CN102819954A - Traffic region dynamic map monitoring and predicating system - Google Patents
Traffic region dynamic map monitoring and predicating system Download PDFInfo
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Abstract
The invention discloses a traffic region dynamic map monitoring and predicating system and belongs to the field of traffic flow monitoring. The traffic region dynamic map monitoring and predicating system comprises a sensing module, a data management module, a map generation module, a map display module, a vehicle analog module, an authentication configuration module and a traffic flow parameter predicating module. By means of combination of a cellular automation model and an RBF (radial basis function) neural network short-term traffic flow predicating model, current jamming conditions and future jamming conditions of road sections can be computed to enable drivers to rapidly know road network situations so as to select proper roads, and accordingly running time is saved while carbon conservation and environment protection are realized. Further, traffic management personnel can know road network conditions in real time and be aware of specific traffic parameters and road condition videos through windows to take necessary measures.
Description
Technical field
The invention belongs to magnitude of traffic flow monitoring field, more particularly, relate to a kind of traffic zone dynamic map monitoring and controlling forecast system.
Background technology
Along with China's car owning amount grows with each passing day, the road network flux density continues to increase, and vehicle blocks up has become the difficult problem of the current society of puzzlement with traffic hazard.Obviously; Effectively the measure of guide car flow have real-time control road network flux density, in good time select suitable route, balance road network flow, rapidly know traffic events, and in time emergent row's barrier handle, relieve traffic congestion etc., but this depends on the modernized science perception means of road network both macro and micro.Although awareness apparatus such as monitoring camera, radar velocity measurement are generally laid at present, many vehicles have been installed the GIS tour guide device, and the traffic perception has subject matter to be solved to have:
1) the single monitoring camera of trackside can only provide the road conditions video of effective sighting distance, is difficult to reflection highway section, the whole traffic of road network, or macroscopical traffic situation, can not provide road to induce in real time or accurate information that point duty is dredged;
2) a large amount of trackside shootings, manual work is watched, and except that labor intensive, often can not in time understand traffic events;
3) road GIS device is housed, is loaded with static road data, do not reflect the actual motion road conditions, can not select route from consuming time or unimpeded angle.
Chinese patent numbers 201010290408.9; Open day on 01 26th, 2011; Disclose a name and be called patent document based on the magnitude of traffic flow and the road congestion detection system of GPS, it comprise in order to be installed on the monitor vehicle car-mounted terminal and in order to information that receives car-mounted terminal and the server that crowds calculating; Said car-mounted terminal is connected with server communication through cordless communication network, and said car-mounted terminal comprises GPS module, first communication module, display module and message processing module; Said server comprises; The congestion status computing module; In order to coordinate information according to different car-mounted terminal passbacks; Function obtains Real-time Traffic Information through blocking up: adopt the crowding in crowded each different highway sections of function calculation, if crowding is judged to be congestion state greater than preset crowding threshold value; Otherwise, be judged to be unobstructed state; Second communication module sends to car-mounted terminal in order to the traffic behavior with each different highway sections.This invention is low-cost, practical, be easy to promote.
One Chinese patent application numbers 201110439431.4; Open day on 06 13rd, 2012; The patent document that a name is called collection of a kind of road traffic flow and Forecasting Methodology is disclosed; It comprises toroidal inductor, vehicle detection module, magnitude of traffic flow acquisition module, traffic flow data pre-service and prediction, and data pre-service of road traffic flow and forecasting software carry out on host computer (PC), and reads the traffic flow data in the acquisition module (SD card) through network interface.For improving forecasting reliability, in data pre-service of road traffic flow and Forecasting Methodology, at first adopt wavelet analysis to combine least square method that traffic flow data is carried out noise and reject; Adopt improved BP neural network to set up model of traffic flux forecast then, realize prediction, for control timing scheme and the traffic planninng of optimizing road traffic provides foundation the magnitude of traffic flow.This invention can obtain the road traffic parameter such as vehicle flowrate, average speed, occupation rate and traffic density in the specified period, thereby realizes the prediction to the road traffic flow, improves data acquisition and road traffic flow prediction accuracy.
The above-mentioned magnitude of traffic flow and the patent of road congestion detection system based on GPS requires travels down that a large amount of monitoring vehicles is arranged, and perhaps requires a large amount of common vehicle that the GPS module is installed and to the server open message interface.The former can increase traffic pressure, and cost is huge, and it is big that the latter promotes difficulty, is difficult to carry out.The patent of collection of above-mentioned a kind of road traffic flow and Forecasting Methodology has just described how traffic is carried out data acquisition and prediction; Do not mention the information that how system statistical analysis is obtained and express better, and effective feedback form of information is only the direct mode that embodies the traffic analysis system value.
