CN105390000A - Traffic signal control system and method based on road condition traffic big data - Google Patents
Traffic signal control system and method based on road condition traffic big data Download PDFInfo
- Publication number
- CN105390000A CN105390000A CN201510973461.1A CN201510973461A CN105390000A CN 105390000 A CN105390000 A CN 105390000A CN 201510973461 A CN201510973461 A CN 201510973461A CN 105390000 A CN105390000 A CN 105390000A
- Authority
- CN
- China
- Prior art keywords
- signal
- data
- traffic
- time
- scheme
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 18
- 238000005457 optimization Methods 0.000 claims abstract description 35
- 238000000819 phase cycle Methods 0.000 claims abstract description 27
- 230000003068 static effect Effects 0.000 claims abstract description 13
- 238000012545 processing Methods 0.000 claims abstract description 10
- 238000013500 data storage Methods 0.000 claims description 14
- 238000007405 data analysis Methods 0.000 claims description 9
- 230000000737 periodic effect Effects 0.000 claims description 3
- 238000010276 construction Methods 0.000 description 4
- 238000011161 development Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 206010063385 Intellectualisation Diseases 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000006698 induction Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000006855 networking Effects 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/07—Controlling traffic signals
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention provides a traffic signal control system and method based on road condition traffic big data. The method comprises the following steps: a client of a vehicle sending client information to a cloud platform; carrying out massive traffic data information cloud storage; dividing the data into real-time data and historical data according to time sequence; establishing a corresponding static database and a dynamic database according to the type of the data; generating a corresponding optimization control scheme and a coordination control scheme; determining road conditions of a control area according to the schemes, and setting corresponding configuration schemes; selecting the corresponding optimization control scheme; adjusting signal period, signal phase sequence and relative phase difference; and driving a signal lamp to execute an instruction transmitted based on the client. By utilizing the cloud storage and based on big data processing, trend is obtained, and an optimization scheme is made according to change rules; the optimal operation phase of the traffic signals is controlled, so that people are allowed to go out conveniently; and by comparing the collected real-time data with the historical data, the coordination control scheme is generated, so that the operation phase sequence, time (period) and phase sequence of the signal lamps can be adjusted in time, and emergency traffic conditions can be coped with.
Description
Technical Field
The invention relates to the technical field of traffic signal control, in particular to a traffic signal control system and method based on road condition traffic big data.
Background
With the continuous development and progress of national economic construction, the number of transportation vehicles is increasing day by day, and the pressure on urban traffic is getting heavier and heavier. In order to relieve traffic pressure and effectively dredge vehicles on each road, the running of the vehicles is dredged and controlled in a mode of installing traffic signal lamps at a crossroad; the traditional signal control method has the defects that a large number of detectors need to be arranged at intersections or road sections, the cost is high, and meanwhile, the damage of coil detectors and the like to the road surface is serious. In addition, once the coils are arranged, the coils cannot be adjusted, and the queuing length of the intersection cannot be accurately detected. Furthermore, the control of the signal by the intersection or road section detector can only be the induction or self-adaptive control of a single intersection, and the area coordination control cannot be realized.
By adopting the internet traffic big data, the real-time dynamic road condition information is provided, the road condition prediction, the historical road condition query and the congestion bottleneck analysis can be realized, the data in a shorter time can be provided, and the accuracy of the traffic control is greatly improved. With the advance of new energy automobiles, smart city construction and car networking, the development space of traffic big data is further expanded, and the precision and the accuracy of the data are improved.
Therefore, the traffic signal control system and method based on the road condition traffic big data are designed, so that the road traffic control is accurately and efficiently carried out by utilizing the big data processing technology, and the intellectualization and the efficiency of traffic guidance are improved.
Disclosure of Invention
The invention aims to solve the problem that traffic information is monitored and controlled in real time by adopting a big data processing and analyzing technology, so that traffic pressure is relieved, and the problem of waste of road dredging resources is solved.
In order to solve the technical problems, the invention adopts the technical scheme that: a traffic signal control method based on road condition traffic big data is characterized by comprising the following steps:
a client on the vehicle sends client information to the cloud platform;
the cloud stores mass traffic data information;
dividing data into real-time data and historical data according to a time sequence;
building a corresponding static database and a dynamic database according to the data types; generating a corresponding optimized control scheme and a corresponding coordinated control scheme;
determining the road condition of a control area according to the scheme, and setting a corresponding configuration scheme; selecting a corresponding optimization control scheme, determining a signal period, a signal phase sequence and a relative phase difference of the signal machine, and generating a configuration optimization scheme; selecting a coordination control scheme, determining a signal period, a signal phase sequence and a relative phase difference of a signal machine, and generating a configuration coordination scheme;
according to the received configuration scheme, adjusting the signal period, the signal phase sequence and the relative phase difference of each signal controller;
and driving the signal lamp to execute.
Further, the client information comprises a client ID, vehicle longitude and latitude coordinates, a traveling direction, a traveling speed, time and a request instruction; the request instruction comprises a straight-line request instruction, a left-turning request instruction, a right-turning request instruction and a turning request instruction.
