CN109035816A - A kind of intelligent tide flow lamp based on data prediction - Google Patents
A kind of intelligent tide flow lamp based on data prediction Download PDFInfo
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- CN109035816A CN109035816A CN201811112208.7A CN201811112208A CN109035816A CN 109035816 A CN109035816 A CN 109035816A CN 201811112208 A CN201811112208 A CN 201811112208A CN 109035816 A CN109035816 A CN 109035816A
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Classifications
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/07—Controlling traffic signals
- G08G1/08—Controlling traffic signals according to detected number or speed of vehicles
-
- 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
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/095—Traffic lights
Abstract
The present invention can only carry out the mechanical timing variation of reddish yellow green light progress to traditional traffic lights according to the time of regulation and be improved, including obtaining data module, algorithm realizes analysis prediction module, the tri coloured lantern module of the traffic lights on intelligent control red yellowish green trichromatism lamp timing duration module and basis, by with numerical map, navigation, the connection of taxi-hailing software, obtain the data of real-time number of users, the vehicle flowrate size and road congestion state in the region are predicted in conjunction with the fuzzy Judgment method that time series data prediction and Principle components analysis combine, to increase the green time in crowded direction by the reasonable green time for reducing the loose direction of wagon flow of data analysis realization, to reduce the congestion state of road.The present invention can easily realize the adjustment traffic lights timing that practical intelligence is analyzed by data, solve the road congestion state in peak time, popularization with higher and practical value.
Description
Technical field
The present invention relates to technical field of data prediction, intelligent traffic light field more particularly to it is a kind of based on data prediction
The design of tide flow lamp.
Background technique
With the development of social economy and gradually human-orientedization of vehicle price, the vehicle fleet size in city is increased rapidly, which
Be afraid of that traffic jam issue is also got worse in three lines, four line city.Urban transport problems also directly affects the work and study of people
Life, to restrict the construction and economic growth in city.Commuter time especially on weekdays, huge vehicle flowrate
So that road is crowded, many unnecessary time wastes are caused.Also, all kinds of map softwares are used now, taxi-hailing software
User increase, according to software provide data come the traffic lights to crowded section of highway time shorten the congestion time be very necessary
's.
The road traffic flow in road direction can be realized with related algorithm.Based on time series data and principal component point
Phase separation in conjunction with fuzzy synthesis quantitative evaluation method can be by the average speed and the magnitude of traffic flow in section come to the vehicle in region
The degree of crowding is predicted.In big data era, is increased using the user of numerical map, navigation, taxi-hailing software, be can use
Related algorithm predicts overall data by software data realization.
Summary of the invention
The present invention is improved in wagon flow peak period to overcome the above problem, by all kinds of maps, navigate or call a taxi soft
The data of part are predicted, road crowded state at that time is obtained, to control the tide transformation that traffic lights realizes duration.
The present invention can only carry out reddish yellow green light according to the time of regulation to traditional traffic lights and carry out mechanical timing change
Change is improved, and by contacting with numerical map, navigation, taxi-hailing software, obtains the data of real-time number of users, in conjunction with
The fuzzy Judgment method that time series data prediction and Principle components analysis combine predicts the vehicle flowrate size and road in the region
Congestion state, to realize the reasonable green time for reducing the loose direction of wagon flow by data analysis to increase crowded direction
Green time, to reduce the congestion state of peak period road.Make this hair based on big data prediction and tidal control traffic lights
It is bright that there is very high practical value and promotional value in intelligent traffic light application.
Detailed description of the invention
Fig. 1 is the flow chart of the intelligent tide flow lamp work of the present invention；
Fig. 2 is traffic conditions when convention traffic lamp is applied；
Fig. 3 is the remission effect in the rear section of present invention application；
Specific embodiment
Realization process of the invention is discussed in detail with reference to the accompanying drawing.
Fig. 1 is a kind of intelligent tide flow lamp work flow diagram based on big data prediction of the invention, including obtains number
According to module, algorithm realizes analysis prediction module, intelligent control red yellowish green trichromatism lamp timing duration module and the traffic lights on basis
Tri coloured lantern module；
So-called acquisition data module is exactly for obtaining the section obtained from numerical map, navigation, taxi-hailing software
Active user quantity information, there are also after the route planning on navigation software, it is possible to participate in the number of users in the section.
