CN107293133A - A kind of method for controlling traffic signal lights - Google Patents
A kind of method for controlling traffic signal lights Download PDFInfo
- Publication number
- CN107293133A CN107293133A CN201710692895.3A CN201710692895A CN107293133A CN 107293133 A CN107293133 A CN 107293133A CN 201710692895 A CN201710692895 A CN 201710692895A CN 107293133 A CN107293133 A CN 107293133A
- Authority
- CN
- China
- Prior art keywords
- traffic signal
- signal lights
- group ratio
- cycle
- section
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/07—Controlling traffic signals
- G08G1/081—Plural intersections under common control
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
- G08G1/0112—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from the vehicle, e.g. floating car data [FCD]
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
- G08G1/0145—Measuring and analyzing of parameters relative to traffic conditions for specific applications for active traffic flow control
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention discloses a kind of method for controlling traffic signal lights, comprise the following steps：S1, initialization：The green time in all sections is adjusted to setting value；S2, the non-parking group ratio of acquisition：The non-parking group ratio p ' in a cycle and non-parking group ratio p in this cycle on each section is obtained using floating car data；S3, determine each significance of highway segment c：Each significance of highway segment c is determined according to the non-parking group ratio p ' in a upper cycle and non-parking group ratio p in this cycle size and its situation of change；S4, the linear programming method according to setting, the green time of the optimization of each intersection is solved using each significance of highway segment c；S5, optimization circulation：Repeat step S2 to S5, is continuously available the green time of optimization.The present invention can predict that traffic congestion, to mitigate congestion, postpones the generation of congestion so as to change signal lamp in advance, improve the traffic capacity of road.
Description
Technical field
The present invention relates to a kind of method for controlling traffic signal lights, particularly real-time Traffic signal control.
Background technology
Signal control is broadly divided into fixed timing signal control and Adaptive Signal Control.China's major part city makes now
It is the method for fixed timing, but its flexibility and adaptability ratio are relatively low, because the state of traffic is real-time change, Gu
Determine timing method timely and effectively can not make a response to traffic behavior change, the problems such as result in traffic congestion.Adaptive letter
Number control mode can be according to the situation of change of road, and the parameter to signal is adjusted in real time, is that one kind can be in intersection
Magnitude of traffic flow running status control mode signal adaptive in the case of changing.
Adaptive control method has a lot, wherein most notable is that Britain TRRL develops SCOOT systems (Split-
Cycle-Offset Optimization Technique), come into operation in 1979.SCOOT target be reduce delay and
Parking, it is according to real transport need, and it carries out regular to parameters such as Cycle Length, phase perdurabgility, phase differences
Small adjustment reduces delay and parking to reach.SCOOT MC3 are the SCOOT of latest edition, and it has the characteristics of some are new,
Such as it can skip the phase by bus for the purpose of preferential.1970 or so, highway and sea-freight before Sydney, AUS
Service department develops SCATS systems (The Sydney coordinated adaptive traffic system).SCATS's
Target is to maximize traffic flow and analogy saturation (ratio of effective green time and total green time).It is with SCOOT systems
It is more similar, but difference is having levels property of SCATS systems structure without the optimization program of traffic signalization.
Roberson and Betherton develop a kind of use dynamic programming method to optimize the intersection of separation, should
Method is called DYPIC (Dynamic Programmed Intersection).University of Arizona develops RHODES
(Real-Time Hierarchical Optimized Distributed Effective System) system, it is a kind of
Adaptive control system (Mirchandani and Head, 2001) with hierarchical structure, the system uses phase optimization
Control algolithm (Controlled Optimization of Phases, COP), with prediction and control function.Farges et
Al. a kind of PRODYN methods of the Self Adaptive Control based on Dynamic Programming are developed.Yu and Recker develop MDP＆DP
Signal control is used Ma Er by method (Markov Decision Process and Dynamic Programming), this method
Section's husband's decision process is solved to model by the method for Dynamic Programming.Pignataro and Rathi is carried respectively in the eighties
The signal control strategy gone out under congestion status, its specific method is to extend the green time of downstream intersection and hand over upstream is each
Prong green time is adjusted accordingly, and belongs to one kind strategy that control is coordinated in arterial traffic.Hadi et al. enters to TRANSYT-7
Improvement is gone, blocked state can be handled.Distinguishing rule of this method using queue length as traffic behavior, signal timing dial
The queueing message in downstream road section can be taken into full account.In this method, queue length is obtained by simulation program, rather than
Gathered by detection device, it may appear that analog result deviates the situation of true traffic behavior.In the state of supersaturation
Section, Owen and Stallard propose a kind of rule-based (rule-based) Adaptive Signal Control method.This side
Method is that unlike signal lamp distributes different rules, and effect is more satisfactory in single crossing.Lin Zhang et al. propose one
Kind of simulation traffic police directs traffic the method based on fuzzy rule of behavior, and this method alleviates the congested in traffic feelings of single crossing
Condition, but its control thought is still similar to traditional signal timing dial method.Also some researchs have used fuzzy logic control, multistage
The control methods such as broker architecture.