Further, the static database establishes a short-time traffic flow prediction algorithm under the week similarity characteristic by weighting historical data and correcting a time series prediction model, calculates the average travel time between road network intersections according to the historical data, the average stop times and the average waiting time of red lights of each intersection and the distance between adjacent intersections according to the road load distribution effective green light time principle, and provides parameters for an optimization control scheme; dividing congestion levels according to the average running speed of the vehicles and the vehicle saturation of the distance in unit time; and determining the optimized control schemes corresponding to the congestion levels respectively according to the congestion levels and the time periods.
Further, the dynamic database is used as a real-time data processing database. And according to the data collected in real time, calculating the vehicle saturation according to the average running speed of the vehicle and the distance in unit time, comparing the vehicle saturation with historical data, and determining a coordination control scheme according to the difference range.
Further, driving the signal lamp is performed.
And further, the congestion level is divided into four levels of smooth, slow running, congestion and severe congestion.
A traffic signal control system based on road condition traffic big data comprises: the system comprises a client, a cloud virtual machine, a data storage server, a data analysis server, a signal control center, a regional traffic signal controller and a signal lamp;
the client is used for sending client information to the cloud platform by the client on the vehicle;
the cloud virtual machine is used for acquiring the road condition information of a large number of intersection vehicles and traffic lights of each LINK road section on the road from the high-grade map;
the data storage server is used for acquiring data, dividing the data into real-time data and historical data according to a time sequence, and generating and storing traffic data information by using known road network structure parameters and intersection signal timing parameters in combination with a related traffic flow parameter short-time prediction method for reading;
the data analysis server is used for judging the congestion state of the intersection, building a corresponding static database and a corresponding dynamic database according to the data type and generating a corresponding optimization control scheme and a coordination control scheme;
the signal control center determines the road condition of a control area according to the scheme and sets a corresponding configuration scheme; selecting a corresponding optimization control scheme, determining a signal period, a signal phase sequence and a relative phase difference of the signal machine, and generating a configuration optimization scheme; selecting a coordination control scheme, determining a signal period, a signal phase sequence and a relative phase difference of a signal machine, and generating a configuration coordination scheme;
the regional traffic signal controller adjusts the relative phase difference according to the received configuration scheme to generate a rapid smooth signal control scheme, and sets a signal period, a signal phase sequence and a relative phase difference for each machine according to a coordination configuration scheme and an optimization configuration scheme;
and the signal machine is used for driving the signal lamp to adjust the signal period and the signal phase sequence.
Preferably, the client is arranged on the vehicle and sends client information to the cloud platform through a data interface; the client information comprises a client ID, vehicle longitude and latitude coordinates, a traveling direction, a traveling speed, time and a request instruction; the request instruction comprises a straight-going request instruction, a left-turning request instruction, a right-turning request instruction and a turning request instruction.
Preferably, the static database establishes a short-term traffic flow prediction algorithm under the week similarity characteristic by weighting historical data and correcting a time series prediction model, calculates the average travel time between road network intersections according to the historical data, the average stop times and the average waiting time of red lights of each intersection and the distance between adjacent intersections according to the road load distribution effective green light time principle, and provides parameters for an optimization control scheme; dividing congestion levels according to the average running speed of the vehicles and the vehicle saturation of the distance in unit time; and determining the optimized control schemes corresponding to the congestion levels respectively according to the congestion levels and the time periods.
Preferably, the dynamic database is used as a real-time data processing database. And according to the data collected in real time, calculating the vehicle saturation according to the average running speed of the vehicle and the distance in unit time, comparing the vehicle saturation with historical data, and determining a coordination control scheme according to the difference range.
Preferably, the congestion level is divided into four levels of smooth traffic, slow traffic, congestion and severe congestion.
The invention has the advantages and beneficial effects that: based on the instructions transmitted by the client, utilizing cloud storage, mastering the trend based on big data processing, and making an optimization scheme according to the change rule; the optimal operation phase of the traffic signal is controlled, people can go out conveniently, the operation of the signal lamp can be adjusted in time according to a coordination control scheme generated by real-time data collection and comparison with historical data, and the emergency traffic condition can be met.
Drawings
Fig. 1 is a main flow chart of a traffic signal control method based on road condition traffic big data according to the present invention;
FIG. 2 is a schematic structural diagram of a traffic signal control system based on road traffic big data according to the present invention;
fig. 3 is a detailed flowchart of the traffic signal control system based on traffic big data shown in fig. 1;
in the figure: 1. the system comprises a client, 2, a cloud virtual machine, 3, a data storage server, 4, a data analysis server, 5, a signal control center, 6, a regional traffic signal controller, 7, a signal lamp, 31, a data storage module, 301, a historical data storage module and 302, and a real-time data storage module.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
As shown in fig. 1, a traffic signal control method based on road traffic big data includes:
step 101, a client on a vehicle sends client information to a cloud platform;
102, the cloud stores mass traffic data information;
103, dividing the data into real-time data and historical data according to a time sequence;
104, building a corresponding static database and a dynamic database according to the data types; generating a corresponding optimization control scheme and a coordination control scheme;
step 105, determining the road condition of the control area according to the scheme, and setting a corresponding configuration scheme; selecting a corresponding optimization control scheme, determining a signal period, a signal phase sequence and a relative phase difference of the signal machine, and generating a configuration optimization scheme; selecting a coordination control scheme, determining a signal period, a signal phase sequence and a relative phase difference of a signal machine, and generating a configuration coordination scheme;
step 106, adjusting the signal period, the signal phase sequence and the relative phase difference of each signal controller according to the received configuration scheme;
and step 107, driving the signal lamp to execute.
