CN108376470A - Traffic road congestion prediction technique based on particle cluster algorithm - Google Patents
Traffic road congestion prediction technique based on particle cluster algorithm Download PDFInfo
- Publication number
- CN108376470A CN108376470A CN201810045762.1A CN201810045762A CN108376470A CN 108376470 A CN108376470 A CN 108376470A CN 201810045762 A CN201810045762 A CN 201810045762A CN 108376470 A CN108376470 A CN 108376470A
- Authority
- CN
- China
- Prior art keywords
- traffic
- data
- road
- end side
- particle
- 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.)
- Withdrawn
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/008—Artificial life, i.e. computing arrangements simulating life based on physical entities controlled by simulated intelligence so as to replicate intelligent life forms, e.g. based on robots replicating pets or humans in their appearance or behaviour
-
- 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
- G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
-
- 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
- G08G1/0133—Traffic data processing for classifying traffic situation
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Analytical Chemistry (AREA)
- Chemical & Material Sciences (AREA)
- Theoretical Computer Science (AREA)
- Artificial Intelligence (AREA)
- Computing Systems (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Biophysics (AREA)
- General Engineering & Computer Science (AREA)
- Biomedical Technology (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Robotics (AREA)
- Traffic Control Systems (AREA)
Abstract
The traffic road congestion prediction technique based on particle cluster algorithm that the invention discloses a kind of, including step：S1. it utilizes the sensor system of end side and the traffic surveillance and control system at intersection to acquire traffic data, is uploaded to Cloud Server；S2. cloud server traffic data is handled based on modified particle swarm optiziation, obtains prediction data；S3. the equipment that prediction data is transmitted to end side by Cloud Server；S4. the equipment of end side receives the data of Cloud Server passback, and saves it in local storage；S5. Cloud Server obtains the traffic data of terminal and traffic surveillance and control system acquisition in real time, using traffic data as new input variable, is based on modified particle swarm optiziation continuous learning, continues to optimize prediction data.The present invention has been obviously improved precision of prediction, can effectively adjust the magnitude of traffic flow, reduces traffic loading, reduces traffic delay and parking rate, improves the traffic capacity of road network, improve urban traffic conditions.
Description
Technical field
The present invention relates to technical field of transportation, more specifically, are related to a kind of traffic route based on particle cluster algorithm and gather around
Stifled prediction technique.
Background technology
As the development of auto industry has also manufactured a series of problems while automobile is that people bring various convenient,
Such as traffic congestion, environmental pollution, traffic accident.The traffic national conditions in China are that motor vehicle mixes row, bicycle with non-motor vehicle
Quantity is big, and vehicles number is more, and road network carrying capacity is limited etc., implements traffic power strategy, needs solve problem
It is exactly traffic congestion.And in road traffic control field, traffic is monitored, the information system of data acquisition and processing (DAP) etc. is deposited
In many insurmountable problems, especially in intersection traffic control system, there are period length, vehicle delay and lead to
The problems such as row ability is limited.
Conventional traffic control mode is using historical traffic flow data as the foundation of regulation and control, by analyzing in different time
The changing rule of traffic flow carries out timing using artificial mode, and timing scheme is then entered into friendship by computer technology
In ventilating controller, in the application by calling different timing schemes to carry out traffic control, since a large amount of vehicle pours in city
Traffic route, causes serious traffic blocking problem, and the regulation and control traffic flow of timing scheme is increasingly difficult to adapt to traffic congestion
Improvement demand.
For example, the Chinese patent application of Publication No. CN106991815A discloses a kind of traffic congestion control method, packet
Include following steps：S1. obtain target road set parameter, including road passage capability Q, free stream velocity vf and obstruction it is close
Spend kj；S2. the real-time traffic parameter of target road, including real-time traffic amount q, real-time speed v and real-time traffic density are obtained
k；S3. build traffic state judging model, according to the model judge present road traffic speed and the magnitude of traffic flow to traffic flow
Influence degree, and control measure are made to traffic according to influence degree, can in conjunction with the traffic flow of road virtual condition with
And the volume of traffic and traffic speed are analyzed in traffic flow, make accurate traffic control measure, target track can be effectively relieved
The traffic congestion on road, and can effectively avoid the waste of path resource.But, however it remains defect, such as algorithm are set
The problems such as meter is complicated, can not achieve self study, and real-time is poor.
