CN104464295A - Intelligent traffic control method and device for elevated road entrance ramps based on video - Google Patents

Abstract

The invention relates to an intelligent traffic control method for elevated road entrance ramps based on video. The method comprises the steps that acquired real-time traffic parameter information is transmitted to a background server in real time to be stored by video detection devices through data communication equipment, and is sent to an intelligent traffic control processing server through the background server; the average traffic flow density parameter of the video detection devices is calculated by the intelligent traffic control processing server, and real-time traffic running indexes of the video detection devices are extracted; the average traffic running index of all the entrance ramps is calculated, whether the average traffic running index is larger than or equal to a threshold value or not is judged, if the judgment result shows that the average traffic running index is larger than or equal to the threshold value, a portal frame descends, traffic control is carried out on the entrance ramps, and otherwise the portal frame ascends, and road passage is restored. The invention further discloses an intelligent traffic control device for the elevated road entrance ramps based on the video. The accuracy of automatically detecting traffic jam events is improved, the response speed of ramp emergency control is increased, urban traffic flow is effectively dredged, traffic jams of elevated roads are relieved, and traffic accidents are reduced.

Description

A kind of overhead Entrance ramp intelligence restricted driving method based on video and device

Technical field

The present invention relates to the restricted driving control technology field of overhead road of city Entrance ramp, especially a kind of overhead Entrance ramp intelligence restricted driving method based on video and device.

Background technology

Along with the quickening of urbanization process, car owning amount significantly increases, the contradiction of the magnitude of traffic flow and road supply and demand, the construction of overhead road of city considerably increases the selection of traffic path, unimpeded, fast advantage attracted increasing vehicle, every peak period is overhead has just become parking lot, high-altitude, greatly delays the trip of the public, causes the waste of social resources.It is the effective way that transport solution blocks up that ring road restricted driving peak period is closed, traditional ring road restricted driving mode is undertaken by manual type mostly, not only emergency processing postpones, also waste of manpower cost, by Intelligent traffic information acquiring technology, the intelligence realizing Entrance ramp is restricted driving, and is the direction of following ramp metering rate management development.

The mode of traffic information collection is varied, there is the information acquisition mode based on fixed test equipment, as video detects, its drawback carries out information acquisition to breakpoint road surface, the complete traffic behavior in overall section cannot be known, there is blind area traffic events misjudgment phenomenon, the differentiation of mistake may cause road section traffic volume load, is formed and stagnates; Traveler, as controlled in real time overhead congestion information, can continue to sail into overhead, cause ring road traffic congestion to spread, Frequent Accidents, affect the traffic circulation efficiency in whole city.In addition, the too stiff setting of the change according to certain traffic parameter threshold value of the ring road restricted driving algorithm used at present carrys out control signal, as the magnitude of traffic flow, speed, density etc., multiple parametric synthesis is not arranged detection, carry out comprehensive analysis processing, the accuracy of condition discrimination is lower, and system can be caused the erroneous judgement of state, and at present video detecting device is all by manually monitoring, waste of manpower and inefficiency.

Summary of the invention

Primary and foremost purpose of the present invention is to provide a kind of manpower and the equipment cost that can reduce traffic information collection, improve accuracy and the efficiency of overpass traffic congestion event-state graph, realize the overhead Entrance ramp intelligence restricted driving method based on video of the overhead Entrance ramp intelligence restricted driving control and management based on video.

For achieving the above object, present invention employs following technical scheme: a kind of overhead Entrance ramp intelligence restricted driving method based on video, the method comprises the step of following order:

(1) portal frame restricted driving equipment is set in the front of each Entrance ramp overhead, overpass is installed multiple video detecting device, pavement section carried out to overpass and each Entrance ramp is encoded, then with section, coupling being associated to video detecting device;

(2) the real-time traffic parameter information of acquisition stores to background server through data communications equipment real-time Transmission by video detecting device, and real-time traffic parameter information is sent to intelligent restricted driving processing server by background server;

(3) intelligent restricted driving processing server calculates the average traffic current density parameter of each video detecting device, and the real-time traffic extracting each video detecting device runs index;

(4) average traffic calculating each Entrance ramp according to the corresponding relation of Entrance ramp and video detecting device runs index, judge that the average traffic of each Entrance ramp runs index and whether is more than or equal to threshold value, if the determination result is YES, then open the portal frame restricted driving equipment of corresponding Entrance ramp, portal frame declines, and restricts driving to Entrance ramp, otherwise, close the portal frame restricted driving equipment of corresponding Entrance ramp, portal frame rises, and recovers road.