The client information in step 101 includes a client ID, vehicle longitude and latitude coordinates, a traveling direction, a traveling speed, time and a request instruction; the request instruction comprises a straight-line request instruction, a left-turning request instruction, a right-turning request instruction and a turning request instruction.
In step 104, the static database is a short-term traffic flow prediction algorithm under the week similarity characteristic established by weighting historical data and correcting a time series prediction model, average travel time among road network intersections is calculated according to the historical data, the red light of each intersection is average stop times and average waiting time, and the distance between adjacent intersections is effectively green light time distributed according to the road load degree, so that parameters are provided for an optimization control scheme; dividing congestion levels according to the average running speed of the vehicles and the vehicle saturation of the distance in unit time; and determining an optimization control scheme corresponding to each congestion level according to the congestion level and the time interval.
The calculation of the average travel time between road network intersections should be in accordance with the following formula, Ti＝ti2-ti1ti2And ti1Respectively the time when the vehicle i sequentially passes through the front and the rear bayonets; the travel time conforms to the normal distribution rule, namely the sample conforms toWherein,after the minimum probability data is excluded according to the above formula, the arithmetic mean is obtained as the mean travel time of the present cycle. According to the velocity-time equationThe average travel speed can be obtained;
the construction prediction model is formed by accumulating and modeling a construction prediction model such as the traffic flow original sequence counted by a certain intersection in a road network according to a certain time t, and is expressed as followsWherein,representing a traffic flow predicted value in n hours of break after the current time t in the w week; w is akWeight of week k ηkRepresents a correction coefficient;showing the historical traffic flow in the period of time n after the time t of the w week.
And calculating the traffic flow in the time period of t per day according to the above formula algorithm, determining the vehicle saturation at the moment, and grading the vehicle saturation at the moment.
Wherein, the dynamic database in step 104 is used as the real-time data processing database. And according to the data collected in real time, calculating the vehicle saturation according to the average running speed of the vehicle and the distance in unit time, comparing the vehicle saturation with historical data, and determining a coordination control scheme according to the difference range.
In step 104, the congestion level is divided into four levels of smooth, slow running, congestion and severe congestion.
A traffic signal control system based on road condition traffic big data comprises: the system comprises a client 1, a cloud virtual machine 2, a data storage server 3, a data analysis server 4, a signal control center 5, a regional traffic signal controller 6 and a signal lamp 7;
the client 1 is used for sending client information to the cloud platform by the client on the vehicle;
the cloud virtual machine 2 is used for acquiring the road condition information of a large number of intersection vehicles and traffic lights of each LINK road section on the road from the high-grade map;
the data storage server 3 comprises a data storage module 31, which is divided into a historical data storage module 301 and a real-time data storage module 302 for acquiring data, dividing the data into real-time data and historical data according to a time sequence, and generating and storing traffic data information by using known road network structure parameters and intersection signal timing parameters in combination with a related traffic flow parameter short-time prediction method for reading;
the data analysis server 4 is used for judging the congestion state of the intersection, building a corresponding static database and a corresponding dynamic database according to the data type and generating a corresponding optimization control scheme and a corresponding coordination control scheme;
the signal control center 5 determines the road condition of the control area according to the scheme and sets a corresponding configuration scheme; selecting a corresponding optimization control scheme, determining a signal period, a signal phase sequence and a relative phase difference of the signal machine, and generating a configuration optimization scheme; selecting a coordination control scheme, determining a signal period, a signal phase sequence and a relative phase difference of a signal machine, and generating a configuration coordination scheme;
the regional traffic signal controller 6 adjusts the relative phase difference according to the received configuration scheme to generate a rapid smooth signal control scheme, and sets a signal period, a signal phase sequence and a relative phase difference for each machine according to the coordination configuration scheme and the optimization configuration scheme;
and the signal machine 7 is used for driving the signal lamp to adjust the signal period and the signal phase sequence.
The client 1 is arranged on a vehicle and sends client information to the cloud platform through a data interface; the client information comprises a client ID, vehicle longitude and latitude coordinates, a traveling direction, a traveling speed, time and a request instruction; the request instruction comprises a straight-going request instruction, a left-turning request instruction, a right-turning request instruction and a turning request instruction.
The static database establishes a short-time traffic flow prediction algorithm under the periodic similarity characteristic by weighting historical data and correcting a time sequence prediction model, calculates the average travel time between road network intersections according to the historical data, calculates the average stop times and the average waiting time of red lights of each intersection and distributes effective green light time principles according to the road load between adjacent intersections, and provides parameters for an optimization control scheme; dividing congestion levels according to the average running speed of the vehicles and the vehicle saturation of the distance in unit time; and determining the optimized control schemes corresponding to the congestion levels respectively according to the congestion levels and the time periods.