So-called algorithm realizes analysis prediction module, is exactly the high speed microprocessor built in the present invention, by being based on time sequence
The fuzzy synthesis algorithm that column data and principal component analysis combine, carries out the prediction to the degree of crowding in the section.Such as, it is assumed that
The maximal workload of the section direction is Cm, and the number of users of all kinds of maps of acquisition, navigation, taxi-hailing software is X, according to calculation
Method establishes a coefficient value, then the value Y predicted, which can simplify, to be become, predicted value Y is obtained, then comparison prediction
Relationship between value Y and the maximal workload Cm of the section direction, if, then result " unimpeded " is obtained, traffic lights is pressed
The alternating of reddish yellow green light is carried out according to preset value, if, then result " crowded " is obtained, is compared, obtaining α value is
The metric of crowded degree adjusts the timing duration of reddish yellow green light for subsequent calculations；
So-called intelligent control tri coloured lantern timing duration module, exactly on the basis for having set fixed value of basic traffic lights
On, degree of crowding metric α is obtained by prediction module, come the module that preset time is adjusted.For example, setting initial setting
Red light timing duration be, green light timing duration is, by the α value of acquisition, it is mapped in one, then red light timing is set
Shi Changwei, concurrently setting green light timing length is, amber light flashing duration generally immobilize；
As a kind of enforceable scheme, the data module that obtains includes that the wireless WIFI based on EW-7811Un is received
Device, the vehicle user quantity on the acquired section direction is by numerical map, and navigation, taxi-hailing software side provides.
As a kind of enforceable scheme, the analysis prediction module is based on Time-Series analysis data and principal component point
User is uploaded data and carries out data modeling, predicts the vehicle fleet size in whole section direction by the fuzzy synthesis algorithm that phase separation combines；
When traffic lights at the parting of the ways, general principles are identical, only when calculating the degree of crowding, need plus another
The key factor of the degree of crowding on one direction, four crossings carry out the analysis of the degree of crowding simultaneously, will in fuzzy algorithmic approach
The degree of crowding on other road, which is added, calculates the factor, obtains new degree of crowding metric, subtract in the increase for calculating timing
When few, using new degree of crowding metric, also, obtain one and correspond toNew mapping value, judging
For the green light timing length in " crowded " section, the red light timing length in remaining section；
Fig. 2 be convention traffic lamp in use, will appear " unimpeded direction without vehicle by green light, and crowded direction
Also waiting red light " no small waste of time is caused, Fig. 3 is this thinking after being applied to the section, can realize that mitigation is handed over
The effect of logical pressure.
Claims (5)
1. it is a kind of based on data prediction intelligent tide flow lamp, it is characterised in that using Various types of data carry out to road condition into
Row prediction, so that the control traffic lights of intelligence, alleviates the passage pressure of traffic loading height crowded section of highway by stages.
2. a kind of intelligent tide flow lamp based on data prediction according to requiring 1, it is characterised in that improve traditional friendship
Logical lamp, in traditional traffic lights, reddish yellow green light carries out mechanical alternating by the timer set, and in the present invention,
The time of timing between adjusting reddish yellow green light that can be intelligent, thus road pressure of the optimization in peak time.
3. a kind of intelligent tide flow lamp based on data prediction according to requiring 1, it is characterised in that most with people's life
Common numerical map, navigation, taxi-hailing software obtain real-time traffic data, relatively reliable, also, evidence of fetching is in user, excellent
The adventure in daily life of user is changed.
4. a kind of intelligent tide flow lamp based on data prediction according to requiring 1, it is characterised in that when will be by being based on
Between the fuzzy synthesis algorithm that combines of sequence data and principal component analysis, carry out the prediction to the degree of crowding in the section, for example,
Assuming that the maximal workload of the section direction is Cm, all kinds of maps of acquisition, navigation, the number of users of taxi-hailing software is X, root
According to algorithm, a coefficient value is established, then the value Y predicted, which can simplify, to be become, predicted value Y is obtained, is then compared
Relationship between predicted value Y and the maximal workload Cm of the section direction, if, then result " unimpeded ", traffic are obtained
Lamp carries out the alternating of reddish yellow green light according to preset value, if, then result " crowded " is obtained, is compared, obtain α
Value is the metric of crowded degree, and the timing duration of reddish yellow green light is adjusted for subsequent calculations.