Invention content
It is an object of the invention to overcome the deficiencies of the prior art and provide a kind of traffic routes based on particle cluster algorithm to gather around
The problem of blocking up prediction technique, capable of alleviating traffic congestion, further improves precision of prediction, makes people using to the improvement of technology
Life become finer.
The purpose of the present invention is achieved through the following technical solutions：A kind of traffic route based on particle cluster algorithm is gathered around
Stifled prediction technique, includes the following steps：
S1 acquires the first traffic data, is uploaded to Cloud Server using the sensing system of end side, meanwhile, utilize road
The traffic surveillance and control system of road intersection acquires the second traffic data, is uploaded to Cloud Server；
S2, the first traffic data and second traffic data described in cloud server, and it is based on improved population
Algorithm is handled, and the first prediction data is obtained；
S3, the equipment that first prediction data is transmitted to end side by Cloud Server；
The equipment of S4, the end side receive the data of Cloud Server passback, and save it in local storage,
Then history road condition data whether is stored in the equipment detection local storage of end side, and history road condition data is extracted
Characteristic；
S5, Cloud Server obtain the traffic data of the sensing system acquisition of end side, obtain intersection in real time in real time
Second traffic data of the traffic surveillance and control system acquisition at mouthful, using the first traffic data and the second traffic data as new input
Variable is based on the modified particle swarm optiziation continuous learning, continues to optimize first prediction data.
Further, in step s 2, include the following steps：
S21 carries out initialization operation, the initialization includes kind using the data acquired in step S1 as sample data
Scale, number, weights and the threshold value of iteration of group；
S22 builds neural network structure, and generates a population w at randomi, with population wiRepresent the initial of neural network
Value,
wi=(wi1,wi2...,wis)T
Wherein,
S=pn+pm+p+m
N is the input neuron number of neural network, and p is the hidden layer neuron number of neural network, and m is neural network
Output neuron number；
S23 formulates evaluation parameter, an ANN Evolutionary parameter is created, by the particle newly obtained to neural network
Weights and threshold value are recalculated, until reaching convergent condition, by fitness value fitiIt is defined as,
Wherein, yi′For reality output, yiFor desired output, n represents population scale；
S24 calculates the position of each particle according to sample data, using the best position of particle as history optimum position；
S25 will redefine the position and speed of particle in iterative process each time, will calculate the new adaptation of particle
Then angle value determines individual extreme value；
The optimal solution of weights and threshold value is brought into neural network and is instructed in the condition of convergence for reaching setting by S26
Practice, until obtaining the prediction data of optimal output.
Further, in step s 4, include the following steps：
The history road conditions number that present road whether is preserved in local storage detected in the equipment of end side by S41
According to if so, then extracting the history road condition data of present road, and being marked according to temporal characteristics；If it is not, directly
Enter step S44；
S42, first calculates, and calculates the speed mean value of the history road condition data on present road；Second calculates, and calculates
The running time mean value of history road condition data on present road, and be stored in data are calculated in local storage；
S43, according to the label information in step S41, extraction present road is in history road condition data in different time periods
Speed mean value and/or running time mean value；
S44, the first prediction data that will be stored in local storage, is presented on the display device of the equipment of end side
On, the history road condition data of analysis data and/or end side based on high in the clouds particle cluster algorithm is as a result, to road traffic congestion feelings
Condition is predicted.
Further, the first traffic data includes in real time running speed, residence time, running time, mileage
It is any.
Further, the second traffic data is included in travel speed at signal lamp, running time, residence time, signal
Lamp period, green time, red time any one or more of.
