CN103559795B - Multi-strategy and multi-object self-adaptation traffic control method - Google Patents
Multi-strategy and multi-object self-adaptation traffic control method Download PDFInfo
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Abstract
The invention discloses a multi-strategy and multi-object self-adaptation traffic control method. The multi-strategy and multi-object self-adaptation traffic control method is characterized by comprising the following steps that (1), traffic state inspection equipment collects traffic flow data in real time, obtains the traffic state information and sends the traffic state information to an intersection signal machine, and the intersection signal machine uploads the traffic flow data to a signal center control system; (2), the intersection signal machine calculates the traffic intensity of an intersection, judges the traffic states of the intersection and adjusts the control plan of the current intersection in real time according to the different traffic states; (3), according to the intersection traffic flow data uploaded in real time, the signal center control system optimizes the intersection signal control scheme from the global angle of a road network. According to the control method, the road network grade global optimization self-adaptation signal control method is provided through judgment and analysis of the traffic states of the road network, and artery coordination control applicable to any traffic state is achieved.
Description
Technical field
The invention belongs to traffic automatic control technology field, specifically, relate to the how tactful multiobject self-adaption traffic control method of one and system.
Background technology
Along with constantly riseing of city automobile recoverable amount, cause transport need amount to increase severely, but the transportation supplies that the road network that city reaches its maturity can provide is limited, imbalance of supply and demand, one of inducement becoming urban traffic blocking; Traffic jam issue mainly concentrates on the node (grade crossing) of road network, forces and it should be noted that backward traffic control technology is also the main inducing of traffic problems.Development along with electronics, information and control technology and the widespread use in transportation industry, people are constantly making great efforts the intelligentized traffic control technology of seeking by advanced person and are improving traffic congestion.Wherein, the research and apply of adaptive traffic signal control system is then one of intelligentized important achievement of traffic control.Adaptive traffic signal control system mainly comprises four unit: intersection signal control unit, highway traffic data detection system, remote center's control module, communication unit.According to range of control, single-point Adaptive Signal Control can be divided into, main line Adaptive Signal Control, the Adaptive Signal Control of regional coordination.For single crossing single-point adaptive traffic signal control system, crossing checkout equipment is utilized to obtain the traffic behavior parameter at crossing, at crossing control module or the signal timing plan at the automatic calculation optimization crossing of remote center, and download to teleseme execution, best for target with the control effects of single intersection; For the Trunk Road Coordination Adaptive Signal Control at multiple crossing, while optimization of road joints scheme, upgrade the coordination phase differential of Multiple Intersections, control as target to realize the green ripple of main line; The regional coordination adaptive control of facing area is multiple traffic subareas Region dividing, in subarea and sub-range carry out cooperation control, best for target with the control effects of road network entirety in region.
Mainly there is following problem in existing self-adaptation control method: the adaptability 1) for traffic behavior change is poor, lacks the detection method of the peak period congestion status that can be applicable to traffic signalization; The green ripple of single-point adaptive control, main line controls, can not the demand for control of adaptive peak phase congestion status.2) that road network is isolated in units of point, line is optimized control, and effect of optimization is attended to one thing and lose sight of another, and is absorbed in the predicament of local optimum, causes the global optimization effect of road network poor.3) for modern city road network structure, traffic behavior, feature complicated and changeable, lacks clear and definite control objectives, the control strategy of system.
Based on this, how to invent the how tactful multiobject self-adaption traffic control method of one and system, go for the system optimization method of the Arterial Coordination Control of any traffic behavior, the method introduces key parameter (namely initial green light is queued up) the modified basis phase differential affecting Trunk Road Coordination effect, with the corresponding relation of traffic behavior and control objectives for foundation, providing the control method for coordinating that blocks up of main line, is the technical matters that the present invention mainly solves.
Summary of the invention
The present invention's problem that the real-time change adaptability of existing traffic control method to traffic behavior is poor in order to solve, prioritization scheme can not be optimized from the overall situation for whole road network structure, provide the how tactful multiobject adaptive traffic signal control system of one, by to the differentiation of road network (comprising crossing, section, main line, region) traffic behavior and analysis, determine the control objectives corresponding to different traffic; Set up changing the mechanism based on the road network level of traffic state judging, systematized control strategy and control mode, a kind of global optimization Adaptive Signal Control method of road network level is provided.
