CN105139670A - Video-based regional self-optimizing signal control method and apparatus - Google Patents
Video-based regional self-optimizing signal control method and apparatus Download PDFInfo
- Publication number
- CN105139670A CN105139670A CN201510443091.0A CN201510443091A CN105139670A CN 105139670 A CN105139670 A CN 105139670A CN 201510443091 A CN201510443091 A CN 201510443091A CN 105139670 A CN105139670 A CN 105139670A
- Authority
- CN
- China
- Prior art keywords
- traffic
- crossing
- road network
- video
- signal
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Abstract
The invention provides a video-based regional self-optimizing signal control method, and mainly relates to the regional road network intersection signal optimization control field. Through comprehensive applications of a novel video vehicle detection device, dynamic detection of traffic states of multiple intersections of a regional road network as well as signal optimization can be achieved. According to the technical scheme, the method includes the steps: installing video equipment; performing data acquisition and communication; processing and calculating regional road network self-optimizing signals; and issuing and controlling signal instructions. The invention also provides a video-based regional self-optimizing signal control apparatus. Through adoption of an active video technology, the real-time traffic states of the intersections of the regional road network can be accurately detected, a regional optimized signal control scheme can be made, real-time decision and emergency handling information can be provided to traffic management and control, and the operating efficiency and the service level of regional road network traffic can be improved.
Description
Technical field
The present invention relates to the traffic signal optimization control field at the multiple crossing of Regional Road Network, specifically a kind of region self-optimizing signal control method based on video and device.
Background technology
City friendship is blocked up and accident takes place frequently day by day, particularly section, crossing event of blocking up is serious, traffic control system that is advanced, that be suitable for is one of the most effective approach solving urban traffic congestion, and traffic signalization is the core of traffic control system, Regional Road Network traffic signal optimization controls to play regional traffic induction advantage to greatest extent, improves road traffic operational efficiency.
Video encoder server technology is that the road by blocking up at complex or easily formed installs video capture device, passing automobile quantity, speed, queue length are detected, the data collected are passed back by wired or wireless network the technology that server-centric carries out processing, dynamic traffic signal control can be carried out by the traffic parameter of Real-time Collection, realize effective rule induction of traffic flow, reduce traffic congestion to greatest extent.
At present, signal control method mainly comprises timing controlled, multi-period control, induction control and adaptive control etc., traditional model algorithm too stiff the change according to certain traffic parameter setting threshold value carry out signal optimizing, system can be caused the erroneous judgement of state; The present invention proposes a kind of region self-optimizing signal control method based on video, the real-time detection of index and comprehensive analysis is run by crossing, the signal extracting the multiple crossing of Regional Road Network controls self-optimizing algorithm, greatly can improve the operational efficiency of Regional Road Network road traffic.
Summary of the invention
A kind of region self-optimizing signal control method based on video and device, the equipment used in the method comprises video equipment, data communications equipment, data store and standardized server, Regional Road Network self-optimizing processing server signal and signal issue terminal equipment, between described each equipment, signal connects in order, and the method comprises the steps:
(1) in crossing, each importer upwards installs video encoder server equipment, regulates the angle of detection faces, determines that detection zone and blind area critical line are positioned at 10-20 rice before stop line;
(2) crossing of Regional Road Network is numbered in order, then the detecting device of each crossing is numbered according to clockwise direction, the section numbering belonging to crossing and detecting device are numbered and binds;
(3) by video encoder server equipment, the traffic flow of Real-time Collection detector segments and car speed parameter information, described parameter information is through data communications equipment, and real-time returned data stores and standardized server, carries out real-time data memory and standardization;
(4) extract the real time traffic data of storage server, calculate detector segments average traffic current density parameter, and calculate real-time section traffic operation index according to average traffic current density;
(5) according to the traffic circulation exponential sum section road class attribute of each importer of crossing to section, polymerization calculates intersection traffic and runs index, run by intersection traffic the cycle that index-signal period relational model calculates integrative design intersection, and then draw the signal period of each crossing;
(6) according to the traffic circulation exponential sum section road class attribute of each importer of crossing to section, calculate crossing arterial traffic and run index, COMPREHENSIVE CALCULATING Regional Road Network intersection traffic runs exponential average;
(7) run exponential average-split model based on Regional Road Network intersection traffic, according to main line priority principle, the split of each crossing main line of zoning road network, then calculates each crossing main signal and controls red light and green time;
(8) utilize signal issue terminal equipment, served by calling data bank interface, sent to by the real-time parameter of integrative design intersection signal to control lamp, control lamp by signal and each crossing traffic of Regional Road Network is dynamically induced.