And the dynamic database is used as a real-time data processing database. And according to the data collected in real time, calculating the vehicle saturation according to the average running speed of the vehicle and the distance in unit time, comparing the vehicle saturation with historical data, and determining a coordination control scheme according to the difference range.
The congestion level is divided into four levels of smooth, slow running, congestion and severe congestion.
The specific embodiment is as follows: the client 1 is carried on a vehicle, sends client instruction information through the client, provides specific position information, uploads the client instruction information to the cloud server 2, updates real-time data through the cloud server 2, and stores historical data and the real-time data in a classified manner through the data storage server 3; the data analysis server 4 analyzes the historical data to divide congestion levels, and determines the current congestion level according to the real-time data; the data analysis server 4 makes a corresponding optimization control scheme according to the congestion level and determines a coordination control scheme according to the congestion level determined by the real-time data; meanwhile, the signal control center 5 determines the road condition of the control area according to the scheme and sets a corresponding configuration scheme; selecting a corresponding optimization control scheme, determining a signal period, a signal phase sequence and a relative phase difference of the signal machine, and generating a configuration optimization scheme; and selecting a coordination control scheme, determining the signal period, the signal phase sequence and the relative phase difference of the annunciator, generating a configuration coordination scheme signal control center 5 and the regional traffic signal controller 6, and controlling the two serial ports by adopting serial ports which are connected through optical fibers. The regional traffic signal controller 6 adjusts the relative phase difference according to the received configuration scheme to generate a fast and smooth signal control scheme, and sets a signal period, a signal phase sequence and a relative phase difference for each machine according to the coordination configuration scheme and the optimization configuration scheme.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A traffic signal control method based on road condition traffic big data is characterized by comprising the following steps:
a client on the vehicle sends client information to the cloud platform;
the cloud stores mass traffic data information;
dividing data into real-time data and historical data according to a time sequence;
building a corresponding static database and a dynamic database according to the data types; generating a corresponding optimization control scheme and a coordination control scheme;
determining the road condition of a control area according to the scheme, and setting a corresponding configuration scheme; selecting a corresponding optimization control scheme, determining a signal period, a signal phase sequence and a relative phase difference of the signal machine, and generating a configuration optimization scheme; selecting a coordination control scheme, determining a signal period, a signal phase sequence and a relative phase difference of a signal machine, and generating a configuration coordination scheme;
according to the received configuration scheme, adjusting the signal period, the signal phase sequence and the relative phase difference of each signal controller;
and driving the signal lamp to execute.
2. The traffic signal control method based on road condition traffic big data as claimed in claim 1, wherein: the client information comprises a client ID, vehicle longitude and latitude coordinates, a traveling direction, a traveling speed, time and a request instruction; the request instruction comprises a straight-line request instruction, a left-turning request instruction, a right-turning request instruction and a turning request instruction.
3. The traffic signal control method based on road condition traffic big data as claimed in claim 1, wherein: the static database establishes a short-time traffic flow prediction algorithm under the periodic similarity characteristic by weighting historical data and correcting a time sequence prediction model, calculates the average travel time between road network intersections according to the historical data, calculates the average stop times and the average waiting time of red lights of each intersection and distributes effective green light time principles according to the road load between adjacent intersections, and provides parameters for an optimization control scheme; dividing congestion levels according to the average running speed of the vehicles and the vehicle saturation of the distance in unit time; and determining the optimized control schemes corresponding to the congestion levels respectively according to the congestion levels and the time periods.
4. The traffic signal control method based on road condition traffic big data as claimed in claim 1, wherein: and the dynamic database is used as a real-time data processing database. And according to the data collected in real time, calculating the vehicle saturation according to the average running speed of the vehicle and the distance in unit time, comparing the vehicle saturation with historical data, and determining a coordination control scheme according to the difference range.
5. The traffic signal control method based on road condition traffic big data as claimed in claim 3, wherein: the congestion level is divided into four levels of smooth, slow running, congestion and severe congestion.
6. A traffic signal control system based on road condition traffic big data is characterized by comprising the following components: the system comprises a client, a cloud virtual machine, a data storage server, a data analysis server, a signal control center, a regional traffic signal controller and a signal lamp;
the client is used for sending client information to the cloud platform by the client on the vehicle;
the cloud virtual machine is used for acquiring the road condition information of a large number of intersection vehicles and traffic lights of each LINK road section on the road from the high-grade map;
the data storage server is used for acquiring data, dividing the data into real-time data and historical data according to a time sequence, and generating and storing traffic data information by using known road network structure parameters and intersection signal timing parameters in combination with a related traffic flow parameter short-time prediction method for reading;
the data analysis server is used for judging the congestion state of the intersection, building a corresponding static database and a corresponding dynamic database according to the data type and generating a corresponding optimization control scheme and a coordination control scheme;
the signal control center determines the road condition of a control area according to the scheme and sets a corresponding configuration scheme; selecting a corresponding optimization control scheme, determining a signal period, a signal phase sequence and a relative phase difference of the signal machine, and generating a configuration optimization scheme; selecting a coordination control scheme, determining a signal period, a signal phase sequence and a relative phase difference of a signal machine, and generating a configuration coordination scheme;
the regional traffic signal controller adjusts the relative phase difference according to the received configuration scheme to generate a rapid smooth signal control scheme, and sets a signal period, a signal phase sequence and a relative phase difference for the signal machine according to the coordination configuration scheme and the optimization configuration scheme;
and the signal machine is used for driving the signal lamp to adjust the signal period and the signal phase sequence.