In the case of saturation or supersaturation, existing traffic control system can only accomplish to block the evacuation after occurring.It is existing
Traffic control system largely flow, occupation rate, saturation degree for being provided using induction coil etc. as traffic state data, even if
Queue length is employed as traffic state data, nor detecting what is obtained from reality, but passes through and simulates what is obtained.Cause
This, existing traffic control system can not differentiate the traffic behavior situation of change blocked before occurring, it is impossible to block generation
Preceding this trend of discovery simultaneously changes traffic control scheme to avoid the generation of local stoppages and spread.Here it is existing traffic control
The unpredictable traffic congestion of system processed is so as to the reason for change signal lamp is to mitigate congestion in advance.
The content of the invention
In order to solve the above technical problems, the present invention proposes a kind of method for controlling traffic signal lights, the current energy of road is improved
Power, postpones the generation of congestion.
To reach above-mentioned purpose, method for controlling traffic signal lights of the invention comprises the following steps：S1, initialization：By institute
The green time for having section is adjusted to setting value；S2, the non-parking group ratio of acquisition：Obtained using floating car data one on each section
The non-parking group ratio p ' in the cycle and non-parking group ratio p in this cycle；S3, determine each significance of highway segment c：According to a upper cycle
Non- parking group ratio p ' and the non-parking group ratio p in this cycle size and its situation of change determine each section weight
Spend c；S4, the linear programming method according to setting, during the green light for the optimization that each intersection is solved using each significance of highway segment c
Between；S5, optimization circulation：Repeat step S2 to S5, is continuously available the green time of optimization.
The beneficial effect of the present invention compared with prior art is：The present invention is based on floating car data, with non-parking
Group ratio is traffic behavior Classification Index, green time is adjusted by linear programming method, because it is by the previous cycle
Judged with the non-parking group ratio value of current period and the trend of change, it is possible to predict traffic congestion to carry
The reason for preceding change signal lamp is to mitigate congestion, so as to prevent congestion, postpones the generation of congestion, improves the notification capabilities of road.
Brief description of the drawings
Fig. 1 is city of embodiment of the present invention arterial traffic Signalized control schematic diagram.
Fig. 2 is linear programming timing method flow chart of the embodiment of the present invention.
Embodiment
Below against accompanying drawing and with reference to preferred embodiment the invention will be further described.
The present embodiment is illustrated by taking certain intown major trunk roads as an example to this control method, and the section has 9 roads
Section, 8 intersections are in the east suburb, and west is urban district, and present case simulates 6:00 AM to 10 points of this periods, from suburb
Area is to the traffic circulation in urban district, as shown in figure 1, wherein link is section, DCP is the detector set in simulation software
(note：The present embodiment is tested by the form of emulation, such as following).