Pavement section is carried out to overpass, editor section coding r _s, to section coding r _sto encode d with video detecting device _jassociate pairing one to one:

r _s＝f(d _j)(s∈S,j∈J)

Wherein, J is total number of all video detecting devices in overpass one direction; S is total number in all sections in overpass one direction; On overpass, every 1 ~ 3 km installs a video detecting device.

The computing method that intelligence restricted driving processing server calculates the average traffic current density parameter of each video detecting device are as follows:

The data layout of video detecting device 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, individual ,/hour/track and thousand ms/h;

Suppose that sample data set representations is S={ (t, 1, q ₁, v ₁), (t, 2, q ₂, v ₂) ..., (t, n, q _n, v _n), the process granularity period of sample is T, and unit is hour, the average traffic stream of section to be measured Spatial Dimension and time dimension in the time of statistical treatment granularity period T

$\stackrel{\‾}{q}=\underset{n=1}{\overset{N}{\mathrm{\Σ}}}{q}_n/N---\left(1\right)$

In formula (1), n is track, place; N is the total number in track in section; q _nit is the traffic flow in the n-th track; for the average traffic stream of unit granularity period;

The average velocity of unit of account granularity period again:

$\stackrel{\‾}{v}=\underset{n=1}{\overset{N}{\mathrm{\Σ}}}{v}_n/N---\left(2\right)$

In formula (2), v _nbe the speed in the n-th track; for the average velocity of unit granularity period; N is the total number in track in section;

The average traffic current density of overhead section Spatial Dimension to be measured and time dimension in the time of counting statistics process granularity period T

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

In formula (3), for the average traffic current density of unit cycle granularity, unit is/km/track.

The method extracting the real-time traffic operation index of each video detecting device is as follows:

Build road section traffic volume and run index RTPI and average traffic current density functional relationship model:

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

Table 1 road section traffic volume runs exponential model parameter

In formula (4), x, y, z, p, m value is that road traffic congestion experiences Optimal Parameters, and its initialized reference value is as shown in table 1.

The computing method of the average traffic operation index of each Entrance ramp are as follows:

To be encoded z by Entrance ramp _iwith video detecting device d _jcorresponding relation calculate Entrance ramp average traffic and run index z _i=f (d ₁, d ₂..., d _j) (i ∈ I, j ∈ J)

$\stackrel{\&OverBar;}{z_i}=\underset{1}{\overset{j}{\mathrm{\&Sigma;}}}\mathrm{RTP}{I}_{d_j}/j---\left(6\right)$

That is, the average traffic of Entrance ramp runs index for all video detecting device d arranged from the overpass of its entrance along its wagon flow direction to super expressway terminal _jreal-time traffic run the mean value of index.

Adopt the signal instruction sig restricted driving _icomputing formula judges that the average traffic of each Entrance ramp runs index and whether is more than or equal to threshold value, and computing formula is as follows:

${\mathrm{sig}}_i=\left\{\begin{array}{cc}1& {\stackrel{\&OverBar;}{z}}_i\&GreaterEqual;9\\ 0& {\stackrel{\&OverBar;}{z}}_i<9\end{array}\right.---\left(7\right)$

In formula (7), sig _iit is the signal instruction of i-th Entrance ramp; sig _i=1 refers to that the portal frame of i-th Entrance ramp declines, and road closed is restricted driving; sig _i=0 refers to that the portal frame of i-th Entrance ramp rises, and recovers road.

Another object of the present invention is to provide a kind of overhead Entrance ramp intelligence limit device based on video, it is characterized in that: comprise the multiple video detecting devices be arranged on overpass, its output terminal is connected with the input end of data communications equipment, the output terminal of data communications equipment is connected with the input end of background server, the output terminal of background server is connected with the input end of intelligence restricted driving processing server, described portal frame restricted driving equipment is made up of data sink and portal frame, the described intelligent output terminal of restricted driving processing server is connected with the input end of data sink, the output terminal of data sink is connected with portal frame.

Described data communications equipment adopts optical cable.