7. The traffic signal control system based on road traffic big data as claimed in claim 6, wherein: the client is arranged on the vehicle and sends client information to the cloud platform through the data interface; the client information comprises a client ID, vehicle longitude and latitude coordinates, a traveling direction, a traveling speed, time and a request instruction; the request instruction comprises a straight-going request instruction, a left-turning request instruction, a right-turning request instruction and a turning request instruction.
8. The traffic signal control system based on road traffic big data as claimed in claim 6, wherein: the static database establishes a short-time traffic flow prediction algorithm under the periodic similarity characteristic by weighting historical data and correcting a time sequence prediction model, calculates the average travel time between road network intersections according to the historical data, calculates the average stop times and the average waiting time of red lights of each intersection and distributes effective green light time principles according to the road load between adjacent intersections, and provides parameters for an optimization control scheme; dividing congestion levels according to the average running speed of the vehicles and the vehicle saturation of the distance in unit time; and determining the optimized control schemes corresponding to the congestion levels respectively according to the congestion levels and the time periods.
9. The traffic signal control system based on road traffic big data as claimed in claim 6, wherein: and the dynamic database is used as a real-time data processing database. And according to the data collected in real time, calculating the vehicle saturation according to the average running speed of the vehicle and the distance in unit time, comparing the vehicle saturation with historical data, and determining a coordination control scheme according to the difference range.
10. The traffic signal control system based on road traffic big data as claimed in claim 8, wherein: the congestion level is divided into four levels of smooth, slow running, congestion and severe congestion.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510973461.1A CN105390000A (en) | 2015-12-18 | 2015-12-18 | Traffic signal control system and method based on road condition traffic big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510973461.1A CN105390000A (en) | 2015-12-18 | 2015-12-18 | Traffic signal control system and method based on road condition traffic big data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN105390000A true CN105390000A (en) | 2016-03-09 |
Family
ID=55422238
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510973461.1A Pending CN105390000A (en) | 2015-12-18 | 2015-12-18 | Traffic signal control system and method based on road condition traffic big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105390000A (en) |
Cited By (35)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105702036A (en) * | 2016-03-23 | 2016-06-22 | 深圳市金溢科技股份有限公司 | Calculation method, apparatus and system for vehicle driving time |
| CN105894814A (en) * | 2016-05-11 | 2016-08-24 | 浙江大学 | Joint optimization method and system for multiple traffic management and control measures in consideration of environmental benefits |
| CN105957370A (en) * | 2016-06-20 | 2016-09-21 | 三峡大学 | QoE-based traffic scheduling big data system and method |
| CN106355885A (en) * | 2016-11-24 | 2017-01-25 | 深圳市永达电子信息股份有限公司 | Traffic signal dynamic control method and system based on big data analysis platform |
| CN106530758A (en) * | 2016-10-31 | 2017-03-22 | 安徽科力信息产业有限责任公司 | Method, apparatus and system for adjusting control parameter of annunciator automatically by using internet |
| CN106530766A (en) * | 2017-01-04 | 2017-03-22 | 成都四方伟业软件股份有限公司 | Traffic regulation and control system and method based on big data |
| CN106548634A (en) * | 2016-10-31 | 2017-03-29 | 安徽科力信息产业有限责任公司 | The method and device of semaphore control parameter is precisely adjusted based on Internet map |
| CN107331172A (en) * | 2017-08-25 | 2017-11-07 | 青岛海信网络科技股份有限公司 | A kind of traffic phase adjusting method and device based on road traffic state |
| CN107730927A (en) * | 2017-11-29 | 2018-02-23 | 天津易华录信息技术有限公司 | Signal Optimal Control System based on Internet traffic big data |
| CN108765988A (en) * | 2018-05-23 | 2018-11-06 | 华南理工大学 | A kind of crossing signals dynamic optimization method of Internet data |
| CN108806239A (en) * | 2018-06-15 | 2018-11-13 | 公安部交通管理科学研究所 | The pedestrian's street crossing control system and method coordinated based on upstream and downstream signalized intersections |
