CN104575050A - Express way ramp intelligent inducing method and device based on floating vehicles - Google Patents

Abstract

The invention relates to an express way ramp intelligent inducing method and device based on floating vehicles. The method comprises the following sequential steps that inducting screen devices are arranged in front of express way ramps, and way segment partitioning and coding, ramp coding and inducing screen device coding are conducted; real-time dynamic parameter data of all the floating vehicles in a floating vehicle detection way network are obtained and transmitted to a background server to conduct standardized storage; the average travel time and the average travel speed of all the floating vehicles on all way segments of a way are calculated; the way segment average travel time is corrected, the real-time traffic operation indexes of all the way segments of the express way are obtained; the average traffic operation index of all the ramps is calculated, and the traffic operation state levels of related way segments of all the ramps are determined; an intelligent traffic inducing model is established, and intelligent traffic inducing strategies are displayed in real time through the inducing screen devices. By means of the express way ramp intelligent inducing method and device, express way ramp intelligent traffic inducing can be achieved.

Description

A kind of fast road ramp intellectual inducing method based on Floating Car and device

Technical field

The present invention relates to the intelligent transportation inductive technology field of city expressway and elevated ramp, especially a kind of fast road ramp intellectual inducing method based on Floating Car and device.

Background technology

Along with city automobile owning amount significantly increases, the contradiction of the magnitude of traffic flow and road supply and demand, the appearance of city expressway considerably increases traffic path and selects, but through street brings more contradiction while bringing unimpeded trip, every peak period, through street just became parking lot, greatly delayed the trip of the public.

Intelligent transportation induction solves the through street effective way of blocking up, and traditional method is that the restricted driving carrying out ring road by manual type is closed and regulated overhead traffic flow mostly, and emergency processing postpones, also waste of manpower cost; By Intelligent traffic information acquiring technology, realizing the intellectual inducing of ring road, is the direction of following ramp metering rate management development.The mode of traffic information collection is varied, has the information acquiring technology based on Floating Car, also has the information acquisition mode based on fixed test equipment, as video detection, microwave detection, ground induction coil etc.; There is certain drawback in traditional information acquisition mode, easily judge by accident under rain, snow, foggy environment as video detects, the weight that Coil Detector is easily subject to oversize vehicle is damaged, the investigative range of microwave is less etc., the differentiation of mistake may cause road section traffic volume load, formed and stagnate, the process affecting emergency event causes the waste of public administration resource.Floating car traffic information acquisition technique is by installing GPS on vehicle, the dynamic position change information of vehicle is utilized to carry out the technology of real-time road extraction, based on Floating Car displacement data, seasonal effect in time series vehicle location coordinate is mated with map, calculate average velocity and the section travelling speed of all floating points, and then the traffic behavior of road can be extracted, this technology comprises gps data pre-service, map match, path culculating and historical speed such as to supplement at the data processing core program, the transaction module of each program is also diversified, precision also exists difference.

The ring road traffic guidance algorithm used at present is mostly based on point of fixity traffic information collection equipment, can only to the road traffic state detected very among a small circle, large-scale traffic information cannot be obtained, there is the drawback of check frequency, system can be caused the erroneous judgement of state, traveler is not as controlled in real time traffic information, can continue to sail through street into, cause ring road traffic congestion to spread, Frequent Accidents, affect the traffic circulation efficiency in whole city.

Summary of the invention

Primary and foremost purpose of the present invention is to provide one can reduce traffic information collection cost, improves accuracy and the efficiency of Expressway Traffic state-detection, realizes the control and management method of the intelligent transportation induction of fast road ramp.

For achieving the above object, present invention employs following technical scheme:

Based on a fast road ramp intellectual inducing method for Floating Car, the method comprises the step of following order:

(1) induced screen equipment is set in fast road ramp front, pavement section coding, fast road ramp coding and induced screen device coding are carried out to through street, and pavement section coding, fast road ramp coding are carried out associating pairing with induced screen device coding.