When one group of vehicle drives to the main line section, Floating Car can collect the number such as average hourage of these vehicles
According to, and these vehicles are divided into parking group and the non-class of parking group two.It can be calculated by the average hourage data being collected into
Go out the ratio p of non-parking group vehicle, the value is exactly the index of division traffic behavior in the present invention.Pass through the big of non-parking group ratio
Small and its trend for changing over time, can obtain the importance in current each section, the big section of importance will be obtained more
Big green time is poor.The summation of each significance of highway segment and the product of green time difference will be as object function, by every
The adjustment of individual signal lamp green time is optimized, and obtains one group of new green time.The adjustment process carries out one in every five minutes
It is secondary, realize the real-time control of arterial traffic signal lamp.Wherein related notion is described as follows：
Illustrate 1. non-parking group ratio p
In arterial traffic, the vehicle of some straight trips will directly run into green light and leave section (the non-parking vehicle, Non-
stopped vehicles).Other vehicles for forming queue will occupy a part of red time in downstream intersection, wait down
One green light could pass through the section (parking vehicle, Stopped vehicles).The quantity and straight traffic of non-parking group vehicle
The ratio of quantity be non-parking group ratio.If not the ratio of parking vehicle is higher, then show that the section is more unobstructed, if
The ratio of parking vehicle is higher, then shows that the section is more crowded.Non- parking group ratio p value is the average trip by through vehicles
The row time, t was obtained, and average hourage t can be directly collected into by floating car data.Floating vehicle system (Probe Vehicles
System, PVS) data such as hourage, the type of vehicle of vehicle can be provided in real time.Its data acquisition modes are to pass through
Mobile detector is completed, and detectors of these movements are the vehicles for being loaded with Position Fixing Navigation System, in actual use, are gone out
It is most commonly seen Floating Car vehicle to hire a car.
Illustrate the determination of 2. object functions
If it is intended to improving the bus capacity in a certain section, the green time between the two intersections can be increased
Poor (difference of two adjacent intersections, the green time of downstream intersection and the green time of upstream intersection) is due to this hair
Bright target is the generation for delaying to block, it is determined that we will not only consider road current situation when section importance,
It is also contemplated that change situation of the road in the current generation, anticipation and the adjustment shifted to an earlier date to signal can be so made in advance.
Value and its change herein according to the non-parking group ratio in each section can determine that the importance in each section is (specific to use
Simulated annealing, is shown in explanation 3).It is poor that its larger upstream and downstream intersection green time should be given for the high section of importance,
More vehicles can be so passed to.Thus object function is defined as by we：Each significance of highway segment and green time are poor
Product summation.
Illustrate 3. methods that the optimal importance in each section is obtained by simulated annealing
By being used in combination for simulated annealing and this chapter control methods proposed, this example, which is proposed, following obtains section weight
Spend the algorithm of optimal solution：
Parameter setting：Under initial temperature T=70, each temperature T repeat simulation frequency n=10, rate of temperature fall λ=
0.95th, total number of run N=100.θ=(θ1,θ2,...,θ11) for the vectors of 11 dimensions, and θ1＜ θ2＜ ... ＜ θ11, generate new explanation
Method be random generation.Loss function is：
L (θ)=mean (ConjestionTime)
Its algorithm steps is：
Step 0 (initialization)：Set initial temperature T=70, current solution θcurr, by θcurrSubstitute into simulation model, profit
L (θ are calculated with postrun resultcurr)。
Step 1 (candidate solution)：It is random to determine new explanation θnewAnd pass through simulation calculation L (θnew)。
Step 2 (compares loss function value)：If L (θnew) ＜ L (θcurr), then receive θnew.If L (θnew)≥L
(θcurr), determine to receive θ using Metropolis criterionsnewProbability, otherwise keep former solution θcurr。
Step 3 (repeats) under fixed temperature：Step 1 and 2 is repeated before temperature T changes.
Step 4 (cooling)：According to annealing rule reduction temperature, T=α T return to Step 1.It is effectively restrained until reaching
(N=100) algorithm terminates afterwards.
Final result see the table below 2, obtain final result for (3,5,11,12,25,27,39,40,41,44,45).
The simulated annealing table of table 2.
Specific control method is as shown in Fig. 2 wherein simperiod is simulation time, and Ti is section i green time.For
It is easy to verify the effect of this method, we are tested with the method emulated, concretely comprised the following steps：
A. initialize：The green time in all sections is adjusted to the maximum of setting：70s.And substitute into emulation platform and enter
Row is emulated and preheated.Emulation platform uses the micro-simulation simulator VISSIM of the research and development of PTV companies of Germany.