5. a kind of intelligent tide flow lamp based on data prediction according to requiring 1, it is characterised in that pass through prediction module
Degree of crowding metric α is obtained, come the module that preset time is adjusted, for example, setting red light timing duration initially set
For, green light timing duration is, by the α value of acquisition, it is mapped in one, then set red light timing length as
, concurrently setting green light timing length is, amber light flashing duration generally immobilize.
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| Application Number | Priority Date | Filing Date | Title |
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| CN201811112208.7A CN109035816A (en) | 2018-09-25 | 2018-09-25 | A kind of intelligent tide flow lamp based on data prediction |
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| CN201811112208.7A CN109035816A (en) | 2018-09-25 | 2018-09-25 | A kind of intelligent tide flow lamp based on data prediction |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110414708A (en) * | 2019-06-10 | 2019-11-05 | 上海旷途科技有限公司 | A kind of tide lane prioritization scheme selection method, device and storage medium |
| CN111402615A (en) * | 2020-04-08 | 2020-07-10 | 王爱伶 | Variable lane control method based on navigation information |
| CN112950964A (en) * | 2021-03-16 | 2021-06-11 | 北京市商汤科技开发有限公司 | Traffic light intelligent control method and device, electronic equipment and storage medium |
| CN113362597A (en) * | 2021-06-03 | 2021-09-07 | 济南大学 | Traffic sequence data anomaly detection method and system based on non-parametric modeling |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2009003698A (en) * | 2007-06-21 | 2009-01-08 | Kyosan Electric Mfg Co Ltd | Traffic signal controller and outgoing traffic flow prediction method |
| CN105046983A (en) * | 2015-08-14 | 2015-11-11 | 奇瑞汽车股份有限公司 | Traffic flow prediction system and method based on vehicle-road cooperation |
| CN106251650A (en) * | 2016-08-23 | 2016-12-21 | 上海斐讯数据通信技术有限公司 | A kind of traffic light control system based on mobile terminal 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 |
| CN107038877A (en) * | 2017-04-07 | 2017-08-11 | 北京易华录信息技术股份有限公司 | Intersection vehicle flowrate forecasting system based on vehicle electron identifying |
| CN107680391A (en) * | 2017-09-28 | 2018-02-09 | 长沙理工大学 | Two pattern fuzzy control methods of crossroad access stream |
-
2018
- 2018-09-25 CN CN201811112208.7A patent/CN109035816A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2009003698A (en) * | 2007-06-21 | 2009-01-08 | Kyosan Electric Mfg Co Ltd | Traffic signal controller and outgoing traffic flow prediction method |
| CN105046983A (en) * | 2015-08-14 | 2015-11-11 | 奇瑞汽车股份有限公司 | Traffic flow prediction system and method based on vehicle-road cooperation |
| CN106251650A (en) * | 2016-08-23 | 2016-12-21 | 上海斐讯数据通信技术有限公司 | A kind of traffic light control system based on mobile terminal 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 |
| CN107038877A (en) * | 2017-04-07 | 2017-08-11 | 北京易华录信息技术股份有限公司 | Intersection vehicle flowrate forecasting system based on vehicle electron identifying |
| CN107680391A (en) * | 2017-09-28 | 2018-02-09 | 长沙理工大学 | Two pattern fuzzy control methods of crossroad access stream |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110414708A (en) * | 2019-06-10 | 2019-11-05 | 上海旷途科技有限公司 | A kind of tide lane prioritization scheme selection method, device and storage medium |
| CN110414708B (en) * | 2019-06-10 | 2023-04-18 | 上海旷途科技有限公司 | Tide lane optimization scheme selection method and device and storage medium |
| CN111402615A (en) * | 2020-04-08 | 2020-07-10 | 王爱伶 | Variable lane control method based on navigation information |
| CN112950964A (en) * | 2021-03-16 | 2021-06-11 | 北京市商汤科技开发有限公司 | Traffic light intelligent control method and device, electronic equipment and storage medium |
| CN113362597A (en) * | 2021-06-03 | 2021-09-07 | 济南大学 | Traffic sequence data anomaly detection method and system based on non-parametric modeling |
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Application publication date: 20181218 |