(2) Floating Car is detected the real-time dynamic parameter data that the whole Floating Car in road network obtain, transfer to background server and carry out standardization storage, real-time dynamic parameter data is transferred to intellectual inducing processing server by background server.

(3) the real-time dynamic parameter data that the whole Floating Car received obtain is carried out map match and path planning process by intellectual inducing processing server, extract the association road section information in each Floating Car path according to time sequencing respectively, calculate the average travel time in each Floating Car each section on path and average trip speed.

(4) make up model and speed-operation exponential model based on historical data, intellectual inducing processing server corrects section average travel speed, and the real-time traffic obtaining each section, through street runs index.

(5) intellectual inducing processing server adds up the traffic circulation index in section associated by each ring road, the average traffic calculating each ring road runs index, and run index according to the average traffic of each ring road, judge the traffic circulation state grade in each ring road association section.

(6) intellectual inducing processing server is according to the traffic circulation state grade in each ring road association section, builds intelligent transportation guidance model, and shows intelligent transportation induction decision-making in real time by induced screen equipment.

In step (3), the average travel time in each Floating Car of described calculating each section on path and average trip speed, the concrete following methods that adopts realizes:

Suppose certain Floating Car in computation period a series of GPS points of process, the concrete path after map match and driving path culculating is { P _i, i=1,2 ..., n}, wherein, Pi represent this car No. ID of i-th section of process;

Formula (1) is first utilized to calculate this car by section P _itravel time:

$t_{\mathrm{ij}}=\frac{\mathrm{\Δ}{t}_j\×l_i}{\mathrm{\Δ}{d}_j}---\left(1\right)$

Wherein, t _ijrepresent that Floating Car j is at section P _ion travel time, △ d _jrepresent that Floating Car j is at △ t _jlength through path in time, △ t _jrepresent the mistiming of adjacent two reported datas before and after Floating Car j, l _irepresent section P _ilength.

Recycling formula (2) calculates section P _ion average travel speed v _i:

$v_i=\frac{l_i\×n_i}{\underset{j=1}{\overset{n_i}{\mathrm{\Σ}}}{t}_{\mathrm{ij}}},n_i\≠0---\left(2\right)$

Wherein, v _irepresent section P _iaverage travel speed, n _irepresent section P _ithe upper total number of Floating Car participating in calculating, works as n _iequal 0, when namely this section not having gps data to cover, need to make up model by historical data and carry out making up process.

In step (4), described historical data makes up model, shown in specific as follows:

When section not having gps data cover, formula (3) is utilized to obtain the correction rate information in this section,

$V_i=k_1{\stackrel{\‾}{V}}_i+(1-k_1){\hat{V}}_i---\left(3\right)$

Wherein, represent the historical average speeds of this section same time period, represent the average travel speed that this section the last time calculates, k1 be greater than 0 and be slightly less than 1 coefficient.

When section there being gps data cover, formula (4) is first utilized to calculate the correction rate V of current road segment _i, recycling formula (5) upgrades the speed of this section the last time calculating utilize formula (6) to upgrade the historical average speeds of same time period simultaneously

$V_i=k_2{\stackrel{\‾}{V}}_i+(1-k_2)v_i---\left(4\right)$

${\hat{V}}_i=V_i---\left(5\right)$

${\stackrel{\‾}{V}}_i=k_3{\stackrel{\‾}{V}}_i+(1-k_3)V_i---\left(6\right)$

Wherein, k2 and k3 be greater than 0 and be slightly less than 1 coefficient.

