CN107545757A - Urban road flow rate measuring device and method based on Car license recognition - Google Patents
Urban road flow rate measuring device and method based on Car license recognition Download PDFInfo
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Abstract
The invention discloses a kind of urban road flow rate measuring device based on Car license recognition, and it includes traffic information collection module, traffic information management module, road section processing module, section flow relocity calculation module, traffic behavior evaluation module, traffic status prediction module and traffic-information service module.The urban road flow rate measuring device based on Car license recognition of the present invention, the measurement of urban road flow velocity can be realized, traffic behavior is analyzed in real time and traffic status prediction, and can be the offer intelligent navigation of smart city intelligent automobile, enable the vehicle to intelligent selection most efficiently path, and real arrival time is provided for vehicle.The invention also discloses a kind of urban road flow-speed measurement method based on Car license recognition.
Description
Technical field
The present invention relates to intelligent transportation field, particularly a kind of urban road flow velocity measurement based on Car license recognition
Apparatus and method, belong to intelligent transportation service field.
Background technology
With the increase of car ownership and the magnitude of traffic flow, urban road infrastructure has been difficult to meet quickly to increase
Long transport need, traffic congestion frequently occur, and cause traffic accident to increase, and bring loss economically,
Even injure the life security of people.It is simple by extension road network due to the limitation of urban road area ratio
It is difficult to solves traffic jam issue.In this context, to make traveler efficiently utilize existing roadnet,
The traffic behavior of timely prediction urban road is needed, real time information is provided for intelligent navigation.Its
In, real-time traffic states analysis and prediction, the selection in direct relation automobile navigation path.
The data source of existing road flow relocity calculation and its short-term prediction mainly include fixed point Coil Detector and
Floating vehicle data acquisition etc..Due to pinpoint coil device routes coverage is smaller and coil is easily damaged,
The quantity of GPS Floating Cars often problems such as deficiency, cause flow relocity calculation and prediction not accurate enough, it is impossible to
Meets the needs of intelligent navigation.
Traditional navigation algorithm is that shortest path or as far as possible selection backbone, road are found between initial point, terminal
The input of line planning algorithm is route length, major trunk roads are preferential, evades the static boundary condition such as charge station, is counted
The navigation way of calculation can not be updated according to the real-time change of urban traffic status.
License plate recognition technology (ANPR：Automatic number plate recognition) it is that computer regards
A kind of application of the frequency image recognition technology in License Plate Identification, can be by the license plate in motion from again
Extract and identify in miscellaneous background, pass through license plate retrieving, image preprocessing, feature extraction, characters on license plate
The technologies such as identification, identify the information such as license plate number and color.With Digital Image Processing, pattern-recognition and calculating
The technologies such as machine vision it is perfect, car plate capture rate and discrimination significantly improve, based on license plate identification data
Traffic information collection technology is arisen at the historic moment.Compared to other traffic information collection technologies, based on license plate identification data
Traffic information collection technology have the advantages that work continuity is strong, data accuracy is high, detection sample size it is big.
Therefore the present invention proposes a kind of urban road flow rate measuring device and method based on Car license recognition, it
Intelligent navigation service can be provided for smart city intelligent automobile.
The content of the invention
The present invention seeks to propose a kind of urban road flow rate measuring device and method based on Car license recognition, energy
Enough realize that urban road flow velocity measures, traffic behavior is analyzed in real time and traffic status prediction, and can be wisdom city
The offer intelligent navigation of city's intelligent automobile, enable the vehicle to intelligent selection most efficiently path, and being carried for vehicle
For real arrival time.