Further, the equipment of end side include vehicle, the device that is arranged on vehicle.
Further, vehicle includes electric vehicle.
Further, vehicle includes automatic driving vehicle.
The beneficial effects of the invention are as follows：
(1) present invention is learnt using modified particle swarm optiziation and exports optimum prediction data setting to end side
It is standby, compared to conventional neural network algorithm, further improve precision of prediction.
(2) the present invention is based on artificial intelligence technologys to alleviate traffic jam issue, has self-learning function, improves traffic letter
Breath system experiences the efficiency of the variation of traffic flow in road network, can effectively adjust the magnitude of traffic flow, reduces traffic loading, reduces
Traffic delay and parking rate, improve the traffic capacity of road network, improve entire urban traffic conditions.
(3) present invention is acted when the history road condition data of the equipment storage to end side is handled by label
Processing step so that when calling data, program runnability is improved, and then improves data-handling efficiency, enhancing
The runnability of equipment.
(4) it is acquired present invention incorporates the real time traffic data of the equipment of end side acquisition and road traffic monitoring system
Intelligent algorithm module is deployed in cloud service by input variable of the traffic data as the neural network algorithm of high in the clouds side
Device, beyond the clouds server carry out that prediction data is issued to terminal side equipment after study processing, a large amount of distributions can be focused on
The data that formula equipment or system upload reduce the cost that analyzing processing is carried out in the equipment or system of end side, improve pair
The forecasting efficiency of traffic road congestion, the traffic trip of Effective Regulation city commuter.
Description of the drawings
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technology description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention without having to pay creative labor, may be used also for those of ordinary skill in the art
With obtain other attached drawings according to these attached drawings.
Fig. 1 is the method and step flow chart of the present invention.
Specific implementation mode
Technical scheme of the present invention is described in further detail below in conjunction with the accompanying drawings, but protection scope of the present invention is not limited to
It is as described below.All features disclosed in this specification, or implicit disclosed all methods or in the process the step of, in addition to mutual
Other than the feature and/or step of repulsion, it can combine in any way.
Any feature disclosed in this specification (including any accessory claim, abstract and attached drawing), except non-specifically chatting
It states, can be replaced by other alternative features that are equivalent or have similar purpose.That is, unless specifically stated, each feature is only
It is an example in a series of equivalent or similar characteristics.
Specific embodiments of the present invention are described more fully below, it should be noted that the embodiments described herein is served only for illustrating
Illustrate, is not intended to restrict the invention.In the following description, in order to provide a thorough understanding of the present invention, a large amount of spies are elaborated
Determine details.It will be apparent, however, to one skilled in the art that：This hair need not be carried out using these specific details
It is bright.In other instances, in order to avoid obscuring the present invention, well known circuit, software or method are not specifically described.
As shown in Figure 1, a kind of traffic road congestion prediction technique based on particle cluster algorithm, includes the following steps：
S1 acquires the first traffic data, is uploaded to Cloud Server using the sensing system of end side, meanwhile, utilize road
The traffic surveillance and control system of road intersection acquires the second traffic data, is uploaded to Cloud Server；
S2, the first traffic data and second traffic data described in cloud server, and it is based on improved population
Algorithm is handled, and the first prediction data is obtained；
S3, the equipment that first prediction data is transmitted to end side by Cloud Server；
The equipment of S4, the end side receive the data of Cloud Server passback, and save it in local storage,
Then history road condition data whether is stored in the equipment detection local storage of end side, and history road condition data is extracted
Characteristic；
S5, Cloud Server obtain the traffic data of the sensing system acquisition of end side, obtain intersection in real time in real time
Second traffic data of the traffic surveillance and control system acquisition at mouthful, using the first traffic data and the second traffic data as new input
Variable is based on the modified particle swarm optiziation continuous learning, continues to optimize first prediction data.