In step (4), described speed-operation exponential model, specifically such as formula shown in (7):

${\mathrm{RTPI}}_i=\left\{\begin{array}{cc}10-2\&times;\frac{V_i}{x}& (0\&le;V_i\&le;x)\\ 8-2\&times;\frac{V_i-x}{y-x}& (x<V_i\&le;y)\\ 6-2\&times;\frac{V_i-y}{z-y}& (y<V_i\&le;z)\\ 4-2\&times;\frac{V_i-z}{p-z}& (z<V_i\&le;p)\\ 2-2\&times;\frac{V_i-p}{m-p}& (p<V_i\&le;m)\\ 0& (V_i\&GreaterEqual;m)\end{array}\right.---\left(7\right)$

In formula (7), RTPI _irepresent section P _itraffic circulation index; The value of x, y, z, p, m is that road traffic congestion experiences Optimal Parameters, gives its parameter value according to category of roads, and its initialized reference value is as shown in table 1;

Table 1 road section traffic volume runs exponential model parameter

In step (6), described intellectual inducing processing server, according to the traffic circulation state grade in each ring road association section, builds intelligent transportation guidance model, and shows intelligent transportation induction decision-making in real time by induced screen equipment; Specifically comprise the following steps:

(61) obtain the traffic circulation index in section associated by each ring road, utilize formula (8) to calculate ring road average traffic and run index:

$\stackrel{\&OverBar;}{z_h}=\underset{1}{\overset{h_i}{\mathrm{\&Sigma;}}}{\mathrm{RTPI}}_i/h_i---\left(8\right)$

Wherein, represent that ring road average traffic runs index, RTPI _irepresent the traffic circulation index in section associated by current ring road; H represents the coding of current ring road, h _irepresent the total number in section associated by current ring road.

(2) index is run according to ring road average traffic, judge the traffic circulation state grade of road associated by current ring road, generate intelligent transportation induction scheme decision tree database, induced screen equipment carries out intelligent transportation induction by the service of call instruction database interface to fast road ramp.

Another object of the present invention is to provide a kind of fast road ramp intellectual inducing device based on Floating Car, comprise Floating Car, for data storing and standardized background server, intellectual inducing processing server and induced screen equipment.Floating Car, its output terminal is connected with the input end of background server, and the output terminal of background server is connected with the input end of intellectual inducing processing server, and the output terminal of intellectual inducing processing server is connected with the input end of induced screen equipment.

As shown from the above technical solution, the present invention, by building the intelligent traffic Induction Control algorithm based on floating car data, achieves fast road ramp intelligent transportation induction.The present invention can reduce the human cost that manual video mode carries out ramp management, and effectively dredges urban traffic flow, alleviates traffic congestion, reduces the generation of traffic hazard, promotes service efficiency and the service level of through street.

Accompanying drawing explanation

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

Fig. 2 is apparatus structure block diagram of the present invention.

Wherein:

1, Floating Car, 2, for data storing and standardized background server, 3, intellectual inducing processing server, 4, induced screen equipment.

Embodiment

A kind of fast road ramp intellectual inducing method based on Floating Car as shown in Figure 1, the method comprises the step of following order:

S1, induced screen equipment is set in fast road ramp front, pavement section coding Pi, fast road ramp coding z are carried out to through street _hwith induced screen device coding, and the Pi that encoded by pavement section, fast road ramp coding z carries out associating pairing with induced screen device coding.Employing formula (0) is to fast road ramp coding z _hcarry out one-to-many with section coding Pi and associate pairing,

z _h＝{P ₁,P ₂,...,P _i} (i∈I,h∈H) (0)

Wherein, I represents total number in all sections on through street; H represents total number of all ring roads on through street.

The real-time dynamic parameter data that S2, whole Floating Car Floating Car detected in road network obtain, transfer to background server and carry out standardization storage, real-time dynamic parameter data is transferred to intellectual inducing processing server by background server.The real-time dynamic parameter data that Floating Car obtains, comprise the time, longitude, latitude, highly, deflection and instantaneous velocity etc.

The real-time dynamic parameter data that the whole Floating Car received obtain is carried out map match and path planning process by S3, intellectual inducing processing server, extract the association road section information in each Floating Car path according to time sequencing respectively, calculate the average travel time in each Floating Car each section on path and average trip speed.