B. each significance of highway segment c is determined.Importance can be according to the non-parking group ratio p in a upper cycle size and its change
Change situation is determined.It is denoted as c.P on last stage is denoted as p '.Importance is set by simulated annealing determining, it is former
Reason is shown in that the concrete outcome of importance in explanation 3, this example is shown in Table 1 with algorithm steps.
Each section importance decision method of table 1
In upper table 1, p is the non-parking group ratio of current period, and p ' is the non-parking group ratio of upper a cycle, and c is
The importance in section.When the non-parking group large percentage of a cycle on some section, the importance in the section is just higher.Certain
The change of the non-parking group ratio of section current period and upper a cycle also influences the importance in section, because considering
The trend of traffic behavior change.The concrete numerical value of importance realizes that specific steps are shown in be said above by simulated annealing
Bright 3.
In table 1 repeatedly with and 5% allow index, its expression：If the p of some section current state is than the p in a upper cycle
Value add more than 5%, illustrate that the current road segment degree of crowding is alleviated significantly, its importance is relatively small；If some
The p of section current state was reduced more than 5% than the p in upper cycle value, illustrated the deterioration of the traffic behavior in this section drastically,
Significance of highway segment is relatively large；Both changes are in increase 5% and reduce between 5%, illustrate that the road section traffic volume state is more steady
It is fixed.
C. linear programming model is set up, each intersection green time is solved.Specially：
Wherein (1) s.t. this be restrictive condition the meaning, Z is the meaning of integer.
Under current period, by formula (1) can in the hope of each intersection green time.It is that intersection is worked as in formula (1)
Preceding green time, is the green time of a cycle on intersection, is the importance in section.Should for the high section of importance
When giving, its larger upstream and downstream intersection green time is poor, can so be passed to more vehicles.Thus we are by mesh
Scalar functions are defined as：Each significance of highway segment and the summation of the product of green time difference.For the green time of each intersection, its
Excursion is 50s-70s, and there are 8 sections centre, and each section green time is within this range.We set every time each
Green light adjustment time is 2s, i.e., it is necessary to which the green time of adjustment can only increase or reduce 2s within each cycle.
Meanwhile, in order to ensure the traffic capacity of road, it is necessary to which the vehicle number that section downstream is rolled away from drives into more than or equal to upstream
Vehicle number.In order to reach this effect, it is necessary to increase flow or reduction section upstream vehicle that section downstream vehicle is rolled away from
The flow driven into, thus the green time of downstream intersection is greater than intersection green time equal to upstream.
D. each intersection green time is substituted into and emulated in analogue system, obtain new non-parking group ratio p, then
New p is updated in step b, the circulation is repeated.
E. when emulation reaches end condition, (be previously set and emulate total time) stops, output emulation the data obtained.
By l-G simulation test, can know the control method can effectively reduce hourage of vehicle, the delay time at stop,
Stop frequency, is shown in Table 3
The linear programming timing method of table 3. and conventional method Comparative result
As can be seen from the above table, non-parking group ratio index, average hourage index, mean delay time index,
In average stop frequency index, performance of the linear programming timing method in section 4 and section 5 will be fixed better than Webster matches somebody with somebody
Shi Fangfa.Linear programming timing method proposed by the present invention effectively raises the traffic capacity of road, has postponed the hair of congestion
It is raw, reach control targe.
In addition, this method collects data using Floating Car, the cost using instruments such as detectors has been saved.China exists
There is Floating Car experimental system in multiple cities such as Beijing, Shenzhen, and the real-time traffic states information that these systems are provided can be use
Family provides the service such as real-time road inquiry, path navigation, it has also become the primary information resource of the business software such as Baidu map.Float
Car system mainly collects the information such as position, time and speed, and its cost is relatively low.The present invention can be directly real using these Floating Cars
The data that check system is provided, are controlled signal in real time.
Above content is to combine specific preferred embodiment further description made for the present invention, it is impossible to assert
The specific implementation of the present invention is confined to these explanations.For those skilled in the art, do not taking off
On the premise of from present inventive concept, some equivalent substitutes or obvious modification can also be made, and performance or purposes are identical, all should
When being considered as belonging to protection scope of the present invention.