Optionally, in step s 2, include the following steps：
S21 carries out initialization operation, the initialization includes kind using the data acquired in step S1 as sample data
Scale, number, weights and the threshold value of iteration of group；
S22 builds neural network structure, and generates a population w at randomi, with population wiRepresent the initial of neural network
Value,
wi=(wi1,wi2...,wis)T
Wherein,
S=pn+pm+p+m
N is the input neuron number of neural network, and p is the hidden layer neuron number of neural network, and m is neural network
Output neuron number；
S23 formulates evaluation parameter, an ANN Evolutionary parameter is created, by the particle newly obtained to neural network
Weights and threshold value are recalculated, until reaching convergent condition, by fitness value fitiIt is defined as,
Wherein, yi′For reality output, yiFor desired output, n represents population scale；
S24 calculates the position of each particle according to sample data, using the best position of particle as history optimum position；
S25 will redefine the position and speed of particle in iterative process each time, will calculate the new adaptation of particle
Then angle value determines individual extreme value；
The optimal solution of weights and threshold value is brought into neural network and is instructed in the condition of convergence for reaching setting by S26
Practice, until obtaining the prediction data of optimal output.
Optionally, in step s 4, include the following steps：
The history road conditions number that present road whether is preserved in local storage detected in the equipment of end side by S41
According to if so, then extracting the history road condition data of present road, and being marked according to temporal characteristics；If it is not, directly
Enter step S44；
S42, first calculates, and calculates the speed mean value of the history road condition data on present road；Second calculates, and calculates
The running time mean value of history road condition data on present road, and be stored in data are calculated in local storage；
S43, according to the label information in step S41, extraction present road is in history road condition data in different time periods
Speed mean value and/or running time mean value；
S44, the first prediction data that will be stored in local storage, is presented on the display device of the equipment of end side
On, the history road condition data of analysis data and/or end side based on high in the clouds particle cluster algorithm is as a result, to road traffic congestion feelings
Condition is predicted.
Optionally, the first traffic data includes appointing in real time running speed, residence time, running time, mileage
It is a kind of.
Optionally, the second traffic data is included in travel speed at signal lamp, running time, residence time, signal lamp
Period, green time, red time any one or more of.
Optionally, the equipment of end side include vehicle, the device that is arranged on vehicle.
Optionally, vehicle includes electric vehicle.
Optionally, vehicle includes automatic driving vehicle.
General particle cluster algorithm includes step：
Step 1：Initiation parameter condition sets the initial position of the number of particle, each particle；
Step 2：Calculate the fitness function of each particle；
Step 3：For each particle, the fitness function value being calculated is compared with more excellent position before,
If new fitness function is preferable, original more excellent position is replaced using it；
Step 4：Determining end condition, threshold value when set algorithm stops, if meeting end condition, algorithm terminates,
Otherwise continue to iterate to calculate position and speed, until meeting end condition requirement.
Neural network structure, learning rate and particle cluster algorithm scale etc. can be voluntarily arranged in those skilled in the art,
Such as three-layer neural network structure can be used, learning rate can be set as 0.02, and particle cluster algorithm population scale can use 50, iteration
Evolutionary generation takes 150 inferior, using established particle cluster algorithm, is applied to traffic data vehicle flowrate and predicts, it can be achieved that handing over
The prediction of passway congestion, remaining technical characteristic in the present embodiment, those skilled in the art can be according to actual conditions
Flexibly select and to meet different specific actual demands.However, obvious for those of ordinary skill in the art
It is：The present invention need not be carried out using these specific details.In other instances, it in order to avoid obscuring the present invention, does not retouch specifically
Well known algorithm is stated, method or system etc. limit technology in the claimed technical solution of claims of the present invention and protect
Within the scope of shield.
For embodiment of the method above-mentioned, for simple description, therefore it is all expressed as a series of combination of actions, still
Those skilled in the art should understand that the application is not limited by the described action sequence, because according to the application, it is a certain
A little steps can be performed in other orders or simultaneously.Secondly, it those skilled in the art should also know that, is retouched in specification
The embodiment stated belongs to preferred embodiment, necessary to involved action and unit not necessarily the application.