| CN109121117A (en) * | 2018-07-13 | 2019-01-01 | 上海淞泓智能汽车科技有限公司 | A kind of bus or train route real-time communication system and communication means based on cellular network |
| CN109285347A (en) * | 2018-09-26 | 2019-01-29 | 东莞绿邦智能科技有限公司 | A kind of urban road congestion analysis system based on cloud platform |
| WO2019061933A1 (en) * | 2017-09-28 | 2019-04-04 | 孟卫平 | Traffic signal chord panning control method and system |
| CN110491144A (en) * | 2019-07-23 | 2019-11-22 | 平安国际智慧城市科技股份有限公司 | The method and relevant device of adjustment traffic lights duration based on road condition predicting |
| CN110533932A (en) * | 2019-08-06 | 2019-12-03 | 银江股份有限公司 | A kind of City-level universe traffic signals recommended method and system based on rolling optimization |
| CN110546695A (en) * | 2017-03-29 | 2019-12-06 | 赫尔环球有限公司 | Method, apparatus and computer program product for integrated management of signal phase and timing for traffic lights |
| CN110634309A (en) * | 2019-09-04 | 2019-12-31 | 南京洛普股份有限公司 | Scheme selection type traffic signal dynamic green wave control method based on electric alarm data |
| CN111009140A (en) * | 2019-11-26 | 2020-04-14 | 郑州大学 | Intelligent traffic signal control method based on open-source road condition information |
| CN111183465A (en) * | 2018-10-16 | 2020-05-19 | 北京嘀嘀无限科技发展有限公司 | Adaptive traffic control using vehicle trajectory data |
| CN111429730A (en) * | 2018-12-24 | 2020-07-17 | 北京嘀嘀无限科技发展有限公司 | Traffic signal period calculation method and apparatus, and computer-readable storage medium |
| CN111445694A (en) * | 2020-03-04 | 2020-07-24 | 青岛海信网络科技股份有限公司 | Festival and holiday traffic scheduling method and device based on traffic flow prediction |
| CN111445707A (en) * | 2019-11-22 | 2020-07-24 | 北京邮电大学 | Timing method and device of traffic signal lamp, electronic equipment and storage medium |
| CN112116812A (en) * | 2020-09-25 | 2020-12-22 | 安徽达尔智能控制系统股份有限公司 | Vehicle road cooperative control system based on V2X car networking technology |
| CN112735152A (en) * | 2021-01-07 | 2021-04-30 | 王美金 | Multi-agent cooperative internet vehicle intersection mixed traffic control system and method |
| CN112885123A (en) * | 2021-01-18 | 2021-06-01 | 湖南省交通规划勘察设计院有限公司 | Urban road condition information management system combining computer information and communication technology |
| CN112990595A (en) * | 2021-03-30 | 2021-06-18 | 北京嘀嘀无限科技发展有限公司 | Travel time prediction method, travel time prediction device, storage medium and electronic equipment |
| CN113299059A (en) * | 2021-04-08 | 2021-08-24 | 四川国蓝中天环境科技集团有限公司 | Data-driven road traffic control decision support method |
| CN113554868A (en) * | 2021-06-29 | 2021-10-26 | 西安四维图新信息技术有限公司 | Dynamic traffic information updating method, device, medium, cloud platform and driving system |
| CN113628454A (en) * | 2021-08-18 | 2021-11-09 | 中国联合网络通信集团有限公司 | Traffic signal lamp control method, device and system |
| CN113851008A (en) * | 2021-10-25 | 2021-12-28 | 深圳技术大学 | Traffic signal lamp control method, device, equipment and storage medium |
| CN114093170A (en) * | 2021-11-26 | 2022-02-25 | 阿波罗智联(北京)科技有限公司 | Generation method, system and device of annunciator control scheme and electronic equipment |
| CN115376340A (en) * | 2022-08-10 | 2022-11-22 | 重庆市城投金卡信息产业(集团)股份有限公司 | Cross-platform traffic signal lamp coordination control method based on RFID |
| CN116246474A (en) * | 2023-02-15 | 2023-06-09 | 珠海市思瑞达智能科技有限公司 | Traffic signal phase timing control method and system based on cloud edge end cooperation |
| CN116758763A (en) * | 2023-05-06 | 2023-09-15 | 西藏金采科技股份有限公司 | Traffic data processing system and method based on Internet of vehicles |
-
2015
- 2015-12-18 CN CN201510973461.1A patent/CN105390000A/en active Pending
Cited By (50)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105702036A (en) * | 2016-03-23 | 2016-06-22 | 深圳市金溢科技股份有限公司 | Calculation method, apparatus and system for vehicle driving time |
| CN105894814A (en) * | 2016-05-11 | 2016-08-24 | 浙江大学 | Joint optimization method and system for multiple traffic management and control measures in consideration of environmental benefits |
| CN105894814B (en) * | 2016-05-11 | 2018-05-08 | 浙江大学 | Consider a variety of traffic control measure combined optimization methods and system of environmental benefit |
| CN105957370A (en) * | 2016-06-20 | 2016-09-21 | 三峡大学 | QoE-based traffic scheduling big data system and method |
| CN106548634B (en) * | 2016-10-31 | 2019-05-14 | 安徽科力信息产业有限责任公司 | Method and device based on the accurate adjustment signal machine control parameter of Internet map |
| CN106530758A (en) * | 2016-10-31 | 2017-03-22 | 安徽科力信息产业有限责任公司 | Method, apparatus and system for adjusting control parameter of annunciator automatically by using internet |