A kind of region self-optimizing signal control method based on video and device, it is characterized in that: the traffic circulation exponential model based on video builds, traffic circulation index-signal period relational model builds, and runs exponential average-split model with Regional Road Network intersection traffic;
The described traffic circulation exponential model based on video builds, and comprises road section traffic volume and runs index, intersection traffic operation index, crossing arterial traffic operation index and Regional Road Network traffic circulation exponential average 4 parts;
(A) extract traffic flow data and the speed data in each track of video detector segments, calculate average traffic stream parameter and the average velocity parameter of detector segments at Spatial Dimension and time dimension aspect respectively;
Average traffic stream parameter
pass through formula
calculate, wherein, n is track, place, and N is the total number in track in section, q
nit is the traffic flow in the n-th track; Average velocity parameter v
npass through
calculate, wherein, v
nbe the speed in the n-th track,
for the average velocity of unit granularity period;
(B) average traffic current density parameter
pass through formula
calculate;
(C) road section traffic volume operation index RTPI passes through formula
(D) intersection traffic operation index ITPI passes through formula
ITPI=RTPI
1* ω
1+ RTPI
2* ω
2+ ... ,+RTPI
j* ω
jcalculate, wherein ω
1, ω
2..., ω
jfor each importer to weighting coefficient;
(E) crossing arterial traffic operation index IATPI passes through publicity
IATPI=RTPI
1* ω
1+ RTPI
2* ω
2+ ... ,+RTPI
h* ω
hcalculate, wherein ω
1, ω
2..., ω
hfor the weighting coefficient in main line section, crossing;
Described traffic circulation index-signal period relational model builds, and signal period parameter C=T*ITPI/10, T are preset signals cycle parameters;
Described Regional Road Network intersection traffic runs exponential average-split model construction, and Regional Road Network intersection traffic runs exponential average AITPI
mean, crossing main line split parameter
crossing main line green time G
i=C
i× r
i, crossing main line red time R
i=C
i-G
i-Y, C
ibe the signal control cycle time of i-th crossing, G
ibe the green time of i-th crossing Regional Road Network, R
ibe the red time of i-th crossing Regional Road Network, Y represents yellow time.
The present invention adopts active video technology, accurately can detect the real-time traffic states of the multiple crossing of Regional Road Network, formulate Regional Road Network optimization signal timing plan, for traffic administration and control provide Real-time Decision and emergency processing information, the operational efficiency of lifting region road grid traffic and service level.
Accompanying drawing explanation
Fig. 1 is workflow diagram of the present invention;
System equipment scheme of installation used in Fig. 2 Fig. 1;
System equipment connection diagram used in Fig. 3 Fig. 1.
Embodiment
A kind of region self-optimizing signal control method based on video as illustrated in fig. 1 and 2 and device, the equipment used in the method comprises video encoder server equipment 1, data communications equipment 2, for data storing and standardized server 3, Regional Road Network self-optimizing processing server 4 and issue terminal equipment 5, between described each equipment, signal connects in order, and the method comprises following step:
S1, in crossing, each importer upwards installs video encoder server equipment, the angle of adjustment detection faces, determines that detection zone and blind area critical line are positioned at 10-20 rice before stop line;
S2, the crossing of Regional Road Network to be numbered in order, then the detecting device of each crossing to be numbered according to clockwise direction, the section numbering belonging to crossing and detecting device are numbered and binds;
S21, the crossing on Regional Road Network to be numbered in order, crossing be numbered I
i, i is crossing sequence label, and I is total number of crossing on Regional Road Network, i≤I;
The type of S22, crossing has multiple, common are five forks in the road, crossroad, T-shaped crossing, and this method is classified according to the number (J) in crossing inlet direction.