The present invention solves technical problem and adopted the following technical scheme that：A kind of urban road stream based on Car license recognition
Speed measuring device, it includes：
Traffic information collection module, its collection vehicle is by telecommunication flow information during monitoring section, the traffic
Stream information includes license plate image, time, camera number, carriageway type and travel direction；
Traffic information management module, its license plate image to collection are identified, license plate number after identification and when
Between, the traffic established together using license plate number and camera number as keyword of camera number and travel direction believes
Database is ceased, the form of the traffic information database is { license plate number；Camera number；Time；Traveling side
To；
Road section processing module, the distribution situation and urban road of its comprehensive analysis traffic information collection module
Web frame form, target road section is chosen with reference to crowded place distribution situation；By numerical map and traffic
Information database, target road section information table is established, wherein, the element in target road section information table includes starting point
Camera number, finish film machine numbering, section distance, section number of track-lines, the section gradient and signal lamp week
Phase；
Section flow relocity calculation module, using target road section information table as input, extracted from traffic information database
The match information of section is monitored by target road section two；Calculated for each sample vehicle by the section
Speed, according to the distribution situation of the speed of sample vehicle, rational sample vehicle is chosen, calculates road flow velocity；
Traffic behavior evaluation module, calculated based on single section, complete the stream in all sections of whole road network
Speed is calculated, and traffic behavior is evaluated from two levels in section and road network；
Traffic status prediction module, by the excavation to specific road section contemporaneous data, find out traffic flow parameter with
The rule or relation of time, short-term forecast is carried out to section mean flow rate according to this rule, it is short with mean flow rate
When prediction result for input, obtain the short term variations trend of traffic behavior；
Traffic-information service module, real-time release road network, the traffic state information in section and short-term prediction letter
Breath, there is provided intelligent navigation calculates, and the intelligent navigation route of real-time delivery optimization is to vehicle termination.
Optionally, match information includes car plate used by the section flow relocity calculation module, drives into the time,
Roll away from the time, starting point camera number and finish film machine numbering.
Optionally, the traffic behavior evaluation module is handed over using road-section average flow velocity as evaluation index according to section
Logical state classification standard provides road section traffic volume state grade, or using the evaluation result of road section traffic volume state to be defeated
Enter, the traffic behavior in all observation sections of simultaneous display, sketches the contours of road network traffic behavior on numerical map.
The present invention solves technical problem and also adopted the following technical scheme that：A kind of urban road based on Car license recognition
Flow-speed measurement method, it comprises the following steps：
Step 1, traffic information collection module collection vehicle is described by telecommunication flow information during monitoring section
Telecommunication flow information includes license plate image, time, camera number, carriageway type and travel direction；
Step 2, the license plate image of collection is identified traffic information management module, the license plate number after identification
With establishing the friendship using license plate number and camera number as keyword together with time, camera number and travel direction
Logical information database, the form of the traffic information database is { license plate number；Camera number；Time；OK
Sail direction }；
Step 3, the distribution situation of road section processing module comprehensive analysis traffic information collection module and city
Road network shape, target road section is chosen with reference to crowded place distribution situation；By numerical map with
Traffic information database, target road section information table is established, wherein, the element in the target road section information table
Including starting point camera number, finish film machine numbering, section distance, section number of track-lines, the section gradient and
Signal lamp cycle；
Step 4, section flow relocity calculation module, using target road section information table as input, from traffic information data
The match information that vehicle monitors section by target road section two is extracted in storehouse；Calculated for each sample vehicle
By the speed in the section, according to the distribution situation of the speed of sample vehicle, rational sample vehicle, meter are chosen
Calculate road flow velocity；
Step 5, traffic behavior evaluation module, calculated based on single section, complete all of whole road network
The flow relocity calculation in section, and traffic behavior is evaluated from two levels in section and road network；
Step 6, traffic status prediction module, by the excavation to specific road section contemporaneous data, traffic is found out
Parameter and the rule or relation of time are flowed, short-term forecast is carried out to section mean flow rate according to this rule, with flat
Equal flow velocity short-term prediction result is input, obtains the short term variations trend of traffic behavior；
Step 7, traffic-information service module, real-time release road network, the traffic state information in section and short
When information of forecasting, there is provided intelligent navigation calculate, and real-time delivery optimization intelligent navigation route to vehicle
Terminal.
Optionally, match information includes car plate used by the section flow relocity calculation module, drives into the time,
Roll away from the time, starting point camera number and finish film machine numbering.
Optionally, the traffic behavior evaluation module is handed over using road-section average flow velocity as evaluation index according to section
Logical state classification standard provides road section traffic volume state grade, or using the evaluation result of road section traffic volume state to be defeated
Enter, the traffic behavior in all observation sections of simultaneous display, sketches the contours of road network traffic behavior on numerical map.