| CN106548634A (en) * | 2016-10-31 | 2017-03-29 | 安徽科力信息产业有限责任公司 | The method and device of semaphore control parameter is precisely adjusted based on Internet map |
| CN106355885A (en) * | 2016-11-24 | 2017-01-25 | 深圳市永达电子信息股份有限公司 | Traffic signal dynamic control method and system based on big data analysis platform |
| CN106530766A (en) * | 2017-01-04 | 2017-03-22 | 成都四方伟业软件股份有限公司 | Traffic regulation and control system and method based on big data |
| CN110546695A (en) * | 2017-03-29 | 2019-12-06 | 赫尔环球有限公司 | Method, apparatus and computer program product for integrated management of signal phase and timing for traffic lights |
| CN107331172A (en) * | 2017-08-25 | 2017-11-07 | 青岛海信网络科技股份有限公司 | A kind of traffic phase adjusting method and device based on road traffic state |
| CN107331172B (en) * | 2017-08-25 | 2020-11-20 | 青岛海信网络科技股份有限公司 | Traffic phase adjusting method and device based on road traffic state |
| WO2019061933A1 (en) * | 2017-09-28 | 2019-04-04 | 孟卫平 | Traffic signal chord panning control method and system |
| CN107730927B (en) * | 2017-11-29 | 2021-03-23 | 天津易华录信息技术有限公司 | Signal optimization control system based on internet traffic big data |
| CN107730927A (en) * | 2017-11-29 | 2018-02-23 | 天津易华录信息技术有限公司 | Signal Optimal Control System based on Internet traffic big data |
| CN108765988B (en) * | 2018-05-23 | 2021-01-19 | 华南理工大学 | Intersection signal dynamic optimization method for internet data |
| CN108765988A (en) * | 2018-05-23 | 2018-11-06 | 华南理工大学 | A kind of crossing signals dynamic optimization method of Internet data |
| CN108806239B (en) * | 2018-06-15 | 2020-12-08 | 公安部交通管理科学研究所 | Pedestrian crossing control system and method based on upstream and downstream signalized intersection coordination |
| CN108806239A (en) * | 2018-06-15 | 2018-11-13 | 公安部交通管理科学研究所 | The pedestrian's street crossing control system and method coordinated based on upstream and downstream signalized intersections |
| CN109121117A (en) * | 2018-07-13 | 2019-01-01 | 上海淞泓智能汽车科技有限公司 | A kind of bus or train route real-time communication system and communication means based on cellular network |
| CN109121117B (en) * | 2018-07-13 | 2021-02-05 | 上海淞泓智能汽车科技有限公司 | Cellular network-based vehicle road real-time communication system and communication method |
| CN109285347A (en) * | 2018-09-26 | 2019-01-29 | 东莞绿邦智能科技有限公司 | A kind of urban road congestion analysis system based on cloud platform |
| CN111183465A (en) * | 2018-10-16 | 2020-05-19 | 北京嘀嘀无限科技发展有限公司 | Adaptive traffic control using vehicle trajectory data |
| CN111183465B (en) * | 2018-10-16 | 2021-03-23 | 北京嘀嘀无限科技发展有限公司 | Adaptive traffic control using vehicle trajectory data |
| CN111429730A (en) * | 2018-12-24 | 2020-07-17 | 北京嘀嘀无限科技发展有限公司 | Traffic signal period calculation method and apparatus, and computer-readable storage medium |
| CN110491144A (en) * | 2019-07-23 | 2019-11-22 | 平安国际智慧城市科技股份有限公司 | The method and relevant device of adjustment traffic lights duration based on road condition predicting |
| CN110533932A (en) * | 2019-08-06 | 2019-12-03 | 银江股份有限公司 | A kind of City-level universe traffic signals recommended method and system based on rolling optimization |
| CN110634309A (en) * | 2019-09-04 | 2019-12-31 | 南京洛普股份有限公司 | Scheme selection type traffic signal dynamic green wave control method based on electric alarm data |
| CN110634309B (en) * | 2019-09-04 | 2021-01-05 | 南京洛普股份有限公司 | Scheme selection type traffic signal dynamic green wave control method based on electric alarm data |
| CN111445707A (en) * | 2019-11-22 | 2020-07-24 | 北京邮电大学 | Timing method and device of traffic signal lamp, electronic equipment and storage medium |
| CN111009140B (en) * | 2019-11-26 | 2021-02-05 | 郑州大学 | Intelligent traffic signal control method based on open-source road condition information |
| CN111009140A (en) * | 2019-11-26 | 2020-04-14 | 郑州大学 | Intelligent traffic signal control method based on open-source road condition information |
| CN111445694B (en) * | 2020-03-04 | 2022-02-01 | 青岛海信网络科技股份有限公司 | Festival and holiday traffic scheduling method and device based on traffic flow prediction |
| CN111445694A (en) * | 2020-03-04 | 2020-07-24 | 青岛海信网络科技股份有限公司 | Festival and holiday traffic scheduling method and device based on traffic flow prediction |
| CN112116812A (en) * | 2020-09-25 | 2020-12-22 | 安徽达尔智能控制系统股份有限公司 | Vehicle road cooperative control system based on V2X car networking technology |
| CN112735152A (en) * | 2021-01-07 | 2021-04-30 | 王美金 | Multi-agent cooperative internet vehicle intersection mixed traffic control system and method |
| CN112885123A (en) * | 2021-01-18 | 2021-06-01 | 湖南省交通规划勘察设计院有限公司 | Urban road condition information management system combining computer information and communication technology |