S23, to each crossing I
ibe numbered detecting device according to clockwise direction, detecting device is numbered D
ij, i be crossing numbering, j be importer to numbering, j≤J;
The road section scope that S24, video can detect is 10 meters-100 meters, blind area in first 10 meters of installation site, the multidate information of vehicle is can't detect in blind area, therefore the installation site of crossing video equipment is extremely important, after determining section to be measured, detection zone and blind area critical line are positioned at 10-20 rice, stop line front, and the equipment scheme of installation of general crossroad is as Fig. 2.
S3, by video encoder server equipment, the traffic flow of Real-time Collection detector segments and car speed parameter information, described parameter information is through data communications equipment, and real-time returned data stores and standardized server, carries out real-time data memory and standardization;
The real time traffic data of S4, extraction storage server, calculates detector segments average traffic current density parameter, and calculates real-time section traffic operation index according to average traffic current density;
S41, average traffic current density
The data layout of video equipment real-time report is (t, n, q, v), t represents and calls time, and n represents track, place, and q represents traffic flow data, and v represents flow speeds data, the unit of (t, n, q, v) be respectively second, 1 ,/hour/track and thousand ms/h.
Suppose that sample data collection can be expressed as S={ (t, 1, q
1, v
1), (t, 2, q
2, v
2) ..., (t, n, q
n, v
n), the average traffic stream of section to be measured Spatial Dimension and time dimension in timing statistics
the average velocity in unit particle size cycle
the average traffic density of section to be measured Spatial Dimension and time dimension
(unit :/km/track), then
In above-mentioned formula: n is track, place; N is the total number in track in section; q
nit is the traffic flow in the n-th track; v
nbe the speed in the n-th track.
S42, road section traffic volume run index
Build road section traffic volume and run index RTPI (RoadTrafficPerformanceIndex) and average traffic current density
functional relationship model,
Wherein x, y, z, p, m value is that road traffic congestion experiences Optimal Parameters, needs to utilize questionnaire and data analysis the Fitting Calculation, and different categories of roads, and parameter size is also different, and suggesting system for wearing initialized reference value is as table 1.
Table 1 road section traffic volume runs exponential model parameter
S5, according to the traffic circulation exponential sum section road class attribute of each importer of crossing to section, polymerization calculates intersection traffic and runs index, run by intersection traffic the cycle that index-signal period relational model calculates integrative design intersection, and then draw the signal period of each crossing;
Index is run in S51, crossing
It is run to road section traffic volume the polymerization analysis that the basis of index is carried out each importer of crossing to calculate that index ITPI (IntersectionTrafficPerformanceIndex) is run in crossing,
ITPI=RTPI
1*ω
1+RTPI
2*ω
2+,...,+RTPI
j*ω
j(5)
ω
1, ω
2..., ω
jfor each importer to weighting coefficient;
RTPI
jthe road section traffic volume calculated for each entrance ingress detecting device of crossing runs index;
The weighting coefficient in crossing inlet direction is relevant with category of roads, in table 2:
Table 2 category of roads and crossing weight relationship table
Category of roads | Through street | Trunk roads | Secondary distributor road | Branch road |
Weighted value | P1 | P2 | P3 | P4 |
The some importers in crossing to weighted value computing formula as follows:
Wherein:
ω
j' be importer to weighted value corresponding to category of roads;
J is the total number in crossing inlet direction, j be importer to numbering, j≤J;
S52, integrative design intersection cycle
According to the traffic circulation exponential sum section road class attribute of each importer of crossing to section, polymerization calculates intersection traffic and runs index, runs by intersection traffic the cycle that index-signal period relational model calculates integrative design intersection;
C=T*ITPI/10(7)
Wherein,
C is the signal control cycle time;
T is preset signals cycle parameter;
S6, according to the traffic circulation exponential sum section road class attribute of each importer of crossing to section, calculate crossing arterial traffic and run index, COMPREHENSIVE CALCULATING Regional Road Network intersection traffic runs exponential average;
S61, crossing arterial traffic run index IATPI
IATPI=RTPI
1*ω
1+RTPI
2*ω
2+,...,+RTPI
h*ω
h(8)
ω
1, ω
2..., ω
hfor the weighting coefficient in main line section, crossing;
S62, Regional Road Network intersection traffic run exponential average AITPI
mean
ITPI
iit is the intersection traffic operation index of i-th crossing;
I is total number of crossing on Regional Road Network;
S7, based on Regional Road Network intersection traffic run exponential average-split model, the split r of each crossing main line of zoning road network
i, then calculate each crossing main signal and control green light G
iwith red time R
i;
G
i=C
i×r
i(11)
R
i=C
i-G
i-Y(12)
Wherein,
C
iit is the signal control cycle time of i-th crossing;
G
iit is the green time of i-th crossing Regional Road Network;
R
iit is the red time of i-th crossing Regional Road Network;
Y represents yellow time.