Claims (10)
- Comprise the following steps 1. a kind of method for controlling traffic signal lights is characterized in that：S1, initialization：The green time in all sections is adjusted to setting value；S2, the non-parking group ratio of acquisition：The non-parking group ratio p ' in a cycle and sheet on each section are obtained using floating car data The non-parking group ratio p in cycle；S3, determine each significance of highway segment c：According to the non-parking group ratio p ' in a upper cycle and the non-parking group ratio p in this cycle Size and its situation of change determine each significance of highway segment c；S4, the linear programming method according to setting, the green time of the optimization of each intersection is solved using each significance of highway segment c；S5, optimization circulation：Repeat step S2 to S4, is continuously available the green time of optimization.
- 2. method for controlling traffic signal lights according to claim 1, it is characterised in that in step S1, by all sections Green time is adjusted to the maximum of setting.
- 3. method for controlling traffic signal lights according to claim 2, it is characterised in that in step S3, the setting of importance Determined by simulated annealing.
- 4. method for controlling traffic signal lights according to claim 3, it is characterised in that in step S3, when on some section During the non-parking group large percentage of a cycle, the importance in the section is just higher.Certain section current period and upper a cycle Non- parking group ratio change also influence section importance.
- 5. in method for controlling traffic signal lights according to claim 1, step S4, solve the green light of the optimization of each intersection Include following strategy during the time：It is poor that its larger upstream and downstream intersection green time is given for the high section of importance.
- 6. method for controlling traffic signal lights according to claim 4, it is characterised in that in step S4, each intersection it is excellent The green time of change is obtained by solving the method for object function maximum, and object function is：Each significance of highway segment with it is green The summation of the product of lamp time difference.
- 7. method for controlling traffic signal lights according to claim 5, it is characterised in that in step S4, solves each intersection Optimization green time when include following strategy：The green time of downstream intersection is greater than the intersection green light equal to upstream Time.
- 8. method for controlling traffic signal lights according to claim 1, it is characterised in that in step S2, non-parking group ratio p Value be to be obtained by the average hourage t of through vehicles, average hourage t is directly collected into by floating car data.
- 9. method for controlling traffic signal lights according to claim 8, it is characterised in that：Floating vehicle system provides car in real time Hourage data, its data acquisition modes are completed by mobile detector.
- 10. method for controlling traffic signal lights according to claim 9, it is characterised in that：The detector of the movement is to carry There is the vehicle of Position Fixing Navigation System.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710692895.3A CN107293133B (en) | 2017-08-14 | 2017-08-14 | A kind of method for controlling traffic signal lights |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710692895.3A CN107293133B (en) | 2017-08-14 | 2017-08-14 | A kind of method for controlling traffic signal lights |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107293133A true CN107293133A (en) | 2017-10-24 |
| CN107293133B CN107293133B (en) | 2019-09-06 |
Family
ID=60106226
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710692895.3A Active CN107293133B (en) | 2017-08-14 | 2017-08-14 | A kind of method for controlling traffic signal lights |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107293133B (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107895481A (en) * | 2017-11-21 | 2018-04-10 | 福建工程学院 | Area road wagon flow amount control method based on floating car technology |
| CN109191847A (en) * | 2018-10-12 | 2019-01-11 | 山东交通学院 | Adaptive Arterial Coordination Control method and system based on city bayonet data |