The average travel time in each Floating Car of described calculating each section on path and average trip speed, the concrete following methods that adopts realizes:

Suppose certain Floating Car in computation period a series of GPS points of process, the concrete path after map match and driving path culculating is { P _i, i=1,2 ..., n}, wherein, Pi represent this car No. ID of i-th section of process;

Formula (1) is first utilized to calculate this car by section P _itravel time:

$t_{\mathrm{ij}}=\frac{\mathrm{\&Delta;}{t}_j\&times;l_i}{\mathrm{\&Delta;}{d}_j}---\left(1\right)$

Wherein, t _ijrepresent that Floating Car j is at section P _ion travel time, △ d _jrepresent that Floating Car j is at △ t _jlength through path in time, △ t _jrepresent the mistiming of adjacent two reported datas before and after Floating Car j, l _irepresent section P _ilength.

Recycling formula (2) calculates section P _ion average travel speed v _i:

$v_i=\frac{l_i\&times;n_i}{\underset{j=1}{\overset{n_i}{\mathrm{\&Sigma;}}}{t}_{\mathrm{ij}}},n_i\&NotEqual;0---\left(2\right)$

Wherein, v _irepresent section P _iaverage travel speed, n _irepresent section P _ithe upper total number of Floating Car participating in calculating, works as n _iequal 0, when namely this section not having gps data to cover, need to make up model by historical data and carry out making up process.

S4, the number of samples obtained in measurement period, make up model and speed-operation exponential model based on historical data, intellectual inducing processing server corrects section average travel speed, and the real-time traffic obtaining each section, through street runs index.By carrying out periodic statistics analytical calculation to the whole floating car data in through street, extracting road-section average travel time and average trip speed, making up model and speed-operation exponential model in conjunction with historical data, obtain road section traffic volume and run index.

Described historical data makes up model, and for correcting current floating car data, this model can improve the accuracy of data.This historical data makes up model, shown in specific as follows:

When section not having gps data cover, formula (3) is utilized to obtain the correction rate information in this section,

$V_i=k_1{\stackrel{\&OverBar;}{V}}_i+(1-k_1){\hat{V}}_i---\left(3\right)$

Wherein, represent the historical average speeds of this section same time period, represent the average travel speed that this section the last time calculates, k1 be greater than 0 and be slightly less than 1 coefficient.

When section there being gps data cover, formula (4) is first utilized to calculate the correction rate V of current road segment _i, recycling formula (5) upgrades the speed of this section the last time calculating utilize formula (6) to upgrade the historical average speeds of same time period simultaneously

$V_i=k_2{\stackrel{\&OverBar;}{V}}_i+(1-k_2)v_i---\left(4\right)$

${\hat{V}}_i=V_i---\left(5\right)$

${\stackrel{\&OverBar;}{V}}_i=k_3{\stackrel{\&OverBar;}{V}}_i+(1-k_3)V_i---\left(6\right)$

Wherein, k2 and k3 be greater than 0 and be slightly less than 1 coefficient.

Described speed-operation exponential model, specifically such as formula shown in (7):

${\mathrm{RTPI}}_i=\left\{\begin{array}{cc}10-2\&times;\frac{V_i}{x}& (0\&le;V_i\&le;x)\\ 8-2\&times;\frac{V_i-x}{y-x}& (x<V_i\&le;y)\\ 6-2\&times;\frac{V_i-y}{z-y}& (y<V_i\&le;z)\\ 4-2\&times;\frac{V_i-z}{p-z}& (z<V_i\&le;p)\\ 2-2\&times;\frac{V_i-p}{m-p}& (p<V_i\&le;m)\\ 0& (V_i\&GreaterEqual;m)\end{array}\right.---\left(7\right)$

In formula (7), RTPI represents traffic circulation index; The value of x, y, z, p, m is that road traffic congestion experiences Optimal Parameters, gives its parameter value according to category of roads, and its initialized reference value is as shown in table 1;