It will be appreciated by those of skill in the art that unit described in conjunction with the examples disclosed in the embodiments of the present disclosure and
Algorithm steps can be realized with the combination of electronic hardware or computer software and electronic hardware.These functions are actually with hard
Part or software mode execute, and depend on the specific application and design constraint of technical solution.Professional technician can be with
Distinct methods are used to realize described function each specific application, but this realization should not exceed the model of the present invention
It encloses.
Disclosed system, module and method, may be implemented in other ways.For example, device described above
Embodiment, only schematically, for example, the division of the unit, can be only a kind of division of logic function, it is practical to realize
When there may be another division manner, such as multiple units or component can be combined or can be integrated into another system, or
Some features can be ignored or not executed.Another point, shown or discussed mutual coupling or direct-coupling or communication
Connection is it may be said that by some interfaces, the INDIRECT COUPLING or communication connection of device or unit can be electrical, machinery or other
Form.
The unit that the discrete parts illustrates can be or can not also receive and be physically separated, and be shown as unit
Component can be or can not receive physical unit, you can be located at a place, or may be distributed over multiple network lists
In member.Some or all of unit therein can be selected according to the actual needs to realize the purpose of the scheme of the present embodiment.
It, can be with if the function is realized in the form of SFU software functional unit and when sold or used as an independent product
It is stored in a computer readable storage medium.Based on this understanding, technical scheme of the present invention is substantially right in other words
The part of part or the technical solution that the prior art contributes can be expressed in the form of software products, the calculating
Machine software product is stored in a storage medium, including some instructions are used so that a computer equipment (can be personal
Computer, server or network equipment etc.) it performs all or part of the steps of the method described in the various embodiments of the present invention.And
Storage medium above-mentioned includes：USB flash disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory
The various media that can store program code such as device (Random Access Memory, RAM), magnetic disc or CD.
One of ordinary skill in the art will appreciate that all or part of flow in the method for realization above-described embodiment, being can
It is completed with instructing relevant hardware by computer program, the program can be stored in computer read/write memory medium
In, the program is when being executed, it may include such as the flow of the embodiment of above-mentioned each method.Wherein, the storage medium can be magnetic
Dish, CD, ROM, RAM etc..
The above is only a preferred embodiment of the present invention, it should be understood that the present invention is not limited to described herein
Form is not to be taken as excluding other embodiments, and can be used for other combinations, modifications, and environments, and can be at this
In the text contemplated scope, modifications can be made through the above teachings or related fields of technology or knowledge.And those skilled in the art institute into
Capable modifications and changes do not depart from the spirit and scope of the present invention, then all should be in the protection domain of appended claims of the present invention
It is interior.
Claims (8)
1. a kind of traffic road congestion prediction technique based on particle cluster algorithm, which is characterized in that include the following steps：
S1 acquires the first traffic data, is uploaded to Cloud Server using the sensing system of end side, meanwhile, it is handed over using road
Traffic surveillance and control system at prong acquires the second traffic data, is uploaded to Cloud Server；
S2, the first traffic data and second traffic data described in cloud server, and it is based on modified particle swarm optiziation
It is handled, obtains the first prediction data；
S3, the equipment that first prediction data is transmitted to end side by Cloud Server；
The equipment of S4, the end side receive the data of Cloud Server passback, and save it in local storage, then
History road condition data whether is stored in the equipment detection local storage of end side, and feature is extracted to history road condition data
Data；
S5, Cloud Server obtain the traffic data of the sensing system acquisition of end side, obtain at intersection in real time in real time
Traffic surveillance and control system acquisition the second traffic data, become the first traffic data and the second traffic data as new input
Amount is based on the modified particle swarm optiziation continuous learning, continues to optimize first prediction data.