S8, enter issue terminal 5, served by calling data bank interface, the signal in equipment 4 is controlled real-time parameter and send to signal to control lamp, control lamp by signal and crossing traffic is dynamically induced.
The present invention make use of the traffic flow of video information collecting device fully and car speed parameter carries out data mining analysis, the road section traffic volume constructed based on video runs exponential model and crossing operation exponential model, achieve the self-optimizing control of Regional Road Network crossing signals, for traffic administration and control provide Real-time Decision and emergency data, reduce traffic hazard, crossing operational efficiency can be increased, the service level of lifting region road network road traffic.
Those skilled in the art will be appreciated that; above embodiment is only used to the present invention is described; and be not used as limitation of the invention; as long as within spirit of the present invention, the suitable change do above embodiment and change all drop within the scope of protection of present invention.
Claims (5)
1. the region self-optimizing signal control method based on video and device, the equipment used in the method comprises video equipment, data communications equipment, data store and standardized server, region self-optimizing processing server signal and signal issue terminal equipment, between described each equipment, signal connects in order, it is characterized in that: the method comprises the steps:
(1) in crossing, each importer upwards installs video encoder server equipment, regulates the angle of detection faces, determines that detection zone and blind area critical line are positioned at 10-20 rice before stop line;
(2) crossing of Regional Road Network is numbered in order, then the detecting device of each crossing is numbered according to clockwise direction, the section numbering belonging to crossing and detecting device are numbered and binds;
(3) by video encoder server equipment, the traffic flow of Real-time Collection detector segments and car speed parameter information, described parameter information is through data communications equipment, and real-time returned data stores and standardized server, carries out real-time data memory and standardization;
(4) extract the real time traffic data of storage server, calculate detector segments average traffic current density parameter, and calculate real-time section traffic operation index according to average traffic current density;
(5) according to the traffic circulation exponential sum section road class attribute of each importer of crossing to section, polymerization calculates intersection traffic and runs index, run by intersection traffic the cycle that index-signal period relational model calculates integrative design intersection, and then draw the signal period of each crossing;
(6) according to the traffic circulation exponential sum section road class attribute of each importer of crossing to section, calculate crossing arterial traffic and run index, COMPREHENSIVE CALCULATING Regional Road Network intersection traffic runs exponential average;
(7) run exponential average-split model based on Regional Road Network intersection traffic, according to main line priority principle, the split of each crossing main line of zoning road network, then calculates each crossing main signal and controls red light and green time;
(8) utilize signal issue terminal equipment, served by calling data bank interface, sent to by the real-time parameter of integrative design intersection signal to control lamp, control lamp by signal and each crossing traffic of Regional Road Network is dynamically induced.
2. a kind of region self-optimizing signal control method based on video according to claim 1 and device, it is characterized in that: the traffic circulation exponential model based on video builds, traffic circulation index-signal period relational model builds, and runs exponential average-split model with Regional Road Network intersection traffic.
3. a kind of region self-optimizing signal control method based on video according to claim 2 and device, it is characterized in that, the described traffic circulation exponential model based on video builds, and comprises road section traffic volume and runs index, intersection traffic operation index, crossing arterial traffic operation index and Regional Road Network traffic circulation exponential average 4 parts;
(31) extract traffic flow data and the speed data in each track of video detector segments, calculate average traffic stream parameter and the average velocity parameter of detector segments at Spatial Dimension and time dimension aspect respectively;
Average traffic stream parameter
pass through formula
calculate, wherein, n is track, place, and N is the total number in track in section, q
nit is the traffic flow in the n-th track; Average velocity parameter v
npass through
calculate, wherein, v
nbe the speed in the n-th track,
for the average velocity of unit granularity period;
(32) average traffic current density parameter
pass through formula
calculate;
(33) road section traffic volume operation index RTPI passes through formula
calculate, wherein x, y, z, p, m value is that road traffic congestion experiences Optimal Parameters;
(34) intersection traffic operation index ITPI passes through formula
ITPI=RTPI
1* ω
1+ RTPI
2* ω
2+ ... ,+RTPI
j* ω
jcalculate, wherein ω
1, ω
2..., ω
jfor each importer to weighting coefficient;
(35) crossing arterial traffic operation index IATPI passes through publicity
IATPI=RTPI
1* ω
1+ RTPI
2* ω
2+ ... ,+RTPI
h* ω
hcalculate, wherein ω
1, ω
2..., ω
hfor the weighting coefficient in main line section, crossing.