As shown from the above technical solution, the present invention installs video detecting device on overpass, run index by intelligence restricted driving processing server to the average traffic of each Entrance ramp to calculate, if this average traffic runs index be more than or equal to threshold value, then make portal frame decline, road closed is restricted driving, otherwise, portal frame rises, and recovers road.The automatic detection that present invention achieves overhead Entrance ramp is closed and is restricted driving, and takes full advantage of traffic flow and car speed traffic parameter carries out overall treatment, improves the accuracy of traffic congestion event-state graph; The human cost that manual type carries out ring road closedown can be reduced after application the present invention, increase the response speed of ring road emergency flight control, urban traffic flow is effectively dredged, alleviate the traffic congestion of overpass, reduce the generation of traffic hazard, promote service efficiency and the service level of overhead expressway.

Accompanying drawing explanation

Fig. 1 is method flow diagram of the present invention;

Fig. 2 is that equipment of the present invention is installed and coding schematic diagram;

Fig. 3 is system architecture diagram of the present invention.

Embodiment

A kind of overhead Entrance ramp intelligence restricted driving method based on video, comprise (1) and portal frame restricted driving equipment is set in the front of each Entrance ramp overhead, overpass is installed multiple video detecting device, pavement section carried out to overpass and each Entrance ramp is encoded, then with section, coupling being associated to video detecting device; (2) the real-time traffic parameter information of acquisition stores to background server through data communications equipment real-time Transmission by video detecting device, and real-time traffic parameter information is sent to intelligent restricted driving processing server by background server; (3) intelligent restricted driving processing server calculates the average traffic current density parameter of each video detecting device, and the real-time traffic extracting each video detecting device runs index; (4) average traffic calculating each Entrance ramp according to the corresponding relation of Entrance ramp and video detecting device runs index, judge that the average traffic of each Entrance ramp runs index and whether is more than or equal to threshold value, if the determination result is YES, then open the portal frame restricted driving equipment of corresponding Entrance ramp, portal frame declines, Entrance ramp is restricted driving, otherwise close the portal frame restricted driving equipment of corresponding Entrance ramp, portal frame rises, recover road, as shown in Figure 1.

As shown in Figure 1, 2, in order to ensure the sample size of section data acquisition, and the accuracy of traffic circulation status information, integrality, continuity, for the section that length is too short or long, needing with section is elementary cell, split by adjacent segments merging, long section, form the road segments of mileage basis equalization, the section after merging, fractionation is the minimum space unit of Evaluation of traffic condition.The minimum fundamental length in section is 100 meters, overhead terminal is the starting point of section numbering, and section, direction numbering of driving in the wrong direction increases successively, and Entrance ramp and exit ramp junction force to be defined as section node, need each section carrying out merging, category of roads must be consistent.

As shown in Figure 1, 2, pavement section is carried out to overpass, editor section coding r _s, to section coding r _sto encode d with video detecting device _jassociate pairing one to one:

r _s＝f(d _j)(s∈S,j∈J)

Wherein, J is total number of all video detecting devices in overpass one direction; S is total number in all sections in overpass one direction; On overpass, every 1 ~ 3 km installs a video detecting device.

The computing method that intelligence restricted driving processing server calculates the average traffic current density parameter of each video detecting device are as follows:

The data layout of video detecting device 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, individual ,/hour/track and thousand ms/h;

Suppose that sample data set representations is S={ (t, 1, q ₁, v ₁), (t, 2, q ₂, v ₂) ..., (t, n, q _n, v _n), the process granularity period of sample is T, and unit is hour, the average traffic stream of section to be measured Spatial Dimension and time dimension in the time of statistical treatment granularity period T

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

In formula (1), n is track, place; N is the total number in track in section; q _nit is the traffic flow in the n-th track; for the average traffic stream of unit granularity period;

The average velocity of unit of account granularity period again:

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

In formula (2), v _nbe the speed in the n-th track; for the average velocity of unit granularity period; N is the total number in track in section;

The average traffic current density of overhead section Spatial Dimension to be measured and time dimension in the time of counting statistics process granularity period T

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

In formula (3), for the average traffic current density of unit cycle granularity, unit is/km/track.