| CN109712414A (en) * | 2019-01-30 | 2019-05-03 | 同济大学 | A kind of optimization method of more bandwidth arterial highway public transport control programs |
| CN111554111A (en) * | 2020-04-21 | 2020-08-18 | 河北万方中天科技有限公司 | Signal timing optimization method and device based on multi-source data fusion and terminal |
| CN113421427A (en) * | 2021-08-25 | 2021-09-21 | 深圳市城市交通规划设计研究中心股份有限公司 | Traffic signal coordination control method, device and system based on queuing length |
| CN114267186A (en) * | 2021-12-15 | 2022-04-01 | 曾明德 | Method for adjusting traffic congestion through origin-destination tree |
| CN115188208A (en) * | 2022-07-11 | 2022-10-14 | 福建农业职业技术学院 | Traffic control method based on big data and computer equipment |
| CN117935561A (en) * | 2024-03-20 | 2024-04-26 | 山东万博科技股份有限公司 | Intelligent traffic flow analysis method based on Beidou data |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101256718A (en) * | 2008-01-14 | 2008-09-03 | 潘光华 | City road signal lamp local area network automatic control system |
| US20130013179A1 (en) * | 2011-07-06 | 2013-01-10 | International Business Machines | System and Method for Self-Optimizing Traffic Flow Using Shared Vehicle Information |
| CN102956111A (en) * | 2012-11-05 | 2013-03-06 | 华南理工大学 | Method for coordinating and controlling urban arterial road group |
| US20130138331A1 (en) * | 2011-11-29 | 2013-05-30 | International Business Machines Corporation | Heavy vehicle traffic flow optimization |
| CN103531031A (en) * | 2013-02-06 | 2014-01-22 | 支录奎 | Research of realizing green wave band passing control based on traffic main line soft enclosing area video detection identification |
| CN104077920A (en) * | 2014-07-16 | 2014-10-01 | 成都信息工程学院 | Stand-alone type self-adaptive traffic signal control system and realizing method thereof |
| CN105336183A (en) * | 2015-10-26 | 2016-02-17 | 青岛海信网络科技股份有限公司 | Traffic congestion control method and device based on road section passing capacity |
| CN105608912A (en) * | 2016-01-21 | 2016-05-25 | 湖南拓天节能控制技术股份有限公司 | City road traffic intelligent control method and city road traffic intelligence control system |
| CN105761517A (en) * | 2016-05-18 | 2016-07-13 | 杭州智诚惠通科技有限公司 | Signal light timing method |
| CN106875702A (en) * | 2017-04-11 | 2017-06-20 | 冀嘉澍 | A kind of crossroad access lamp control method based on Internet of Things |
| DE102015122893A1 (en) * | 2015-12-29 | 2017-06-29 | Deutsche Telekom Ag | Method and system for traffic control |
-
2017
- 2017-08-14 CN CN201710692895.3A patent/CN107293133B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101256718A (en) * | 2008-01-14 | 2008-09-03 | 潘光华 | City road signal lamp local area network automatic control system |
| US20130013179A1 (en) * | 2011-07-06 | 2013-01-10 | International Business Machines | System and Method for Self-Optimizing Traffic Flow Using Shared Vehicle Information |
| US20130138331A1 (en) * | 2011-11-29 | 2013-05-30 | International Business Machines Corporation | Heavy vehicle traffic flow optimization |
| CN102956111A (en) * | 2012-11-05 | 2013-03-06 | 华南理工大学 | Method for coordinating and controlling urban arterial road group |
| CN103531031A (en) * | 2013-02-06 | 2014-01-22 | 支录奎 | Research of realizing green wave band passing control based on traffic main line soft enclosing area video detection identification |
| CN104077920A (en) * | 2014-07-16 | 2014-10-01 | 成都信息工程学院 | Stand-alone type self-adaptive traffic signal control system and realizing method thereof |
| CN105336183A (en) * | 2015-10-26 | 2016-02-17 | 青岛海信网络科技股份有限公司 | Traffic congestion control method and device based on road section passing capacity |
| DE102015122893A1 (en) * | 2015-12-29 | 2017-06-29 | Deutsche Telekom Ag | Method and system for traffic control |
| CN105608912A (en) * | 2016-01-21 | 2016-05-25 | 湖南拓天节能控制技术股份有限公司 | City road traffic intelligent control method and city road traffic intelligence control system |
| CN105761517A (en) * | 2016-05-18 | 2016-07-13 | 杭州智诚惠通科技有限公司 | Signal light timing method |
| CN106875702A (en) * | 2017-04-11 | 2017-06-20 | 冀嘉澍 | A kind of crossroad access lamp control method based on Internet of Things |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107895481A (en) * | 2017-11-21 | 2018-04-10 | 福建工程学院 | Area road wagon flow amount control method based on floating car technology |
| CN109191847A (en) * | 2018-10-12 | 2019-01-11 | 山东交通学院 | Adaptive Arterial Coordination Control method and system based on city bayonet data |