The present invention has the advantages that：(1) this method can provide real-time urban road each section flow velocity
Situation and road net traffic state；(2) this method is entered by Car license recognition mode monitoring section each to urban road
Row traffic information collection, the flow relocity calculation source data higher than coil and GPS Floating Car acquisition precisions can be obtained；
(3) city capillary branch line road network can be given full play to based on ANPR (car plate automatic detection and identification)
The traffic capacity, mitigate the peak burdens of trunk roads；(4) this method can provide for smart city intelligent vehicle
Most efficiently path, and arrival time can be estimated；(5) it this method provide urban road real-time traffic shape
State and prediction, the utilization rate of existing road network can be effectively improved, alleviate traffic congestion；(6) this method can be with
The relevant informations such as traffic weather, category of roads and traffic control are introduced, are commented for flow relocity calculation, traffic behavior
Valency and traffic behavior classification prediction, improve traffic prewarning information accuracy, have good economic benefit and
Social benefit.
Brief description of the drawings
Fig. 1 is the urban road flow-speed measurement method schematic diagram based on Car license recognition of the present invention；
Fig. 2 is the structural representation of the urban road flow rate measuring device based on Car license recognition of the present invention；
Fig. 3 is the flow chart of the urban road flow-speed measurement method based on Car license recognition of the present invention.
Mark is illustrated as in figure：111- electronic polices；112- intelligent bayonets；113- traffic monitorings；114- days
Net monitoring；115- fiber optical transceivers；131- core switch；132- management servers；133- storage arrays；
134-Web servers；135- operates terminal.
Embodiment
Technical scheme is further elaborated with reference to embodiment and accompanying drawing.
Embodiment 1
A kind of urban road flow rate measuring device based on Car license recognition is present embodiments provided, i.e., based on current
The license plate image acquisition processing system of road network realizes that road flow velocity measures, and it includes：
Traffic information collection module, collection vehicle is by license plate image during monitoring section, time, video camera
The telecommunication flow informations such as numbering, carriageway type, travel direction.
The traffic information collection module of the present embodiment can be realized by car plate data acquisition equipment, you can with
Including electronic police, intelligent bayonet, traffic monitoring, the monitoring of day net and fiber optical transceiver etc.；As shown in figure 1,
The traffic information collection module can gather the information of vehicles of the monitoring section by imaging node A, pass through
Image the information of vehicles of node B monitoring section, including license plate image, pass through time of the monitoring section etc..
Realize the collection of license plate image and other data.
Traffic information management module, carries out Car license recognition to the license plate image of collection, and with other synchronous acquisitions
Information, such as the information integration such as time, camera number, travel direction establish with license plate number and video camera
Numbering is the traffic information database of keyword, and the form of the traffic information database is { license plate number；Shooting
Machine is numbered；Time；Travel direction }.
Road section processing module, the distribution situation of comprehensive analysis traffic information collection module and urban road network
Structure type, target road section is chosen with reference to crowded place distribution situation；Believe by numerical map and traffic
Database is ceased, establishes target road section information table, wherein, the element in target road section information table is taken the photograph including starting point
Camera numbering, finish film machine numbering, section distance, section number of track-lines, the section gradient and signal lamp cycle
Etc. content；
Section flow relocity calculation module, using target road section information table as input, extracted from traffic information database
The match information of section, such as car plate are monitored by target road section two, the time is driven into, rolls the time away from, rise
Point camera number and finish film machine numbering etc.；The speed by the section is calculated for each sample vehicle,
According to the distribution situation of the speed of sample vehicle, rational sample vehicle is chosen, calculates road flow velocity；
Traffic behavior evaluation module, calculated based on single section, complete the stream in all sections of whole road network
Speed calculates, and traffic behavior is evaluated from two levels in section and road network, i.e., using road-section average flow velocity as
Evaluation index, road section traffic volume state grade is provided according to road section traffic volume state classification standard, such as, unimpeded,
Crowded, congestion etc.；Further, can also be using the evaluation result of road section traffic volume state as input, in numeral
The traffic behavior in all observation sections of simultaneous display, sketches the contours of road network traffic behavior on map；
Traffic status prediction module, by the excavation to specific road section contemporaneous data, find out traffic flow parameter with
The rule or relation of time, short-term forecast is carried out to section mean flow rate according to this rule, it is short with mean flow rate
When prediction result for input, obtain the short term variations trend of traffic behavior；
Traffic-information service module, real-time release road network, the traffic state information in section and short-term prediction letter
Breath, there is provided intelligent navigation calculates, and the intelligent navigation route of real-time delivery optimization is to vehicle termination.