The method extracting the real-time traffic operation index of each video detecting device is as follows:

Build the functional relationship model that road section traffic volume runs index RTPI and average traffic current density k:

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

In formula (4), x, y, z, p, m value is that road traffic congestion experiences Optimal Parameters, and its initialized reference value is as shown in table 1:

Table 1 road section traffic volume runs exponential model parameter

Install the section of video detecting device, road section traffic volume runs the traffic circulation index that index equals video detecting device, and the traffic circulation index not installing the section of video detecting device is 0;

${\mathrm{RTPI}}_{r_s}=\left\{\begin{array}{cc}{\mathrm{RTPI}}_{d_j}& r_s=f\left(d_j\right)\\ 0& r_s\&NotEqual;f\left(d_j\right)\end{array}\right.---\left(5\right)$

The computing method of the average traffic operation index of each Entrance ramp are as follows:

To be encoded z by Entrance ramp _iwith video detecting device d _jcorresponding relation calculate Entrance ramp average traffic and run index z _i=f (d ₁, d ₂..., d _j) (i ∈ I, j ∈ J)

$\stackrel{\&OverBar;}{z_i}=\underset{1}{\overset{j}{\mathrm{\&Sigma;}}}\mathrm{RTP}{I}_{d_j}/j---\left(6\right)$

That is, the average traffic of Entrance ramp runs index for all video detecting device d arranged from the overpass of its entrance along its wagon flow direction to super expressway terminal _jreal-time traffic run the mean value of index.In fig. 2, the average traffic of Entrance ramp z3 runs the mean value that index is exactly the real-time traffic operation index of video detecting device d4, d3, d2, d1, the average traffic of Entrance ramp z2 runs the mean value that index is exactly the real-time traffic operation index of video detecting device d3, d2, d1 three, and the average traffic of Entrance ramp z1 runs the real-time traffic operation index that index is exactly video detecting device d1.

Adopt the signal instruction sig restricted driving _icomputing formula judges that the average traffic of each Entrance ramp runs index and whether is more than or equal to threshold value, and computing formula is as follows:

${\mathrm{sig}}_i=\left\{\begin{array}{cc}1& {\stackrel{\&OverBar;}{z}}_i\&GreaterEqual;9\\ 0& {\stackrel{\&OverBar;}{z}}_i<9\end{array}\right.---\left(7\right)$

In formula (7), sig _iit is the signal instruction of i-th Entrance ramp; sig _i=1 refers to that the portal frame of i-th Entrance ramp declines, and road closed is restricted driving; sig _i=0 refers to that the portal frame of i-th Entrance ramp rises, and recovers road.

As shown in Figure 3, this device comprises the multiple video detecting devices be arranged on overpass, its output terminal is connected with the input end of data communications equipment, the output terminal of data communications equipment is connected with the input end of background server, the output terminal of background server is connected with the input end of intelligence restricted driving processing server, described portal frame restricted driving equipment is made up of data sink and portal frame, the described intelligent output terminal of restricted driving processing server is connected with the input end of data sink, the output terminal of data sink is connected with portal frame, and described data communications equipment adopts optical cable.

In sum, the present invention make use of traffic flow and the car speed supplemental characteristic of video detecting device fully, restricted driving is closed in the automatic detection achieving overhead Entrance ramp, the human cost that manual type carries out ring road closedown can be reduced, increase the response speed of ring road emergency flight control, urban traffic flow is effectively dredged, alleviate the traffic congestion of overpass, reduce traffic hazard, promote service efficiency and the service level of overhead expressway.

Claims (8)

1., based on an overhead Entrance ramp intelligence restricted driving method for video, the method comprises the step of following order:

(1) portal frame restricted driving equipment is set in the front of each Entrance ramp overhead, overpass is installed multiple video detecting device, pavement section carried out to overpass and each Entrance ramp is encoded, then with section, coupling being associated to video detecting device;

(2) the real-time traffic parameter information of acquisition stores to background server through data communications equipment real-time Transmission by video detecting device, and real-time traffic parameter information is sent to intelligent restricted driving processing server by background server;

(3) intelligent restricted driving processing server calculates the average traffic current density parameter of each video detecting device, and the real-time traffic extracting each video detecting device runs index;

(4) average traffic calculating each Entrance ramp according to the corresponding relation of Entrance ramp and video detecting device runs index, judge that the average traffic of each Entrance ramp runs index and whether is more than or equal to threshold value, if the determination result is YES, then open the portal frame restricted driving equipment of corresponding Entrance ramp, portal frame declines, and restricts driving to Entrance ramp, otherwise, close the portal frame restricted driving equipment of corresponding Entrance ramp, portal frame rises, and recovers road.