Table 1 road section traffic volume runs exponential model parameter

S5, intellectual inducing processing server add up the traffic circulation index in section associated by each ring road, the average traffic calculating each ring road runs index, and run index according to the average traffic of each ring road, judge the traffic circulation state grade in each ring road association section.State judges interval as shown in table 2:

Table 2 traffic circulation index hierarchical table

Traffic circulation index	[0,2]	(2,4]	(4,6]	(6,8]	(8,10]
						State evaluation grade	Very unimpeded	Unimpeded	Jogging	Crowded	Block up

S6, intellectual inducing processing server, according to the traffic circulation state grade in each ring road association section, build intelligent transportation guidance model, and show intelligent transportation induction decision-making in real time by induced screen equipment.This process specifically comprises the following steps:

S61, obtain the traffic circulation index in section associated by each ring road, utilize formula (8) to calculate ring road average traffic and run index:

$\stackrel{\&OverBar;}{z_h}=\underset{1}{\overset{h_i}{\mathrm{\&Sigma;}}}{\mathrm{RTPI}}_i/h_i---\left(8\right)$

Wherein, represent that ring road average traffic runs index, RTPI _irepresent the traffic circulation index in section associated by current ring road; H represents the coding of current ring road, h _irepresent the total number in section associated by current ring road.

S62, run index according to ring road average traffic, judge the traffic circulation state grade of road associated by current ring road, build intelligent transportation induction scheme, induced screen equipment carries out intelligent transportation induction by the service of call instruction database interface to fast road ramp.The induced screen equipment being arranged on Entrance ramp realizes the intellectual inducing of Entrance ramp by the service of calling data bank interface, the induced screen equipment being arranged on exit ramp realizes the intellectual inducing of exit ramp by the service of calling data bank interface.

Described structure intelligent transportation induction scheme, specifically comprises the following steps:

S621, judging the attribute of current ring road, is namely Entrance ramp or exit ramp;

S622, grade according to traffic circulation state, it is content distributed to formulate intelligent transportation induction.Induction scheme is made up of four parts: time, traffic circulation index, traffic circulation state and traffic guidance content, shown in specific as follows:

(1) Entrance ramp traffic guidance content runs index intelligent generation based on the real-time traffic in Entrance ramp association section, and content is divided into 5 grades, as follows:

A: this ring road associated road running status is very unimpeded grade: " front overpass traffic noise prediction is very good, regulation speed, better safe than sorry ".

B: this ring road associated road running status is unimpeded grade: " front overpass traffic noise prediction is good, please travels by road standard of the limited speed ".

C: this ring road associated road running status is slight jam level: " front overpass traffic noise prediction slightly blocks up, and trip is slow, driving of taking care ".

D: this ring road associated road running status is moderate jam level: " front overpass traffic noise prediction moderate is blocked up, and suggestion human pilot does not sail into overhead, drives with caution ".

E: this ring road associated road running status is heavy congestion grade: " driver please change travel route, and it is overhead that No entry for front overpass traffic noise prediction extreme difference, heavy congestion ".

(2) exit ramp traffic guidance content runs index intelligent generation based on the real-time traffic in exit ramp association section, and content is divided into 5 grades, as follows:

A: this ring road associated road running status is very unimpeded grade: " front overpass traffic noise prediction is very good, regulation speed, better safe than sorry ".

B: this ring road associated road running status is unimpeded grade: " front overpass traffic noise prediction is good, please travels by road standard of the limited speed ".

C: this ring road associated road running status is slight jam level: " front overpass traffic noise prediction slightly blocks up, and trip is slow, driving of taking care ".

D: this ring road associated road running status is moderate jam level: " front overpass traffic noise prediction moderate is blocked up, and suggestion human pilot rolls away from overhead, drives with caution ".

E: this ring road associated road running status is heavy congestion grade: " driver please roll away from overhead, dodges and blocks up for front overpass traffic noise prediction extreme difference, heavy congestion ".