2. a kind of traffic road congestion prediction technique based on particle cluster algorithm according to claim 1, which is characterized in that
In step s 2, include the following steps：
S21 carries out initialization operation, the initialization includes population using the data acquired in step S1 as sample data
Scale, the number of iteration, weights and threshold value；
S22 builds neural network structure, and generates a population w at randomi, with population wiThe initial value of neural network is represented,
wi=(wi1,wi2...,wis)T
Wherein,
S=pn+pm+p+m
N is the input neuron number of neural network, and p is the hidden layer neuron number of neural network, and m is the defeated of neural network
Go out neuron number；
S23 formulates evaluation parameter, an ANN Evolutionary parameter is created, by the particle newly obtained to the weights of neural network
It is recalculated with threshold value, until reaching convergent condition, by fitness value fitiIt is defined as,
Wherein, yi′For reality output, yiFor desired output, n represents population scale；
S24 calculates the position of each particle according to sample data, using the best position of particle as history optimum position；
S25 will redefine the position and speed of particle in iterative process each time, will calculate the new fitness of particle
Then value determines individual extreme value；
The optimal solution of weights and threshold value is brought into neural network and is trained, directly in the condition of convergence for reaching setting by S26
To obtaining the prediction data of optimal output.
3. a kind of traffic road congestion prediction technique based on particle cluster algorithm according to claim 1, which is characterized in that
In step s 4, include the following steps：
The history road condition data that present road whether is preserved in local storage detected in the equipment of end side by S41,
If so, then extracting the history road condition data of present road, and it is marked according to temporal characteristics；If it is not, directly into
Enter step S44；
S42, first calculates, and calculates the speed mean value of the history road condition data on present road；Second calculates, and calculates current
The running time mean value of history road condition data on road, and be stored in data are calculated in local storage；
S43, according to the label information in step S41, speed of the extraction present road in history road condition data in different time periods
Mean value and/or running time mean value；
S44, the first prediction data that will be stored in local storage, is presented in the display device of the equipment of end side,
The history road condition data of analysis data and/or end side based on high in the clouds particle cluster algorithm is as a result, to road traffic congestion situation
It is predicted.
4. according to a kind of traffic road congestion prediction technique based on particle cluster algorithm of claim 1-3 any one of them,
It is characterized in that, the first traffic data includes any one of real time running speed, residence time, running time, mileage.
5. a kind of traffic road congestion prediction technique based on particle cluster algorithm according to claim 4, which is characterized in that
Second traffic data is included in travel speed at signal lamp, running time, the residence time, signal lamp cycle, green time, red
Lamp time any one or more of.
6. according to a kind of traffic road congestion prediction technique based on particle cluster algorithm of claim 1-3 any one of them,
It is characterized in that, the equipment of end side includes vehicle, the device that is arranged on vehicle.
7. according to a kind of traffic road congestion prediction technique based on particle cluster algorithm of claim 1-3 any one of them,
It is characterized in that, vehicle includes electric vehicle.