In order to solve the problems of the technologies described above, the present invention is achieved by the following technical solutions:
The how tactful multiobject self-adaption traffic control method of one, comprises the following steps:
(1), traffic condition detection equipment Real-time Collection traffic flow data obtains traffic state information, and transmits it to intersection annunciator, by intersection annunciator, traffic flow data is uploaded to signal center's control system;
(2), intersection annunciator calculates crossing traffic intensity according to the traffic flow data obtained, with this traffic intensity value for condition discrimination index, judge the traffic behavior at crossing, and according to different traffic behavior auto-changeover control targets, the control program at the current crossing of adjustment in real time;
(3), crossing traffic intensity is uploaded to signal center's control system by intersection annunciator simultaneously, signal center's control system judges to obtain road section traffic volume state, arterial traffic state and regional traffic state information according to crossing traffic flow data, crossing traffic intensity, according to above-mentioned information auto-changeover control target, generate real-time crossing traffic signal timing plan, and the control command of this control program is transferred to teleseme, the control program that the control program priority that described intersection annunciator generates generates lower than signal center's control system.
Further, divided lane flow, time headway, the time occupancy traffic flow data at entrance detector acquisition crossing in step (1), and transfer to intersection annunciator, outlet detector acquisition section queue length information, and transfer to intersection annunciator.Traffic flow data based on crossing entrance, outlet detects, from crossing, section, main line, region various dimensions complete perception road network traffic behavior.Wherein, traffic state at road cross differentiates it is the basis that road net traffic state differentiates, using crossing, section as the elementary cell obtaining road grid traffic flow data, adopt traffic intensity parameter as condition discrimination index; Each traffic intensity value, characterizes the traffic behavior that crossing is unique.
Further, in described step (2), if the queue length in section reaches maximum queue length L, then sentencing this section is congestion status, otherwise this section is unimpeded state.
Further, in described step (2), when the positive and negative two-way green light initial queue length in section of main line is all less than maximum queue length L, this main line is unimpeded state, otherwise this main line is congestion status.
Further, in described step (2), when the traffic intensities presetting key crossing all in region all reaches respective
position traffic intensity time, judge that this region is as congestion status, otherwise this region is non-congestion status,
The
the traffic intensity of position: the crossing traffic intensity of upper one week always sort the
bit data.
In described step (2), the traffic behavior at crossing comprises: unimpeded, slight crowding, crowded and serious crowded four kinds of states, when crossing is in unimpeded, single spot optimization is taked to control or induction control, also namely system changes according to real-time traffic intensity, the control cycle at calculation optimization crossing, optimal control in dynamic scheme.
Further, in described step (2), when crossing be in non-unimpeded time, if this place, crossing downstream road section is congestion status, then current crossing performs bottleneck control, and continue the congestion status judging section, place, until when section, place is in non-congestion status, perform single-point optimal control or induction control.
Further, in described step (2), when crossing is in non-unimpeded, and when this place, crossing downstream road section is non-congestion status, continue to judge whether this crossing belongs to arterial control crossing, if belong to arterial control crossing, then judge whether this main line is in congestion status, if congestion status, then main line all crossings executive system optimization-Multiple Intersections blocks up coordinated signals, if non-congestion status, main line all crossings executive system optimization-Philodendron ‘ Emerald Queen'.
Further, in described step (2), if crossing does not belong to arterial control crossing, then continue to judge whether this crossing belongs to Region control crossing, if belong to Region control crossing and this region is congestion status, then the green time of constricted zone borderline control crossing control phase, carries out Current limited Control.
Further, in described step (2), described bottleneck control method is: that ends this current crossing enters downstream road section phase place green light.
Compared with prior art, advantage of the present invention and good effect are: the how tactful multiobject Adaptive Signal Control method of one of the present invention, by to the differentiation of road network (crossing, section, main line, region) traffic behavior and analysis, determine the control objectives corresponding to different traffic; Set up changing the mechanism based on the road network level of traffic state judging, systematized control strategy and control mode, a kind of global optimization Adaptive Signal Control method of road network level is provided; Be applicable to the Arterial Coordination Control of any traffic behavior, the method introduces key parameter (namely initial green light is queued up) the modified basis phase differential affecting Trunk Road Coordination effect, with the corresponding relation of traffic behavior and control objectives for foundation, provide the control method for coordinating that blocks up of main line.
