CN105118310A - Video-based single-point self-optimization signal control method and device - Google Patents

Abstract

The invention relates to a video-based single-point self-optimization signal control method, which mainly relates to the field of intersection traffic signal optimization control, and achieves dynamic detection and signal optimization processing of traffic state of a single intersection through the integrated application of novel video vehicle detection equipment. The scheme comprises video equipment installation, data acquisition and communication, single-point self-optimization signal processing calculation, and signal instruction issue and control. The invention further relates to a video-based single-point self-optimization signal control device. The video-based single-point self-optimization signal control method and the video-based single-point self-optimization signal control device adopt an active video technology, can accurately detect real-time traffic state of the single intersection, develop an optimal signal control scheme, provide real-time decision-making and emergency processing information for traffic management and control, and promotes road traffic operating efficiency and service level of the intersections.

Description

A kind of single-point self-optimizing signal control method based on video and device

Technical field

The present invention relates to the traffic signal optimization control field at single crossing, specifically a kind of single-point 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, and particularly section, crossing blocks up event seriously, as the stream that can not effectively relieve traffic congestion in real time, congestion in road can be caused to spread, and reduces crossing operational efficiency, causes great time and economic loss to the public.

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 single-point self-optimizing signal control method based on video, run the real-time detection of index and comprehensive analysis by crossing, extract intersection signal control self-optimizing algorithm, the traffic circulation efficiency of crossing can be improved greatly.

Summary of the invention

A kind of single-point self-optimizing signal control method based on video and device, the equipment used in the method comprises video detecting device, data communications equipment, data store and standardized server, single-point 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, determine that detection zone and blind area critical line are positioned at 10-20 rice, stop line front, according to clockwise direction, detecting device is numbered, the section numbering belonging to crossing and detecting device is numbered and binds;

(2) 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;

(3) 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;

(4) 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;

(5) according to the traffic circulation exponential sum section road class attribute of each importer of crossing to section, calculate East and West direction traffic circulation exponential sum north-south traffic circulation index, calculated the split of integrative design intersection by traffic circulation index-split relational model;

(6) 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 crossing traffic is dynamically induced.

The described traffic circulation exponential model based on video builds, and comprises road section traffic volume and runs index, intersection traffic operation index, East and West direction traffic circulation index and north-south traffic circulation index construction 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

$RTPI=\left\{\begin{array}{c}2\&times;\frac{\stackrel{\&OverBar;}{k}}{x}(0\&le;\stackrel{\&OverBar;}{k}\&le;x)\\ 2+2\&times;\frac{\stackrel{\&OverBar;}{k}-x}{y-x}(x<\stackrel{\&OverBar;}{k}\&le;y)\\ 4+2\&times;\frac{\stackrel{\&OverBar;}{k}-y}{z-y}(y<\stackrel{\&OverBar;}{k}\&le;z)\\ 6+2\&times;\frac{\stackrel{\&OverBar;}{k}-z}{p-z}(z<\stackrel{\&OverBar;}{k}\&le;p)\\ 8+2\&times;\frac{\stackrel{\&OverBar;}{k}-p}{m-p}(p<\stackrel{\&OverBar;}{k}\&le;m)\\ 10(\stackrel{\&OverBar;}{k}>m)\end{array}\right.$ Calculate, wherein x, y, z, p, m value is that road traffic congestion experiences Optimal Parameters;

(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) East and West direction traffic circulation index E WTPI passes through publicity

EWTPI=RTPI ₁* ω ₁+ RTPI ₂* ω ₂+ ... ,+RTPI _h* ω _hcalculate, wherein ω ₁, ω ₂..., ω _hfor the weighting coefficient of east-west direction entrance ingress;

North-south traffic circulation index SNTPI passes through publicity

SNTPI=RTPI ₁* ω ₁+ RTPI ₂* ω ₂+ ... ,+RTPI _n* ω _ncalculate, wherein ω ₁, ω ₂..., ω _nfor the weighting coefficient of North and South direction entrance ingress;

Described traffic circulation index-signal period relational model builds, and signal period parameter C=T*ITPI/10, T are preset signals cycle parameters;

Described traffic circulation index-split relational model builds, east-west direction split parameter east-west direction green time G _ew=C × r _ew, east-west direction red time R _ew=C-G _ew-Y, Y represent yellow time.

The invention has the beneficial effects as follows: the present invention adopts two-dimentional active video technology, accurately can detect the real-time traffic states at single crossing, formulate optimization signal timing plan, for traffic administration and control provide Real-time Decision and emergency processing information, operational efficiency and the service level of intersection traffic can be promoted.

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 single-point 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 background server 3, single-point self-optimizing processing server 4 and issue terminal equipment 5, data communications equipment 2 and video encoder server equipment 1 are linked together by cable and are then arranged on crossing monitoring frame, 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, determine that detection zone and blind area critical line are positioned at 10-20 rice, stop line front, according to clockwise direction, detecting device is numbered, the section numbering belonging to crossing and detecting device is numbered and binds;

The type of S11, 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.

S12, according to clockwise to detecting device (d _j) be numbered, to the section numbering (road belonging to crossing _j) number bind with detecting device, j is the label (j≤J) that sorts.

The road section scope that S13, 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.