Embodiment 2
Reference picture 2, a kind of urban road flow rate measuring device based on Car license recognition is present embodiments provided,
Said from the angle of hardware, the urban road flow rate measuring device based on Car license recognition can include：Car
Board data acquisition equipment, trackside private network communication network and Back end data administrative center.
Car plate data acquisition equipment is connected to Back end data administrative center by trackside private network communication network, completes
License plate image data and other data (such as time, camera number, carriageway type, travel direction etc. are handed over
Through-flow information) transmission, Back end data administrative center realizes Car license recognition, traffic based on license plate image data
Information processing and traffic status prediction.
The car plate data acquisition equipment include electronic police 111, intelligent bayonet 112, traffic monitoring 113,
Its net monitoring 114 grade car plate harvesters and network access equipment fiber optical transceiver 115 etc., all car plates are adopted
Acquisition means are connected with fiber optical transceiver, by the license plate image data of the vehicle of collection be locally stored and on
Pass to trackside private network communication network.
Described Back end data administrative center includes core switch 131, management server 132, storage array
133rd, Web server 134 and operation terminal 135 etc.；Storage array 133 is used to store core switch 131
The license plate image initial data read from trackside private network communication network；Management server 132 is used to realize car plate
Identification, disorder data recognition and processing, Data Integration and storage, traffic behavior evaluation and prediction etc.；Operation
Terminal 135 is used for artificial treatment, examines transport information；Web server 134 provide Traffic information demonstration and
Query function.
Embodiment 3
A kind of urban road flow-speed measurement method based on Car license recognition is present embodiments provided, it can be used
The urban road flow velocity Vehicular system based on Car license recognition described in embodiment 1, and comprise the following steps：
Step 1, traffic information collection module collection vehicle by monitoring section when license plate image, the time,
The telecommunication flow informations such as camera number, carriageway type, travel direction.
As shown in figure 1, the traffic information collection module can gather the monitoring section by imaging node A
Information of vehicles, the information of vehicles of the monitoring section by imaging node B, including car plate, passage time etc..
And when network failure, above-mentioned telecommunication flow information is locally stored, after network recovery, data are certainly
Dynamic to resume, all traffic information collection modules are locally stored that form is identical, and network uploaded format is also identical；
Step 2, traffic information management module the license plate image of collection is carried out Car license recognition and with it is other synchronous
The information integration of collection establish using license plate number, camera number as keyword traffic information database, it is described
The form of traffic information database is { license plate number；Camera number；Time；Travel direction }.The present embodiment
In, the traffic information management module may operate in Back end data administrative center, and the Back end data
Administrative center includes picture recognition module, to identify license plate image.
Step 3, the distribution situation of road section processing module comprehensive analysis traffic information collection module and city
Road network shape, target road section is chosen with reference to crowded place distribution situation；By numerical map with
Traffic information database, target road section information table is established, wherein, the element in the target road section information table
Including starting point camera number, finish film machine numbering, section distance, section number of track-lines, the section gradient and
The contents such as signal lamp cycle.
In the present embodiment, as shown in figure 1, establish shooting node A and image node B information, and to taking the photograph
As node A and shooting node B be associated, formed a target analysis section.
Step 4, section flow relocity calculation module, using target road section information table as input, from traffic information data
The match information that vehicle monitors section by target road section two, such as car plate are extracted in storehouse, drives into the time,
Roll away from the time, starting point camera number and finish film machine numbering etc.；Calculate and pass through for each sample vehicle
The speed in the section, according to the distribution situation of the speed of sample vehicle, rational sample vehicle is chosen, calculates road
Road flow velocity.
As shown in figure 1, an effective sample information of vehicles between shooting node A and shooting node B, i.e., { lucky
AE2170, t5, t8, A node camera number, B node camera number }, led to according to the sample vehicle
Cross the distance between time (t8-t5) and the shooting node A and shooting node B in the section and calculate the sample vehicle
Bicycle speed.And the speed of multiple effective sample vehicles by the section is handled, for example use
Normal distribution, irrational sample is rejected, the flow velocity in the section is calculated using remaining effective sample.