2. the overhead Entrance ramp intelligence restricted driving method based on video according to claim 1, is characterized in that: carry out pavement section to overpass, editor section coding r _s, to section coding r _sto encode d with video detecting device _jassociate pairing one to one:

r _s＝f(d _j) (s∈S,j∈J)

Wherein, J is total number of all video detecting devices in overpass one direction; S is total number in all sections in overpass one direction; On overpass, every 1 ~ 3 km installs a video detecting device.

3. the overhead Entrance ramp intelligence restricted driving method based on video according to claim 1, is characterized in that: the computing method that intelligent restricted driving processing server calculates the average traffic current density parameter of each video detecting device are as follows:

The data layout of video detecting device 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, individual ,/hour/track and thousand ms/h;

Suppose that sample data set representations is S={ (t, 1, q ₁, v ₁), (t, 2, q ₂, v ₂) ..., (t, n, q _n, v _n), the process granularity period of sample is T, and unit is hour, the average traffic stream of section to be measured Spatial Dimension and time dimension in the time of statistical treatment granularity period T

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

In formula (1), n is track, place; N is the total number in track in section; q _nit is the traffic flow in the n-th track; for the average traffic stream of unit granularity period;

The average velocity of unit of account granularity period again:

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

In formula (2), v _nbe the speed in the n-th track; for the average velocity of unit granularity period; N is the total number in track in section;

The average traffic current density of overhead section Spatial Dimension to be measured and time dimension in the time of counting statistics process granularity period T

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

In formula (3), for the average traffic current density of unit cycle granularity, unit is/km/track.

4. the overhead Entrance ramp intelligence restricted driving method based on video according to claim 1, is characterized in that: the method extracting the real-time traffic operation index of each video detecting device is as follows:

Build road section traffic volume and run index RTPI and average traffic current density functional relationship model:

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

Table 1 road section traffic volume runs exponential model parameter

In formula (4), x, y, z, p, m value is that road traffic congestion experiences Optimal Parameters, and its initialized reference value is as shown in table 1.

5. the overhead Entrance ramp intelligence restricted driving method based on video according to claim 1, is characterized in that: the computing method of the average traffic operation index of each Entrance ramp are as follows:

To be encoded z by Entrance ramp _iwith video detecting device d _jcorresponding relation calculate Entrance ramp average traffic and run index z _i=f (d ₁, d ₂..., d _j) (i ∈ I, j ∈ J)

${\stackrel{\&OverBar;}{z}}_i=\underset{1}{\overset{j}{\mathrm{\&Sigma;}}}\mathrm{RT}{\mathrm{PI}}_{d_j}/j---\left(6\right)$

That is, the average traffic of Entrance ramp runs index for all video detecting device d arranged from the overpass of its entrance along its wagon flow direction to super expressway terminal _jreal-time traffic run the mean value of index.

6. the overhead Entrance ramp intelligence restricted driving method based on video according to claim 1, is characterized in that: adopt the signal instruction sig restricted driving _icomputing formula judges that the average traffic of each Entrance ramp runs index and whether is more than or equal to threshold value, and computing formula is as follows:

${\mathrm{sig}}_i=\left\{\begin{array}{cc}1& {\stackrel{\&OverBar;}{z}}_i\&GreaterEqual;9\\ 0& {\stackrel{\&OverBar;}{z}}_i<9\end{array}\right.---\left(7\right)$

In formula (7), sig _iit is the signal instruction of i-th Entrance ramp; sig _i=1 refers to that the portal frame of i-th Entrance ramp declines, and road closed is restricted driving; sig _i=0 refers to that the portal frame of i-th Entrance ramp rises, and recovers road.

7. the overhead Entrance ramp intelligence limit device based on video, it is characterized in that: comprise the multiple video detecting devices be arranged on overpass, its output terminal is connected with the input end of data communications equipment, the output terminal of data communications equipment is connected with the input end of background server, the output terminal of background server is connected with the input end of intelligence restricted driving processing server, described portal frame restricted driving equipment is made up of data sink and portal frame, the described intelligent output terminal of restricted driving processing server is connected with the input end of data sink, the output terminal of data sink is connected with portal frame.

8. the overhead Entrance ramp intelligence limit device based on video according to claim 7, is characterized in that: described data communications equipment adopts optical cable.