As shown in Figure 2, another object of the present invention is to provide a kind of fast road ramp intellectual inducing device based on Floating Car, comprise Floating Car 1, for data storing and standardized background server 2, intellectual inducing processing server 3 and induced screen equipment 4.Floating Car 1, its output terminal is connected with the input end of background server 2, and the output terminal of background server 2 is connected with the input end of intellectual inducing processing server 3, and the output terminal of intellectual inducing processing server 3 is connected with the input end of induced screen equipment 4.

A kind of fast road ramp intellectual inducing device based on Floating Car as shown in Figure 2, comprises Floating Car 1, for data storing and standardized background server 2, intellectual inducing processing server 3 and induced screen equipment 4.Floating Car 1, its output terminal is connected with the input end of background server 2, and the output terminal of background server 2 is connected with the input end of intellectual inducing processing server 3, and the output terminal of intellectual inducing processing server 3 is connected with the input end of induced screen equipment 4.

Above-described embodiment is only be described the preferred embodiment of the present invention; not scope of the present invention is limited; under not departing from the present invention and designing the prerequisite of spirit; the various distortion that those of ordinary skill in the art make technical scheme of the present invention and improvement, all should fall in protection domain that claims of the present invention determines.

Claims (6)

1., based on a fast road ramp intellectual inducing method for Floating Car, it is characterized in that: the method comprises the step of following order:

(1) induced screen equipment is set in fast road ramp front, pavement section coding, fast road ramp coding and induced screen device coding are carried out to through street, and pavement section coding, fast road ramp coding are carried out associating pairing with induced screen device coding;

(2) Floating Car is detected the real-time dynamic parameter data that the whole Floating Car in road network obtain, transfer to background server and carry out standardization storage, real-time dynamic parameter data is transferred to intellectual inducing processing server by background server;

(3) the real-time dynamic parameter data that the whole Floating Car received obtain is carried out map match and path planning process by intellectual inducing processing server, extract the association road section information in each Floating Car path according to time sequencing respectively, calculate the average travel time in each Floating Car each section on path and average trip speed;

(4) make up model and speed-operation exponential model based on historical data, intellectual inducing processing server corrects section average travel speed, and the real-time traffic obtaining each section, through street runs index;

(5) intellectual inducing processing server adds up the traffic circulation index in section associated by each ring road, the average traffic calculating each ring road runs index, and run index according to the average traffic of each ring road, judge the traffic circulation state grade in each ring road association section;

(6) intellectual inducing processing server is according to the traffic circulation state grade in each ring road association section, builds intelligent transportation guidance model, and shows intelligent transportation induction decision-making in real time by induced screen equipment.

2. a kind of fast road ramp intellectual inducing method based on Floating Car according to claim 1, it is characterized in that: in step (3), the average travel time in each Floating Car of described calculating each section on path and average trip speed, the concrete following methods that adopts realizes:

Suppose certain Floating Car in computation period a series of GPS points of process, the concrete path after map match and driving path culculating is { P _i, i=1,2 ..., n}, wherein, Pi represent this car No. ID of i-th section of process;

Formula (1) is first utilized to calculate this car by section P _itravel time:

$t_{\mathrm{ij}}=\frac{\mathrm{\&Delta;}{t}_j\&times;l_i}{\mathrm{\&Delta;}{d}_j}---\left(1\right)$

Wherein, t _ijrepresent that Floating Car j is at section P _ion travel time, Δ d _jrepresent that Floating Car j is at Δ t _jlength through path in time, Δ t _jrepresent the mistiming of adjacent two reported datas before and after Floating Car j, l _irepresent section P _ilength;

Recycling formula (2) calculates section P _ion average travel speed v _i:

$v_i=\frac{l_i\&times;n_i}{\underset{j=1}{\overset{n_i}{\mathrm{\&Sigma;}}}{t}_{\mathrm{ij}}},n_i\&NotEqual;0---\left(2\right)$

Wherein, v _irepresent section P _iaverage travel speed, n _irepresent section P _ithe upper total number of Floating Car participating in calculating, works as n _iequal 0, when namely this section not having gps data to cover, need to make up model by historical data and carry out making up process.