Step 5, traffic behavior evaluation module, calculated based on single section, complete all of whole road network
The flow relocity calculation in section, and traffic behavior is evaluated from two levels in section and road network；Put down with section
Equal flow velocity is evaluation index, and road section traffic volume state grade is provided according to road section traffic volume state classification standard, such as,
Unimpeded, crowded, congestion etc.；Further, can also using the evaluation result of road section traffic volume state as input,
The traffic behavior in all observation sections of simultaneous display, sketches the contours of road network traffic behavior on numerical map；
Step 6, traffic status prediction module, by the excavation to specific road section contemporaneous data, traffic is found out
Parameter and the rule or relation of time are flowed, short-term forecast is carried out to section mean flow rate according to this rule, with flat
Equal flow velocity short-term prediction result is input, obtains the short term variations trend of traffic behavior；Use statistical forecast
Algorithm carries out short-term forecast to section mean flow rate；The starting point of statistical forecast algorithm is the friendship specific road section
Through-flow parameter regards the dependent variable of time as, and rule therein or pass are found out by the excavation to same period historical data
System；
Step 7, traffic-information service module, real-time release road network, the traffic state information in section and short
When information of forecasting, there is provided intelligent navigation calculate, and real-time delivery optimization intelligent navigation route to vehicle
Terminal；Use B/S patterns, in a manner of WEB service, to traffic trip person provide real-time section and
Road net traffic state, to traffic administration person provide transport information monitor in real time, statistical analysis and historical query etc.
Service.
The sequencing of above example only for ease of describing, does not represent the quality of embodiment.
Finally it should be noted that：The above embodiments are merely illustrative of the technical solutions of the present invention, rather than it is limited
System；Although the present invention is described in detail with reference to the foregoing embodiments, one of ordinary skill in the art
It should be understood that：It can still modify to the technical scheme described in foregoing embodiments, or to it
Middle some technical characteristics carry out equivalent substitution；And these modifications or replacement, do not make appropriate technical solution
Essence departs from the spirit and scope of various embodiments of the present invention technical scheme.
Claims (6)
- A kind of 1. urban road flow rate measuring device based on Car license recognition, it is characterised in that including：Traffic information collection module, its collection vehicle is by telecommunication flow information during monitoring section, the traffic Stream information includes license plate image, time, camera number, carriageway type and travel direction；Traffic information management module, its license plate image to collection are identified, license plate number after identification and when Between, the traffic established together using license plate number and camera number as keyword of camera number and travel direction believes Database is ceased, the form of the traffic information database is { license plate number；Camera number；Time；Traveling side To；Road section processing module, the distribution situation and urban road of its comprehensive analysis traffic information collection module Web frame form, target road section is chosen with reference to crowded place distribution situation；By numerical map and traffic Information database, target road section information table is established, wherein, the element in target road section information table includes starting point Camera number, finish film machine numbering, section distance, section number of track-lines, the section gradient and signal lamp week Phase；Section flow relocity calculation module, using target road section information table as input, extracted from traffic information database The match information of section is monitored by target road section two；Calculated for each sample vehicle by the section Speed, according to the distribution situation of the speed of sample vehicle, rational sample vehicle is chosen, calculates road flow velocity；Traffic behavior evaluation module, calculated based on single section, complete the stream in all sections of whole road network Speed is calculated, and traffic behavior is evaluated from two levels in section and road network；Traffic status prediction module, by the excavation to specific road section contemporaneous data, find out traffic flow parameter with The rule or relation of time, short-term forecast is carried out to section mean flow rate according to this rule, it is short with mean flow rate When prediction result for input, obtain the short term variations trend of traffic behavior；Traffic-information service module, real-time release road network, the traffic state information in section and short-term prediction letter Breath, there is provided intelligent navigation calculates, and the intelligent navigation route of real-time delivery optimization is to vehicle termination.
- 2. the urban road flow rate measuring device according to claim 1 based on Car license recognition, its feature It is, match information includes car plate used by the section flow relocity calculation module, the time is driven into, when rolling away from Between, starting point camera number and finish film machine numbering.
- 3. the urban road flow rate measuring device according to claim 2 based on Car license recognition, its feature It is, the traffic behavior evaluation module is using road-section average flow velocity as evaluation index, according to road section traffic volume state Criteria for classification provides road section traffic volume state grade, or using the evaluation result of road section traffic volume state as input, The traffic behavior in all observation sections of simultaneous display, sketches the contours of road network traffic behavior on numerical map.