After reading the detailed description of embodiment of the present invention by reference to the accompanying drawings, the other features and advantages of the invention will become clearly.
Accompanying drawing explanation
Fig. 1 is a kind of embodiment process flow diagram of the multiobject self-adaption traffic control method of many strategies proposed by the invention;
Fig. 2 is bottleneck control flow chart in section in Fig. 1;
Tu3Shi Tu1 Road mouthfeel answers control flow chart;
Fig. 4 is single spot optimization control flow chart in crossing in Fig. 1;
Fig. 5 is arterial control system optimization process flow diagram in Fig. 1.
Embodiment
Below in conjunction with accompanying drawing, the specific embodiment of the present invention is described in more detail.
Embodiment one, present embodiments provides the how tactful multiobject self-adaption traffic control method of one, comprises the following steps:
(1), traffic condition detection equipment Real-time Collection traffic flow data obtains traffic state information, and transmits it to intersection annunciator, by intersection annunciator, traffic flow data is uploaded to signal center's control system;
(2), intersection annunciator calculates crossing traffic intensity according to the traffic flow data obtained, with this traffic intensity value for condition discrimination index, judge the traffic behavior at crossing, and according to different traffic behavior auto-changeover control targets, the control program at the current crossing of adjustment in real time;
(3), crossing traffic intensity is uploaded to signal center's control system by intersection annunciator simultaneously, signal center's control system judges to obtain road section traffic volume state, arterial traffic state and regional traffic state information according to crossing traffic flow data, crossing traffic intensity, according to above-mentioned information auto-changeover control target, generate real-time crossing traffic signal timing plan, and the control command of this control program is transferred to teleseme, the control program that the control program priority that described intersection annunciator generates generates lower than signal center's control system.
Wherein, the computing method of crossing traffic intensity can adopt existing computing method, the object of the invention is to the traffic behavior considering section, main line, region, when section, main line, region are in congestion status, then need implement to block up control for its feature separately, therefore, for the traffic behavior in section, main line, region, only do the differentiation of non-congestion status and congestion status.Based on above-mentioned steps, the method of the present embodiment achieves including crossing, section, main line, the detection of dynamic of multidimensional traffic behavior of Regional Road Network and differentiation, by to the traffic condition detection of road network various dimensions and analysis, preferred control objectives systematically, switching at runtime control strategy and control mode, realize the overall adaptive congestion control algorithm in whole road network (crossing, section, main line, region).
Wherein, the system major equipment realizing this method consists of: signal center's control system, is generally laid in traffic control center; The signal controller at crossing, hereinafter referred to as teleseme, is laid in the crossing needing signal to control; Traffic condition detection equipment, is divided into entrance detecting device and outlet detecting device two kinds, is generally laid on crossing import, exit lane.
As a preferred embodiment, divided lane flow, time headway, the time occupancy traffic flow data at entrance detector acquisition crossing in step (1), and transfer to intersection annunciator, outlet detector acquisition section queue length information, and transferring to intersection annunciator, above-mentioned information is uploaded to signal center's control system by intersection annunciator.Wherein, conventional entrance detecting device comprises, ring coil detector, geomagnetism detecting device, video detector, ultrasonic detector etc.Outlet detecting device can adopt overflow detector to realize, specific implementation is: the mode obtaining section queue length information is specially: in section, far from crossing, upstream, in the middle of 0 ~ 100 meter of position, one group of (2) overflow detector is laid in a track, for detection time occupation rate, flow, velocity information: according to outlet detecting device rolling time occupation rate data, judge whether section reaches maximum queue length.Traffic flow data based on crossing entrance, outlet detects, from crossing, section, main line, region various dimensions complete perception road network traffic behavior.Wherein, traffic state at road cross differentiates it is the basis that road net traffic state differentiates, using crossing, section as the elementary cell obtaining road grid traffic flow data, adopt traffic intensity parameter as condition discrimination index; Each traffic intensity value, characterizes the traffic behavior that crossing is unique.
In the step (2) of the present embodiment, detect queue length by overflow detector, if the queue length in section reaches maximum queue length L, then sentencing this section is congestion status, otherwise this section is unimpeded state, other detection meanss can certainly be adopted to detect queue length.
In addition, in the step (2) of the present embodiment, the traffic behavior determination methods of main line is: when the positive and negative two-way green light initial queue length in section of main line is all less than maximum queue length L, this main line is unimpeded state, otherwise this main line is congestion status.