3. a kind of fast road ramp intellectual inducing method based on Floating Car according to claim 1, is characterized in that: in step (4), described historical data makes up model, shown in specific as follows:

When section not having gps data cover, formula (3) is utilized to obtain the correction rate information in this section,

$V_i=k_1{\stackrel{\&OverBar;}{V}}_i+(1-k_1){\hat{V}}_i---\left(3\right)$

Wherein, represent the historical average speeds of this section same time period, represent the average travel speed that this section the last time calculates, k1 be greater than 0 and be slightly less than 1 coefficient;

When section there being gps data cover, formula (4) is first utilized to calculate the correction rate V of current road segment _i, recycling formula (5) upgrades the speed of this section the last time calculating utilize formula (6) to upgrade the historical average speeds of same time period simultaneously

$V_i=k_2{\stackrel{\&OverBar;}{V}}_i+(1-k_2)v_i---\left(4\right)$

${\hat{V}}_i=V_i---\left(5\right)$

$V_i=k_3{\stackrel{\&OverBar;}{V}}_i+(1-k_3)V_i---\left(6\right)$

Wherein, k2 and k3 be greater than 0 and be slightly less than 1 coefficient.

4. a kind of fast road ramp intellectual inducing method based on Floating Car according to claim 1, is characterized in that: in step (4), described speed-operation exponential model, specifically such as formula shown in (7):

${\mathrm{RTPI}}_i=\left\{\begin{array}{cc}10-2\&times;\frac{V_i}{x}& (0\&le;V_i\&le;x)\\ 8-2\&times;\frac{V_i-x}{y-x}& (x<V_i\&le;y)\\ 6-2\&times;\frac{V_i-y}{z-y}& (y<V_i\&le;z)\\ 4-2\&times;\frac{V_i-z}{p-z}& (z<V_i\&le;p)\\ 2-2\&times;\frac{V_i-p}{m-p}& (p<V_i\&le;m)\\ 0& (V_i\&GreaterEqual;m)\end{array}\right.---\left(7\right)$

In formula (7), RTPI _irepresent section P _itraffic circulation index; The value of x, y, z, p, m is that road traffic congestion experiences Optimal Parameters, gives its parameter value according to category of roads, and its initialized reference value is as shown in table 1;

Table 1 road section traffic volume runs exponential model parameter

5. a kind of fast road ramp intellectual inducing method based on Floating Car according to claim 1, it is characterized in that: in step (6), described intellectual inducing processing server is according to the traffic circulation state grade in each ring road association section, build intelligent transportation guidance model, and show intelligent transportation induction decision-making in real time by induced screen equipment; Specifically comprise the following steps:

(61) obtain the traffic circulation index in section associated by each ring road, utilize formula (8) to calculate ring road average traffic and run index:

${\stackrel{\&OverBar;}{z}}_h=\underset{1}{\overset{h_i}{\mathrm{\&Sigma;}}}{\mathrm{RTPI}}_i/h_i---\left(8\right)$

Wherein, represent that ring road average traffic runs index, RTPI _irepresent the traffic circulation index in section associated by current ring road; H represents the coding of current ring road, h _irepresent the total number in section associated by current ring road;

(2) index is run according to ring road average traffic, judge the traffic circulation state grade of road associated by current ring road, generate intelligent transportation induction scheme decision tree database, induced screen equipment carries out intelligent transportation induction by the service of call instruction database interface to fast road ramp.

6. based on a fast road ramp intellectual inducing device for Floating Car, it is characterized in that: comprise Floating Car, for data storing and standardized background server, intellectual inducing processing server and induced screen equipment;

Floating Car, its output terminal is connected with the input end of background server, and the output terminal of background server is connected with the input end of intellectual inducing processing server, and the output terminal of intellectual inducing processing server is connected with the input end of induced screen equipment.