S2, by video encoder server equipment 1, the traffic flow of Real-time Collection detector segments and car speed parameter information, described parameter information is through data communications equipment 2, and real-time returned data stores and standardized server, carries out real-time data memory and standardization;

S3, utilize described traffic parameter information, calculate road section traffic volume and run index:

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

$\stackrel{\&OverBar;}{q}=\underset{n=1}{\overset{N}{\&Sigma;}}{q}_n/N---\left(1\right)$

$\stackrel{\&OverBar;}{v}=\underset{n=1}{\overset{N}{\&Sigma;}}{v}_n/N---\left(2\right)$

$\stackrel{\&OverBar;}{k}=\frac{\stackrel{\&OverBar;}{q}}{\stackrel{\&OverBar;}{v}}---\left(3\right)$

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.

Build road section traffic volume and run index RTPI (RoadTrafficPerformanceIndex) and average traffic current density functional relationship model,

$RTPI=\left\{\begin{array}{c}2\&times;\frac{\stackrel{\&OverBar;}{k}}{x}(0\&le;\stackrel{\&OverBar;}{k}\&le;x)\\ 2+2\&times;\frac{\stackrel{\&OverBar;}{k}-x}{y-x}(x<\stackrel{\&OverBar;}{k}\&le;y)\\ 4+2\&times;\frac{\stackrel{\&OverBar;}{k}-y}{z-y}(y<\stackrel{\&OverBar;}{k}\&le;z)\\ 6+2\&times;\frac{\stackrel{\&OverBar;}{k}-z}{p-z}(z<\stackrel{\&OverBar;}{k}\&le;p)\\ 8+2\&times;\frac{\stackrel{\&OverBar;}{k}-p}{m-p}(p<\stackrel{\&OverBar;}{k}\&le;m)\\ 10(\stackrel{\&OverBar;}{k}>m)\end{array}\right.---\left(4\right)$

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

S4, calculating integrative design intersection cycle

Index is run in S41, 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;

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	w′ 1	w′ 2	w′ 3	w′ 4

The some importers in crossing to weighted value computing formula as follows:

${\mathrm{\&omega;}}_j=\frac{{{\mathrm{\&omega;}}_j}^{\&prime;}}{\underset{1}{\overset{N}{\&Sigma;}}{{\mathrm{\&omega;}}_j}^{\&prime;}}---\left(6\right)$

Wherein:

ω _j' be calculate importer to weighted value corresponding to category of roads;

J is total number in crossing inlet direction, j≤J

S42, 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;

S5, calculating integrative design intersection split

According to the traffic circulation exponential sum section road class attribute of each importer of crossing to section, calculate East and West direction traffic circulation exponential sum north-south traffic circulation index, calculated the split of integrative design intersection by traffic circulation index-split relational model;

S51, East and West direction traffic circulation index E WTPI

EWTPI＝RTPI ₁*ω ₁+RTPI ₂*ω ₂+,...,+RTPI _h*ω _h(8)

S52, north-south traffic circulation index SNTPI

SNTPI＝RTPI ₁*ω ₁+RTPI ₂*ω ₂+,...,+RTPI _n*ω _n(9)

Wherein,

ω ₁, ω ₂..., ω _hfor the weighting coefficient of east-west direction entrance ingress;

ω ₁, ω ₂..., ω _nfor the weighting coefficient of North and South direction entrance ingress;

S53, split

East-west direction split parameter is r _ew, North and South direction split parameter is r _sn;

$r_{ew}=\frac{EWTPI}{ITPI},r_{ew}=\frac{SNTPI}{ITPI}$

S54, green time

East-west direction green time G _ew=C × r _ew,

East-west direction red time R _ew=C-G _ew-Y, Y represent yellow time.

S6, 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 single-point crossing signals, for traffic administration and control provide Real-time Decision and emergency data, reduce traffic hazard, promote crossing operational efficiency and service level.

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 single-point 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, single-point 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, determine that detection zone and blind area critical line are positioned at stop line 10-20 rice in the past, according to clockwise direction, detecting device is numbered, the section numbering belonging to crossing and detecting device is numbered and binds;

(2) 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;

(3) 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;

(4) 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;

(5) according to the traffic circulation exponential sum section road class attribute of each importer of crossing to section, calculate East and West direction traffic circulation exponential sum north-south traffic circulation index, calculated the split of integrative design intersection by traffic circulation index-split relational model;

(6) 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 crossing traffic is dynamically induced.

2. a kind of single-point 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 traffic circulation index-split relational model builds.

3. a kind of single-point 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, East and West direction traffic circulation index and north-south traffic circulation index construction 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) East and West direction traffic circulation index E WTPI passes through publicity

EWTPI=RTPI ₁* ω ₁+ RTPI ₂* ω ₂+ ... ,+RTPI _h* ω _hcalculate, wherein ω ₁, ω ₂..., ω _hfor the weighting coefficient of east-west direction entrance ingress;

North-south traffic circulation index SNTPI passes through publicity

SNTPI=RTPI ₁* ω ₁+ RTPI ₂* ω ₂+ ... ,+RTPI _n* ω _ncalculate, wherein ω ₁, ω ₂..., ω _nfor the weighting coefficient of North and South direction entrance ingress.

4. a kind of single-point 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 single-point self-optimizing signal control method based on video according to claim 2 and device, is characterized in that: described traffic circulation index-split relational model builds, east-west direction split parameter east-west direction green time G _ew=C × r _ew, east-west direction red time R _ew=C-G _ew-Y, Y represent yellow time.