| CN109191847B (en) * | 2018-10-12 | 2021-01-26 | 山东交通学院 | Self-adaptive trunk line coordination control method and system based on city gate data |
| CN109712414A (en) * | 2019-01-30 | 2019-05-03 | 同济大学 | A kind of optimization method of more bandwidth arterial highway public transport control programs |
| CN109712414B (en) * | 2019-01-30 | 2021-09-03 | 同济大学 | Optimization method of multi-bandwidth trunk road bus control scheme |
| CN111554111A (en) * | 2020-04-21 | 2020-08-18 | 河北万方中天科技有限公司 | Signal timing optimization method and device based on multi-source data fusion and terminal |
| CN111554111B (en) * | 2020-04-21 | 2021-04-20 | 河北万方中天科技有限公司 | Signal timing optimization method and device based on multi-source data fusion and terminal |
| CN113421427A (en) * | 2021-08-25 | 2021-09-21 | 深圳市城市交通规划设计研究中心股份有限公司 | Traffic signal coordination control method, device and system based on queuing length |
| CN114267186A (en) * | 2021-12-15 | 2022-04-01 | 曾明德 | Method for adjusting traffic congestion through origin-destination tree |
| CN115188208A (en) * | 2022-07-11 | 2022-10-14 | 福建农业职业技术学院 | Traffic control method based on big data and computer equipment |
| CN117935561A (en) * | 2024-03-20 | 2024-04-26 | 山东万博科技股份有限公司 | Intelligent traffic flow analysis method based on Beidou data |
| CN117935561B (en) * | 2024-03-20 | 2024-05-31 | 山东万博科技股份有限公司 | Intelligent traffic flow analysis method based on Beidou data |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107293133B (en) | 2019-09-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107293133A (en) | A kind of method for controlling traffic signal lights | |
| Ma et al. | Development and evaluation of a coordinated and conditional bus priority approach | |
| CN106960584B (en) | A kind of traffic control method and device of adaptive crossroad traffic signal lamp | |
| CN104157139B (en) | A kind of traffic congestion Forecasting Methodology and method for visualizing | |
| Mahler et al. | Reducing idling at red lights based on probabilistic prediction of traffic signal timings | |
| CN111899534A (en) | Traffic light intelligent control method based on road real-time capacity | |
| Fouladvand et al. | Optimized traffic flow at a single intersection: traffic responsive signalization | |
| CN101894477A (en) | Self-locking control method for controlling road network traffic through urban signal lamps | |
| Zhang et al. | Optimal signal timing method of intersections based on bus priority | |
| CN104916142A (en) | Adaptive intersection traffic signal control method of trunk road priority | |
| CN106991542B (en) | Method for identifying congestion bottleneck of track network based on seepage theory | |
| CN113158424B (en) | Model construction method and device for mine unmanned road network traffic flow optimization | |
| Conrad et al. | Real-time traffic signal optimization with transit priority: Recent advances in the signal priority procedure for optimization in real-time model | |
| Bayrak et al. | Optimizing bus lane placement on networks while accounting for queue spillbacks | |
| Li et al. | A perimeter control strategy for oversaturated network preventing queue spillback | |
| CN113408189B (en) | Urban multipoint circulating emergency evacuation and simulation deduction method based on variable cells | |
| Bakibillah et al. | Sustainable eco-driving strategy at signalized intersections from driving data | |
| CN105741585A (en) | Fuel-economizing-oriented vehicle track smoothing control method based on Internet-of-vehicles | |
| Ding et al. | Segment travel time route guidance strategy in advanced traveler information systems | |
| Gao et al. | Bus Priority Control for Dynamic Exclusive Bus Lane. | |
| De Coensel et al. | Traffic signal coordination: a measure to reduce the environmental impact of urban road traffic? | |
| CN113628455B (en) | Intersection signal optimization control method considering number of people in vehicle under Internet of vehicles environment | |
| WO2023065057A1 (en) | Method based on intelligent traffic signal control architecture | |
| CN115472023A (en) | Intelligent traffic light control method and device based on deep reinforcement learning | |
| CN108053658A (en) | A kind of through street Ramp control method for coordinating of crowded full chain management |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |