101968930A (en) * | 2010-11-02 | 2011-02-09 | 徐笑晓 | Crossing signal light control method |
| JP2014507040A (en) * | 2011-03-03 | 2014-03-20 | ユーリエビッチ マツル，イゴール | Traffic control method and apparatus for executing the traffic control method |
| CN102682613A (en) * | 2012-05-14 | 2012-09-19 | 邓湘 | Method for coordinately controlling sub road section green wave induction and distribution |
| CN104036646A (en) * | 2014-06-26 | 2014-09-10 | 公安部交通管理科学研究所 | Method for dividing signal-timing periods of intersections |
| CN105206070A (en) * | 2015-08-14 | 2015-12-30 | 公安部交通管理科学研究所 | Real-time road traffic signal coordination optimization control method and control system thereof |
| CN105788302A (en) * | 2016-04-08 | 2016-07-20 | 华北电力大学（保定） | Dual-target-optimization-based dynamic timing method for urban traffic signal lamp |
| CN105957371A (en) * | 2016-06-21 | 2016-09-21 | 同济大学 | Control apparatus, and traffic light control method and system applied thereby |
Non-Patent Citations (6)
| Title |
|---|
| Modeling and optimizing traffic light settings in road networks;Simone Göttlich et al.;《EL Sevier》;20150331;第55卷(第2015期);全文 * |
| 单点自适应控制策略的时间参数和相位结构研究;徐洪峰 等;《交通与计算机》;20060830;第24卷(第4期);第105-120页 * |
| 城市交叉口信号协调控制方法研究;宋现敏;《中国博士学位论文全文数据库 工程科技Ⅱ辑》;20081115;全文 * |
| 城市交通干线动态双向绿波优化控制模型;曹交交 等;《公路交通科技》;20150915;第32卷(第9期);全文 * |
| 干线绿波交通信号控制方法研究;侯永芳;《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》;20150815;全文 * |
| 机动车相位固定最小绿灯时间计算方法;徐洪峰 等;《公路交通科技》;20080531;第25卷(第5期);全文 * |
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