And in the step (2) of the present embodiment, the traffic behavior determination methods in region is: when the traffic intensities presetting key crossing all in region all reaches respective
position traffic intensity time, judge that this region is as congestion status, otherwise this region is non-congestion status,
The
the traffic intensity of position: the bit data that the crossing traffic intensity of upper one week always sorts.
In described step (2), when crossing is in unimpeded (being also non-congestion status), take single spot optimization to control or induction control, also namely system changes according to real-time traffic intensity, the control cycle at calculation optimization crossing, optimal control in dynamic scheme.The control objectives of its setting is that random vehicles delay is minimum, vehicles average delay is minimum and traffic efficiency is maximum, unimpededly comprise again unimpeded state, slight congestion status and congestion status, respectively with above-mentioned three kinds of control objectives one_to_one corresponding, different according to control objectives, system finally determines optimum control scheme, and also namely single spot optimization controls or induction control, and above-mentioned two kinds of control method prior aries solve, its process flow diagram, see shown in Fig. 3, Fig. 4, does not repeat them here.
Shown in Figure 1, in described step (2), when crossing be in non-unimpeded time, if this place, crossing downstream road section is congestion status, then current crossing performs bottleneck control, and continue the congestion status judging section, place, until when section, place is in non-congestion status, perform single-point optimal control or induction control.The control object that current crossing performs bottleneck control is to prevent from queuing up to overflow, and reaches the technique effect regulating this road section traffic volume state when blocking up appears in this section.Preferably, in described step (2), described bottleneck control method is: that ends this current crossing enters downstream road section phase place green light.Shown in Figure 2, end this current crossing enter downstream road section phase place green light after, also need timing to judge whether block status is removed, if so, then recover normal control, otherwise, continue to perform section bottleneck control.
In described step (2), when crossing is in non-unimpeded, and this place, crossing downstream road section is when being non-congestion status, continue to judge whether this crossing belongs to arterial control crossing, if belong to arterial control crossing, then judge whether this main line is in congestion status, if the words of congestion status, then main line all crossings executive system optimization-Multiple Intersections blocks up coordinated signals, shown in Figure 5, also namely: when main line is congestion status, queue up to prevent section and overflow, balanced each section queue length is control objectives, the coordination control strategy that blocks up of main line is performed by the mode of system optimization.If non-congestion status, its control objectives is for reducing main line stop frequency, and main line all crossings executive system optimization-Philodendron ‘ Emerald Queen', performs the green ripple control strategy of main line by system optimization mode.
In described step (2), if crossing does not belong to arterial control crossing, then continue to judge whether this crossing belongs to Region control crossing, if belong to Region control crossing and this region is congestion status, then the green time of constricted zone borderline control crossing control phase, carries out Current limited Control.Under the congestion status of region, block up to alleviate region, prevent from queuing up and overflow for target, by the region transport need of demand modeling policy control, equilibrium traffic supplies.Under the non-congestion status in region, Region control is subordinated to crossing, section, arterial control.
In addition, the dynamical state also comprising the road network overall situation differentiates and Optimization Steps, and also namely road network self-adaptation divides, and its method is: during road grid traffic controls, control object based on crossing; With the optimal control unit that traffic subarea is basic.According to traffic condition detection result, merging is carried out to traffic subarea and or splits adjustment.Need according to actual site inspection result, initial division is carried out to the optimization subarea of road network, region.Subarea initial division, divides cooperation control subarea according to category of roads, major flow trend etc., performs cooperation control in subarea; Region initial division: the nucleus some easily blocked up or the region needing tune to flow divide strategic control district into, by the split time at crossing, border, control area, alleviates the congestion level in region, makes to keep certain unobstructed degree of driving in region.
4) many policy control mode self-adaptation conversion
Based on the road network status information of traffic condition detection module perception, system adjusts control objectives automatically, preferred control strategy, optimization control scheme.Many tactful multi objective control mode self-adaptations are changed the mechanism.
According to preferred control strategy and control mode, optimization control scheme is also issued to teleseme execution.