- 4. a kind of urban road flow-speed measurement method based on Car license recognition, it is characterised in that including following step Suddenly：Step 1, traffic information collection module collection vehicle is described by telecommunication flow information during monitoring section Telecommunication flow information includes license plate image, time, camera number, carriageway type and travel direction；Step 2, the license plate image of collection is identified traffic information management module, the license plate number after identification With establishing the friendship using license plate number and camera number as keyword together with time, camera number and travel direction Logical information database, the form of the traffic information database is { license plate number；Camera number；Time；OK Sail direction }；Step 3, the distribution situation of road section processing module comprehensive analysis traffic information collection module and city Road network shape, target road section is chosen with reference to crowded place distribution situation；By numerical map with Traffic information database, target road section information table is established, wherein, the element in the target road section information table Including starting point camera number, finish film machine numbering, section distance, section number of track-lines, the section gradient and Signal lamp cycle；Step 4, section flow relocity calculation module, using target road section information table as input, from traffic information data The match information that vehicle monitors section by target road section two is extracted in storehouse；Calculated for each sample vehicle By the speed in the section, according to the distribution situation of the speed of sample vehicle, rational sample vehicle, meter are chosen Calculate road flow velocity；Step 5, traffic behavior evaluation module, calculated based on single section, complete all of whole road network The flow relocity calculation in section, and traffic behavior is evaluated from two levels in section and road network；Step 6, traffic status prediction module, by the excavation to specific road section contemporaneous data, traffic is found out Parameter and the rule or relation of time are flowed, short-term forecast is carried out to section mean flow rate according to this rule, with flat Equal flow velocity short-term prediction result is input, obtains the short term variations trend of traffic behavior；Step 7, traffic-information service module, real-time release road network, the traffic state information in section and short When information of forecasting, there is provided intelligent navigation calculate, and real-time delivery optimization intelligent navigation route to vehicle Terminal.
- 5. the urban road flow-speed measurement method according to claim 4 based on Car license recognition, its feature It is, match information includes car plate used by the section flow relocity calculation module, the time is driven into, when rolling away from Between, starting point camera number and finish film machine numbering.
- 6. the urban road flow-speed measurement method according to claim 5 based on Car license recognition, its feature It is, the traffic behavior evaluation module is using road-section average flow velocity as evaluation index, according to road section traffic volume state Criteria for classification provides road section traffic volume state grade, or using the evaluation result of road section traffic volume state as input, The traffic behavior in all observation sections of simultaneous display, sketches the contours of road network traffic behavior on numerical map.
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Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000057482A (en) * | 1998-08-07 | 2000-02-25 | Nippon Telegr & Teleph Corp <Ntt> | Method and device for predicting traffic condition, and recording medium recorded with traffic condition prediction program |
| JP2001304891A (en) * | 2000-04-27 | 2001-10-31 | Toshiba Corp | Predicting system for road traffic state, on-vehicle navigation system, and predicting method for road state |
| JP2003067883A (en) * | 2002-06-10 | 2003-03-07 | Hitachi Ltd | Method for predicting traffic situation in wide road traffic space |
| JP2004005726A (en) * | 2003-07-24 | 2004-01-08 | Fujitsu Ltd | Traffic flow monitoring system for moving-body |
| CN1937001A (en) * | 2006-10-19 | 2007-03-28 | 上海交通大学 | Method for obtaining everage speed of city road section traffic flow |