4. a kind of region self-optimizing signal control method based on video according to claim 2 and device, it is characterized in that: described traffic circulation index-signal period relational model builds, signal period parameter C=T*ITPI/10, T are preset signals cycle parameters.
5. a kind of region self-optimizing signal control method based on video according to claim 2 and device, it is characterized in that: described Regional Road Network intersection traffic runs exponential average-split model construction, Regional Road Network intersection traffic runs exponential average AITPI
mean, crossing main line split parameter
crossing main line green time G
i=C
i× r
i, crossing main line red time R
i=C
i-G
i-Y, C
ibe the signal control cycle time of i-th crossing, G
ibe the green time of i-th crossing Regional Road Network, R
ibe the red time of i-th crossing Regional Road Network, Y represents yellow time.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201510443091.0A CN105139670A (en) | 2015-07-23 | 2015-07-23 | Video-based regional self-optimizing signal control method and apparatus |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201510443091.0A CN105139670A (en) | 2015-07-23 | 2015-07-23 | Video-based regional self-optimizing signal control method and apparatus |
Publications (1)
Publication Number | Publication Date |
---|---|
CN105139670A true CN105139670A (en) | 2015-12-09 |
Family
ID=54724997
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201510443091.0A Pending CN105139670A (en) | 2015-07-23 | 2015-07-23 | Video-based regional self-optimizing signal control method and apparatus |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN105139670A (en) |
Cited By (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN106781564A (en) * | 2016-12-28 | 2017-05-31 | 安徽科力信息产业有限责任公司 | Bus signal priority control method based on video detector |
CN107705586A (en) * | 2016-08-08 | 2018-02-16 | 阿里巴巴集团控股有限公司 | The wagon flow control method and device of intersection |
CN108074406A (en) * | 2016-11-16 | 2018-05-25 | 杭州海康威视数字技术股份有限公司 | A kind of signal control method and system |
CN109410606A (en) * | 2018-03-22 | 2019-03-01 | 合肥革绿信息科技有限公司 | A kind of trunk roads synergistic signal machine control method based on video |
CN109410607A (en) * | 2018-03-22 | 2019-03-01 | 合肥革绿信息科技有限公司 | A kind of crossroad signal machine control method based on video |
WO2021142642A1 (en) * | 2020-01-15 | 2021-07-22 | Beijing Didi Infinity Technology And Development Co., Ltd. | Efficient network-wide signal coordination with multiple cycle lengths and trajectory data |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
EP1921589A2 (en) * | 2006-11-10 | 2008-05-14 | Hitachi, Ltd. | Traffic information interpolation system |
CN104408925A (en) * | 2014-12-17 | 2015-03-11 | 合肥革绿信息科技有限公司 | Array radar based intersection running state evaluation method |
CN104484994A (en) * | 2014-12-17 | 2015-04-01 | 合肥革绿信息科技有限公司 | Urban road network traffic operation index evaluation method and device based on array radar |
CN104575051A (en) * | 2015-01-14 | 2015-04-29 | 合肥革绿信息科技有限公司 | Viaduct ramp intelligent signal control method and device based on array radars |
-
2015
- 2015-07-23 CN CN201510443091.0A patent/CN105139670A/en active Pending
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
EP1921589A2 (en) * | 2006-11-10 | 2008-05-14 | Hitachi, Ltd. | Traffic information interpolation system |
CN104408925A (en) * | 2014-12-17 | 2015-03-11 | 合肥革绿信息科技有限公司 | Array radar based intersection running state evaluation method |
CN104484994A (en) * | 2014-12-17 | 2015-04-01 | 合肥革绿信息科技有限公司 | Urban road network traffic operation index evaluation method and device based on array radar |
CN104575051A (en) * | 2015-01-14 | 2015-04-29 | 合肥革绿信息科技有限公司 | Viaduct ramp intelligent signal control method and device based on array radars |
Cited By (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN107705586A (en) * | 2016-08-08 | 2018-02-16 | 阿里巴巴集团控股有限公司 | The wagon flow control method and device of intersection |
CN107705586B (en) * | 2016-08-08 | 2022-05-10 | 阿里巴巴集团控股有限公司 | Traffic flow control method and device for road intersection |
CN108074406A (en) * | 2016-11-16 | 2018-05-25 | 杭州海康威视数字技术股份有限公司 | A kind of signal control method and system |
CN106781564A (en) * | 2016-12-28 | 2017-05-31 | 安徽科力信息产业有限责任公司 | Bus signal priority control method based on video detector |
CN109410606A (en) * | 2018-03-22 | 2019-03-01 | 合肥革绿信息科技有限公司 | A kind of trunk roads synergistic signal machine control method based on video |
CN109410607A (en) * | 2018-03-22 | 2019-03-01 | 合肥革绿信息科技有限公司 | A kind of crossroad signal machine control method based on video |
CN109410606B (en) * | 2018-03-22 | 2021-05-04 | 合肥革绿信息科技有限公司 | Main road cooperative annunciator control method based on video |
CN109410607B (en) * | 2018-03-22 | 2021-05-04 | 合肥革绿信息科技有限公司 | Cross intersection signal machine control method based on video |
WO2021142642A1 (en) * | 2020-01-15 | 2021-07-22 | Beijing Didi Infinity Technology And Development Co., Ltd. | Efficient network-wide signal coordination with multiple cycle lengths and trajectory data |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN105139670A (en) | Video-based regional self-optimizing signal control method and apparatus | |
CN104778834B (en) | Urban road traffic jam judging method based on vehicle GPS data | |
CN104408925B (en) | Crossing evaluation of running status method based on display radar | |
CN104575051B (en) | Viaduct ramp intelligent signal control method and device based on array radars | |
CN104464295B (en) | A kind of overhead Entrance ramp intelligence restricted driving method based on video | |
CN105070073A (en) | Geomagnetism-based region self-optimization signal control method and device | |
CN107766969B (en) | Large station fast line layout method based on subway service capacity bottleneck section identification | |
CN102855760B (en) | On-line queuing length detection method based on floating vehicle data | |
CN106781499B (en) | Traffic network efficiency evaluation system | |
CN104575050B (en) | A kind of fast road ramp intellectual inducing method and device based on Floating Car | |
WO2020083399A1 (en) | Coordination trunk line planning method and configuration system based on traffic flow data | |
CN104299426B (en) | A kind of traffic signal control system and method based on to pedestrian detection counting statistics | |
CN105118310A (en) | Video-based single-point self-optimization signal control method and device | |
CN104464321A (en) | Intelligent traffic guidance method based on traffic performance index development trend | |
CN104966403A (en) | Trunk line self-optimizing signal control method and device based on terrestrial magnetism | |
CN107437339A (en) | Variable information advices plate control method for coordinating and system under a kind of information guidance | |
CN104484994A (en) | Urban road network traffic operation index evaluation method and device based on array radar | |
CN106781460B (en) | A kind of road section traffic volume state determines method and device | |
CN104966404A (en) | Single-point self-optimization signal control method and device based on array radars | |
CN101783074A (en) | Method and system for real-time distinguishing traffic flow state of urban road | |
CN102509454A (en) | Road state merging method based on floating car data (FCD) and earth magnetism detector | |
CN103489316A (en) | Method for arranging network traffic flow detectors based on road network topological relation | |
CN104464294A (en) | Method and device for evaluating road segment traffic state based on array radar | |
CN107622668A (en) | A kind of dynamic and visual intersection management system for monitoring based on RFID | |
CN105654720A (en) | Detector laying method based on urban road jam identification |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
C06 | Publication | ||
PB01 | Publication | ||
C10 | Entry into substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20151209 |
|
WD01 | Invention patent application deemed withdrawn after publication |