Crossing control program generates and performs
According to the crossing traffic intensity of dynamic calculation, be the cycle of interval optimization control scheme, each phase place green time with 5 minutes, perform single-point optimal control; When traffic state at road cross is in free zone, also can senses that the arrival situation of vehicle is real-time according to real-time entrance detecting device, perform induction and control.Shown in Figure 3, induction control method, has been open ripe control program, has not repeated.Shown in Figure 4, single spot optimization control method, is applying for a patent, is not repeating at this.Below its green light distribution principle is only briefly introduced.
The split allocation strategy of single-point intersection is mainly according to waiting principle of traffic intensity to distribute.Refer to that the distribution of crossing phase place green time is carried out according to the phase place traffic intensity calculated Deng traffic intensity principle.
The control method of the present embodiment, by the differentiation of road network (crossing, section, main line, region) traffic behavior and analysis, determines the control objectives corresponding to different traffic, is that road network signal control strategy is formulated, provides solid reference; Establish based on the control strategy of the road network system of traffic state judging and changing the mechanism of control mode, the method breaches the local optimum drawback that Adaptive Signal Control can only be unit with point (crossing), line (main line), provides a kind of global optimization Adaptive Signal Control method of road network level; Simultaneously, this motion have studied a kind of system optimization method being applicable to the Arterial Coordination Control of any traffic behavior, the method introduces key factor (namely initial green light is queued up) the modified basis phase differential affecting Trunk Road Coordination effect, greatly improve the adaptability that cooperation control changes for traffic behavior, with the corresponding relation of traffic behavior and control objectives for foundation, the breakthrough control method for coordinating that blocks up putting forward main line.
Adaptive traffic signal control system in the present embodiment, by improving the intelligent level of traffic signalization, effectively reducing the complexity of urban traffic signal management and control, thus decreasing the human and material resources resource input of urban transportation management and control; The dynamical state of the road network overall situation differentiates and optimizes, and improves the active prevention and control ability that control system is blocked up to road network, for the steady unimpeded operation of urban transportation provides powerful guarantee.
Certainly; above-mentioned explanation is not limitation of the present invention; the present invention is also not limited in above-mentioned citing, the change that those skilled in the art make in essential scope of the present invention, remodeling, interpolation or replacement, also should belong to protection scope of the present invention.
Claims (9)
1. a tactful multiobject self-adaption traffic control method more than, is characterized in that, comprise the following steps:
(1), traffic condition detection equipment Real-time Collection traffic flow data obtains traffic state information, and transmits it to intersection annunciator, by intersection annunciator, traffic flow data is uploaded to signal center's control system;
(2), intersection annunciator calculates crossing traffic intensity according to the traffic flow data obtained, with this traffic intensity value for condition discrimination index, judge the traffic behavior at crossing, and according to different traffic behavior auto-changeover control targets, the control program at the current crossing of adjustment in real time; In described step (2), the traffic behavior at crossing comprises: unimpeded, slight crowding, crowded and serious crowded four kinds of states, when crossing is in unimpeded, single spot optimization is taked to control or induction control, also namely system changes according to real-time traffic intensity, the control cycle at calculation optimization crossing, optimal control in dynamic scheme;
(3), crossing traffic intensity is uploaded to signal center's control system by intersection annunciator simultaneously, signal center's control system judges to obtain road section traffic volume state, arterial traffic state and regional traffic state information according to crossing traffic flow data, crossing traffic intensity, according to above-mentioned information auto-changeover control target, generate real-time crossing traffic signal timing plan, and the control command of this control program is transferred to teleseme, the control program that the control program priority that described intersection annunciator generates generates lower than signal center's control system.
2. according to the multiobject self-adaption traffic control method of many strategies described in claim 1, it is characterized in that, divided lane flow, time headway, the time occupancy traffic flow data at entrance detector acquisition crossing in step (1), and transfer to intersection annunciator, outlet detector acquisition section queue length information, and transfer to intersection annunciator.
3. according to the multiobject self-adaption traffic control method of many strategies described in claim 2, it is characterized in that, in described step (2), if the queue length in section reaches maximum queue length L, then sentencing this section is congestion status, otherwise this section is unimpeded state.
4. according to the multiobject self-adaption traffic control method of many strategies described in claim 3, it is characterized in that, in described step (3), when the positive and negative two-way green light initial queue length in section of main line is all less than maximum queue length L, this main line is unimpeded state, otherwise this main line is congestion status.
5. according to the multiobject self-adaption traffic control method of many strategies described in claim 4, it is characterized in that, in described step (3), when the traffic intensities presetting key crossing all in region all reaches respective
during the traffic intensity of position, judge that this region is as congestion status, otherwise this region is non-congestion status, the
the traffic intensity of position: the crossing traffic intensity of upper one week always sort the
bit data.
6. according to the multiobject self-adaption traffic control method of many strategies described in claim 1, it is characterized in that, in described step (2), when crossing is in non-unimpeded state, if this place, crossing downstream road section is congestion status, then current crossing performs bottleneck control, and continues the congestion status judging section, place, until when section, place is in unimpeded state, perform single-point optimal control or induction control.
7. according to the multiobject self-adaption traffic control method of many strategies described in claim 6, it is characterized in that, in described step (2), when crossing is in non-unimpeded state, and this place, crossing downstream road section is when being non-congestion status, continue to judge whether this crossing belongs to arterial control crossing, if belong to arterial control crossing, then judge whether this main line is in congestion status, if the words of congestion status, then main line all crossings executive system is optimized Multiple Intersections and to be blocked up coordinated signals, if the words of non-congestion status, main line all crossings executive system optimization-Philodendron ‘ Emerald Queen'.
8. according to the multiobject self-adaption traffic control method of many strategies described in claim 6, it is characterized in that, in described step (2), if crossing does not belong to arterial control crossing, then continue to judge whether this crossing belongs to Region control crossing, if belong to Region control crossing and this region is congestion status, then the green time of constricted zone borderline control crossing control phase, carries out Current limited Control.
9. according to claim 6 how tactful multiobject self-adaption traffic control method, is characterized in that, in described step (2), described bottleneck control method is: that ends this current crossing enters downstream road section phase place green light.
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Families Citing this family (34)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104318790B (en) * | 2014-10-20 | 2016-06-15 | 青岛海信网络科技股份有限公司 | A kind of traffic light collocation method and device |
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| CN112418528B (en) * | 2020-11-24 | 2021-09-14 | 哈尔滨理工大学 | Amphibious vehicle layout area utilization maximization method based on multi-strategy dynamic adjustment |
| CN113689698B (en) * | 2021-08-24 | 2024-05-07 | 阿波罗智联(北京)科技有限公司 | Traffic control method, apparatus, electronic device, storage medium, and program product |
| CN114299736A (en) * | 2021-12-22 | 2022-04-08 | 安徽建德基文化传媒有限公司 | Multi-intersection traffic self-adaptive coordination control method |
| CN114038218A (en) * | 2021-12-28 | 2022-02-11 | 江苏泰坦智慧科技有限公司 | Chained feedback multi-intersection signal lamp decision system and method based on road condition information |
| CN114613157B (en) * | 2022-02-09 | 2023-05-26 | 阿里云计算有限公司 | Traffic control method, system and equipment |
| CN114613162B (en) * | 2022-02-09 | 2024-01-19 | 阿里云计算有限公司 | Traffic control method, system and equipment |
| CN114613158B (en) * | 2022-02-09 | 2023-09-22 | 阿里云计算有限公司 | Traffic control method, system and equipment |
| CN116311990B (en) * | 2023-03-27 | 2023-12-22 | 南京莱斯信息技术股份有限公司 | Signal control method based on fusion of Internet data and detection data |
| CN116229738A (en) * | 2023-05-09 | 2023-06-06 | 安徽星云互联科技有限责任公司 | Intelligent networking region coordination control method, device and equipment based on vehicle-road coordination |
| CN116758767B (en) * | 2023-08-21 | 2023-10-20 | 中南大学 | Traffic signal lamp control method based on multi-strategy reinforcement learning |
Family Cites Families (5)
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| CN100592351C (en) * | 2006-08-10 | 2010-02-24 | 深圳市哈工大交通电子技术有限公司 | A traffic signal lamp control method and traffic signal lamp system |
| CN201667131U (en) * | 2010-01-19 | 2010-12-08 | 青岛海信网络科技股份有限公司 | Signal machine supporting wireless communication and wireless control system based on signal machine |
| CN101908279A (en) * | 2010-07-08 | 2010-12-08 | 上海交大高新技术股份有限公司 | Urban traffic adaptive control system |
| CN102436752A (en) * | 2011-09-29 | 2012-05-02 | 青岛海信网络科技股份有限公司 | Prior control system and method for road signals |
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