| CN101814237A (en) * | 2009-12-04 | 2010-08-25 | 四川川大智胜软件股份有限公司 | Road condition identifier based on license plate recognition technology and identification method thereof |
| CN101853573A (en) * | 2010-05-19 | 2010-10-06 | 北京科技大学 | City expressway short-time traffic information predicting system and method |
| CN101929864A (en) * | 2009-06-26 | 2010-12-29 | 上海市上海中学 | Road traffic navigation system and navigation route generation method |
| CN103186982A (en) * | 2011-12-28 | 2013-07-03 | 南京理工大学常熟研究院有限公司 | Intelligent access system for public security |
| CN104464304A (en) * | 2014-12-25 | 2015-03-25 | 北京航空航天大学 | Urban road vehicle running speed forecasting method based on road network characteristics |
| CN105118293A (en) * | 2015-09-16 | 2015-12-02 | 东南大学 | Road section traffic speed short-term prediction method in view of long-term equilibrium relationship |
-
2016
- 2016-06-24 CN CN201610464726.XA patent/CN107545757B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000057482A (en) * | 1998-08-07 | 2000-02-25 | Nippon Telegr & Teleph Corp <Ntt> | Method and device for predicting traffic condition, and recording medium recorded with traffic condition prediction program |
| JP2001304891A (en) * | 2000-04-27 | 2001-10-31 | Toshiba Corp | Predicting system for road traffic state, on-vehicle navigation system, and predicting method for road state |
| JP2003067883A (en) * | 2002-06-10 | 2003-03-07 | Hitachi Ltd | Method for predicting traffic situation in wide road traffic space |
| JP2004005726A (en) * | 2003-07-24 | 2004-01-08 | Fujitsu Ltd | Traffic flow monitoring system for moving-body |
| CN1937001A (en) * | 2006-10-19 | 2007-03-28 | 上海交通大学 | Method for obtaining everage speed of city road section traffic flow |
| CN101929864A (en) * | 2009-06-26 | 2010-12-29 | 上海市上海中学 | Road traffic navigation system and navigation route generation method |
| CN101814237A (en) * | 2009-12-04 | 2010-08-25 | 四川川大智胜软件股份有限公司 | Road condition identifier based on license plate recognition technology and identification method thereof |
| CN101853573A (en) * | 2010-05-19 | 2010-10-06 | 北京科技大学 | City expressway short-time traffic information predicting system and method |
| CN103186982A (en) * | 2011-12-28 | 2013-07-03 | 南京理工大学常熟研究院有限公司 | Intelligent access system for public security |
| CN104464304A (en) * | 2014-12-25 | 2015-03-25 | 北京航空航天大学 | Urban road vehicle running speed forecasting method based on road network characteristics |
| CN105118293A (en) * | 2015-09-16 | 2015-12-02 | 东南大学 | Road section traffic speed short-term prediction method in view of long-term equilibrium relationship |
Non-Patent Citations (1)
| Title |
|---|
| 徐涛等: "面向城市道路交通状态估计的数据融合研究", 《计算机工程与应用》 * |
Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108364471A (en) * | 2018-04-24 | 2018-08-03 | 浙江方大智控科技有限公司 | Freight planning management method and system based on intelligent traffic light OD information inspections |
| CN108376472A (en) * | 2018-04-24 | 2018-08-07 | 浙江方大智控科技有限公司 | Section management method based on intelligent traffic light OD information analyses and system |
| CN108364471B (en) * | 2018-04-24 | 2023-07-07 | 浙江方大智控科技有限公司 | Freight planning management method and system based on intelligent traffic light OD information inspection |
| CN108961743A (en) * | 2018-05-28 | 2018-12-07 | 马鞍山马文化传媒有限公司 | A kind of advertisement push system based on road traffic |
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| CN111445700B (en) * | 2020-06-02 | 2021-06-11 | 苏州大学 | Regional running and stopping vehicle monitoring method based on license plate identification data |
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| CN113593229A (en) * | 2021-07-28 | 2021-11-02 | 广州时空位置网科学技术研究院有限公司 | Urban area traffic big data analysis system based on positioning system |
| CN113593229B (en) * | 2021-07-28 | 2022-06-21 | 广州时空位置网科学技术研究院有限公司 | Urban area traffic big data analysis system based on positioning system |
| CN114299727A (en) * | 2021-12-28 | 2022-04-08 | 杭州电子科技大学 | Traffic flow prediction system based on Internet of things and edge computing and cloud platform |
| CN114356939A (en) * | 2022-03-21 | 2022-04-15 | 科大天工智能装备技术（天津）有限公司 | Street lamp intelligent management method and device applied to urban space and storage medium |
| CN114356939B (en) * | 2022-03-21 | 2022-05-24 | 科大天工智能装备技术（天津）有限公司 | Street lamp intelligent management method and device applied to urban space and storage medium |
| CN116994441A (en) * | 2023-09-26 | 2023-11-03 | 北京清创美科环境科技有限公司 | Traffic flow information data processing method and system |
| CN116994441B (en) * | 2023-09-26 | 2023-12-12 | 北京清创美科环境科技有限公司 | Traffic flow information data processing method and system |
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