| CN112990595A (en) * | 2021-03-30 | 2021-06-18 | 北京嘀嘀无限科技发展有限公司 | Travel time prediction method, travel time prediction device, storage medium and electronic equipment |
| CN113299059A (en) * | 2021-04-08 | 2021-08-24 | 四川国蓝中天环境科技集团有限公司 | Data-driven road traffic control decision support method |
| CN113554868B (en) * | 2021-06-29 | 2023-04-18 | 西安四维图新信息技术有限公司 | Dynamic traffic information updating method, device, medium, cloud platform and driving system |
| CN113554868A (en) * | 2021-06-29 | 2021-10-26 | 西安四维图新信息技术有限公司 | Dynamic traffic information updating method, device, medium, cloud platform and driving system |
| CN113628454A (en) * | 2021-08-18 | 2021-11-09 | 中国联合网络通信集团有限公司 | Traffic signal lamp control method, device and system |
| CN113851008A (en) * | 2021-10-25 | 2021-12-28 | 深圳技术大学 | Traffic signal lamp control method, device, equipment and storage medium |
| CN114093170A (en) * | 2021-11-26 | 2022-02-25 | 阿波罗智联(北京)科技有限公司 | Generation method, system and device of annunciator control scheme and electronic equipment |
| CN114093170B (en) * | 2021-11-26 | 2023-01-24 | 阿波罗智联(北京)科技有限公司 | Generation method, system and device of annunciator control scheme and electronic equipment |
| CN115376340A (en) * | 2022-08-10 | 2022-11-22 | 重庆市城投金卡信息产业(集团)股份有限公司 | Cross-platform traffic signal lamp coordination control method based on RFID |
| CN116246474A (en) * | 2023-02-15 | 2023-06-09 | 珠海市思瑞达智能科技有限公司 | Traffic signal phase timing control method and system based on cloud edge end cooperation |
| CN116246474B (en) * | 2023-02-15 | 2024-05-31 | 珠海市思瑞达智能科技有限公司 | Traffic signal phase timing control method and system based on cloud edge end cooperation |
| CN116758763A (en) * | 2023-05-06 | 2023-09-15 | 西藏金采科技股份有限公司 | Traffic data processing system and method based on Internet of vehicles |
| CN116758763B (en) * | 2023-05-06 | 2024-02-20 | 西藏金采科技股份有限公司 | Traffic data processing system and method based on Internet of vehicles |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105390000A (en) | Traffic signal control system and method based on road condition traffic big data | |
| CN108806283B (en) | Control method of traffic signal lamp and Internet of vehicles platform | |
| WO2019047905A1 (en) | Road traffic analysis system, method and apparatus | |
| WO2018149307A1 (en) | Method and apparatus for analyzing traffic conditions and electronic device | |
| CN111613070B (en) | Traffic signal lamp control method, traffic signal lamp control device, electronic equipment and computer storage medium | |
| CN111341095B (en) | Traffic signal control system and method based on edge side online calculation | |
| CN109697866B (en) | Edge calculation traffic signal lamp control system and control method | |
| CN111845754B (en) | Decision prediction method of automatic driving vehicle based on edge calculation and crowd-sourcing algorithm | |
| CN113096418B (en) | Traffic network traffic light control method, system and computer readable storage medium | |
| CN109612488B (en) | Big data micro-service-based mixed travel mode path planning system and method | |
| CN111341107A (en) | Shared traffic control method based on cloud platform data | |
| US11798408B2 (en) | Green wave speed determination method, electronic device and storage medium | |
| CN112071095B (en) | Signal timing method for emergency vehicle passing intersection based on V2X technology | |
| Chen et al. | Dynamic traffic light optimization and Control System using model-predictive control method | |
| CN111192457A (en) | Method and device for controlling urban-level integral traffic signals | |
| Wang et al. | Real-time urban regional route planning model for connected vehicles based on V2X communication | |
| CN108765988B (en) | Intersection signal dynamic optimization method for internet data | |
| CN108592930A (en) | New-energy automobile intelligence auxiliary driving method based on Large system optimization and car networking | |
| CN117496726A (en) | Traffic control coordination method and system based on intelligent traffic system | |
| CN105913673B (en) | A kind of large-scale road network signal lamp presumption method | |
| CN108831163B (en) | Main road cooperative annunciator control method based on geomagnetism | |
| CN107730890B (en) | Intelligent transportation method based on traffic flow speed prediction in real-time scene | |
| JPH0714093A (en) | Traffic signal controlling method | |
| WO2018227157A1 (en) | Adaptive traffic control | |
| Liu et al. | Road Speed Fusion Model Based on Wavelet Neural Network Optimized by Genetic Algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20160309 |
|
| WD01 | Invention patent application deemed withdrawn after publication |