CN105096617A - Main-line self-optimizing signal control method based on video and apparatus thereof - Google Patents
Main-line self-optimizing signal control method based on video and apparatus thereof Download PDFInfo
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- CN105096617A CN105096617A CN201510442449.8A CN201510442449A CN105096617A CN 105096617 A CN105096617 A CN 105096617A CN 201510442449 A CN201510442449 A CN 201510442449A CN 105096617 A CN105096617 A CN 105096617A
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Abstract
The invention discloses a main-line self-optimizing signal control method based on a video and mainly relates to the main line road intersection signal optimization control field. Through integration application of novel video vehicle detection equipment, dynamic detection and signal optimization processing of a plurality of intersection traffic states on a main line are realized. The method comprises video detection device installation, data acquisition and communication, main-line self-optimizing signal processing calculation and signal instruction publishing and control. The invention also provides a main-line self-optimizing signal control apparatus based on the video. In the invention, an active video acquisition technology is adopted; accurate detection can be performed on real-time traffic states of the plurality of intersections on the main line and an optimized signal control scheme is made; a real-time decision and emergency processing information are provided for traffic management and control; and operation efficiency and a service level of primary road traffic are increased.
Description
Technical field
The present invention relates to the traffic signal optimization control field at the multiple crossing of main line, specifically a kind of main line 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, arterial traffic signal optimizing 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 main line self-optimizing signal control method based on video, run the real-time detection of index and comprehensive analysis by crossing, the signal extracting the multiple crossing of main line controls self-optimizing algorithm, can greatly improve road traffic operational efficiency.
Summary of the invention
A kind of main line 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, main line 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, the angle of adjustment detection faces, determines that detection zone and blind area critical line are positioned at 10-20 rice before stop line;
(2) crossing on main line 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, the arterial traffic of COMPREHENSIVE CALCULATING crossing runs exponential average;
(7) run exponential average-split model based on crossing arterial traffic, calculate the split of each crossing of main line, then calculate each integrative design intersection 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 main line is dynamically induced.
A kind of main line 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 crossing arterial 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 3 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
calculate, wherein x, y, z, p, m value is that road traffic congestion experiences Optimal Parameters;
(D) intersection traffic runs index ITPI
ITPI＝RTPI
1*ω
1+RTPI
2*ω
2+,...,+RTPI
j*ω
j
Wherein, 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;
(E) crossing arterial traffic runs index IATPI
IATPI＝RTPI
1*ω
1+RTPI
2*ω
2+,...,+RTPI
n*ω
h
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;
The arterial traffic of described crossing runs exponential average-split model construction, and crossing arterial traffic runs exponential average IATPI
mean, crossing split parameter
main line direction, crossing green time G
i=C
i× r
i, main line direction, crossing 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 main line, R
ibe the red time of i-th crossing main line, Y represents yellow time.
The present invention adopts active video detection technique, accurately can detect the real-time traffic states of the multiple crossing of main line, formulate optimization signal timing plan, for traffic administration and control provide Real-time Decision and emergency processing information, promote operational efficiency and the service level of trunk roads traffic.
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 main line 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, main line 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 on main line 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 main line to be numbered in order, crossing be numbered I
i, i is crossing sequence label, and I is total number of crossing on main line, 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, the arterial traffic of COMPREHENSIVE CALCULATING crossing 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, crossing arterial traffic run exponential average IATPI
mean
IATPI
iit is the crossing arterial traffic operation index of i-th crossing;
I is total number of crossing on main line;
S7, run exponential average-split model based on crossing arterial traffic, calculate the split r of each crossing of main line
i, then calculate each integrative design intersection 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 main line;
R
iit is the red time of i-th crossing main line;
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 main line crossing signals, for traffic administration and control provide Real-time Decision and emergency data, reduce traffic hazard, crossing operational efficiency can be increased, promote the service level of arterial 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 main line 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, main line 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, the angle of adjustment detection faces, determines that detection zone and blind area critical line are positioned at 10-20 rice before stop line;
(2) crossing on main line 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, the arterial traffic of COMPREHENSIVE CALCULATING crossing runs exponential average;
(7) run exponential average-split model based on crossing arterial traffic, calculate the split of each crossing of main line, then calculate each integrative design intersection 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 main line is dynamically induced.
2. a kind of main line 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 crossing arterial traffic.
3. a kind of main line 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 3 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
n* ω
hcalculate, wherein ω
1, ω
2..., ω
hfor the weighting coefficient in main line section, crossing.
4. a kind of main line 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 main line self-optimizing signal control method based on video according to claim 2 and device, is characterized in that: the arterial traffic of described crossing runs exponential average-split model construction, and crossing arterial traffic runs exponential average IATPI
mean, crossing split parameter
main line direction, crossing green time G
i=C
i× r
i, main line direction, crossing 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 main line, R
ibe the red time of i-th crossing main line, Y represents yellow time.
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