8. according to a kind of traffic road congestion prediction technique based on particle cluster algorithm of claim 1-3 any one of them,
It is characterized in that, vehicle includes automatic driving vehicle.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810045762.1A CN108376470A (en) | 2018-01-17 | 2018-01-17 | Traffic road congestion prediction technique based on particle cluster algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810045762.1A CN108376470A (en) | 2018-01-17 | 2018-01-17 | Traffic road congestion prediction technique based on particle cluster algorithm |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108376470A true CN108376470A (en) | 2018-08-07 |
Family
ID=63015150
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810045762.1A Withdrawn CN108376470A (en) | 2018-01-17 | 2018-01-17 | Traffic road congestion prediction technique based on particle cluster algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108376470A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10757485B2 (en) | 2017-08-25 | 2020-08-25 | Honda Motor Co., Ltd. | System and method for synchronized vehicle sensor data acquisition processing using vehicular communication |
| US11163317B2 (en) | 2018-07-31 | 2021-11-02 | Honda Motor Co., Ltd. | System and method for shared autonomy through cooperative sensing |
| US11181929B2 (en) | 2018-07-31 | 2021-11-23 | Honda Motor Co., Ltd. | System and method for shared autonomy through cooperative sensing |
| CN117373263A (en) * | 2023-12-08 | 2024-01-09 | 深圳市永达电子信息股份有限公司 | Traffic flow prediction method and device based on quantum pigeon swarm algorithm |
-
2018
- 2018-01-17 CN CN201810045762.1A patent/CN108376470A/en not_active Withdrawn
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10757485B2 (en) | 2017-08-25 | 2020-08-25 | Honda Motor Co., Ltd. | System and method for synchronized vehicle sensor data acquisition processing using vehicular communication |
| US11163317B2 (en) | 2018-07-31 | 2021-11-02 | Honda Motor Co., Ltd. | System and method for shared autonomy through cooperative sensing |
| US11181929B2 (en) | 2018-07-31 | 2021-11-23 | Honda Motor Co., Ltd. | System and method for shared autonomy through cooperative sensing |
| CN117373263A (en) * | 2023-12-08 | 2024-01-09 | 深圳市永达电子信息股份有限公司 | Traffic flow prediction method and device based on quantum pigeon swarm algorithm |
| CN117373263B (en) * | 2023-12-08 | 2024-03-08 | 深圳市永达电子信息股份有限公司 | Traffic flow prediction method and device based on quantum pigeon swarm algorithm |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108376470A (en) | Traffic road congestion prediction technique based on particle cluster algorithm | |
| Zhang et al. | Optimizing minimum and maximum green time settings for traffic actuated control at isolated intersections | |
| CN105702029B (en) | A kind of Expressway Traffic trend prediction method for considering space-time relationship at times | |
| CN103996289B (en) | A kind of flow-speeds match model and Travel Time Estimation Method and system | |
| CN104442825B (en) | A kind of Forecasting Methodology and system of electric automobile remaining driving mileage | |
| CN110361024A (en) | Utilize the dynamic lane grade automobile navigation of vehicle group mark | |
| CN108399763B (en) | Intersection traffic signal lamp control algorithm based on neural network | |
| CN103578273B (en) | A kind of road traffic state estimation method based on microwave radar data | |
| CN104574968B (en) | Determining method for threshold traffic state parameter | |
| CN108492557A (en) | Highway jam level judgment method based on multi-model fusion | |
| CN108335485A (en) | The method of major issue traffic dynamic emulation congestion prediction based on license plate identification data | |
| CN104464295A (en) | Intelligent traffic control method and device for elevated road entrance ramps based on video | |
| CN105551250B (en) | A kind of urban road intersection operating status method of discrimination based on interval clustering | |
| Roshan et al. | Adaptive traffic control with TinyML | |
| CN108682147A (en) | A kind of highway traffic congestion dredges decision-making technique | |
| CN110400462A (en) | Track traffic for passenger flow monitoring and pre-alarming method and its system based on fuzzy theory | |
| CN108364490A (en) | Municipal highway transit system vehicle runs regulation and control method | |
| Grover et al. | Traffic control using V-2-V based method using reinforcement learning | |
| CN104599500A (en) | Grey entropy analysis and Bayes fusion improvement based traffic flow prediction method | |
| CN104933877B (en) | Method for controlling bus priority and system based on bus platform parking information | |
| CN109559506A (en) | Urban road discrete traffic flow delay time at stop prediction technique under a kind of rainy weather | |
| CN108334079A (en) | Pilotless automobile method for obtaining road condition information in real time | |
| Hu et al. | A novel intelligent traffic light control scheme | |
| Heydecker | Objectives, stimulus and feedback in signal control of road traffic | |
| CN108364491A (en) | Driver information based reminding method based on vehicle flowrate prediction |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| WW01 | Invention patent application withdrawn after publication | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20180807 |