Summary of the invention
The problem that solves
Infrastructure is required high to existing traffic analysis system; The information manifestation mode is dull, content is single, lacks the problem of multi-level information feedback from the macroscopic view to the microcosmic, and the present invention provides a kind of traffic zone dynamic map monitoring and controlling forecast system; Can let the driver understand road network situation rapidly; Select suitable route, save working time, the joint carbocyclic ring is protected; The traffic administration personnel can hold the road network situation in real time, know concrete traffic parameter and road conditions video through window, take necessary counter-measure, in time relieve traffic congestion.
Technical scheme
In order to address the above problem, the technical scheme that the present invention adopted is following:
A kind of traffic zone dynamic map monitoring and controlling forecast system comprises that sensing module, data management module, map generation module, map put on display module, vehicle simulation module, authenticated configuration module and traffic flow parameter prediction module;
Described sensing module is responsible for acquired signal; Be used to detect the information of each porch, unit, highway section vehicle; Via the data management module analysis; Input traffic flow parameter prediction module is handled, and puts on display module and vehicle simulation module formation dynamic map through map generation module, map, and reflection road network macroscopic view is to the multi-level situation of microcosmic;
The measuring-signal of described sensing module is mainly derived from the monitoring camera on the highway; Native system is an elementary cell with the highway section in the middle of per two monitoring cameras; Detect the essential information of each elementary cell porch vehicle; Utilize traffic flow parameter prediction module prediction vehicle the go mode inner and carry out three-dimensional simulation to show, constitute the virtual region dynamic map in the unit;
Described map generation module carries out the dynamic map platform to be safeguarded, mainly carries out processing such as three-dimensional simulation, projection conversion, map registration, the sign of blocking up, and can from the macroscopic view to the microcosmic, show the road network operation conditions at many levels;
Described map is put on display module has a color ribbon to characterize the degree of blocking up of corresponding road section at the other mark of road automatically;
Described vehicle simulation module is carried out the virtual demonstration of vehicle '; Carry out the processing such as three-dimensional modeling, speed of a motor vehicle adjusting, car look registration, vehicle classification of vehicle; The rule that the described speed of a motor vehicle is regulated is according to specific traffic flow model; With less detection data performance road network operation conditions, and guarantee smoothly going of highway section, unit intersection model vehicle, described vehicle detects through monitor video and obtains; Can be divided into middle minibus, motor bus, jubilee wagon, medium truck, high capacity waggon, super-huge lorry, seven kinds of vehicles of container car, corresponding seven kinds of three-dimensional models;
Described authenticated configuration module; Mainly be to obtain authority through the authenticated configuration module; Can retrieve for examination the fixedly video pictures of monitoring camera shooting of local trackside; Also can command mobile video source such as road inspection car specifying the highway section follow shot, on-the-spot details or the road blind area of watching fixing monitoring to be difficult to catch.
Further, described traffic flow parameter prediction module adopts the mode that cellular Automation Model combines with RBF neural network short-term forecasting traffic flow model;
Described cellular Automation Model is all done careful calculating to the rule of going of each car, is that system's each car that satisfies the need in the segment unit is done the theoretical foundation that three-dimensional simulation shows; In the traffic flow model of cellular automaton; The time of vehicle ', space and speed are by discretize on the track; In
process, model is pressed following four regular parallel evolutionaries:
1) quickens rule: in the larger distance of the place ahead, do not have car;
, the characteristic of going with maximal rate corresponding to driver expectation in the reality;
2) rule of slowing down: in the nearer distance in the place ahead, car is arranged;
, the measure that the driver takes to slow down for fear of bumping with front truck;
3) slowing down at random: with Probability p;
; Represent various uncertain factors; For example pavement behavior is bad, different phychology fluctuations of driver etc., the vehicle deceleration that may cause, but the speed of a motor vehicle is all the time greater than a back car;
4) motion:
, vehicle according to adjusted speed to overtake.
Here, Xn represents the position of n car; Vn representes the speed of n car, this car distance in earlier stage after the every frame of dynamic map refreshes in this speed characterization system; Dn representes the distance between n car and the front truck n+1.
System handles each car on each frame of dynamic map, reads information of vehicles, according to aforementioned four rules, calculate car in the position of next frame, state such as speed, acceleration, and in next frame, carry out corresponding demonstration;
Accomplish model and numerical simulation and also must confirm boundary condition, the present invention adopts improved opening boundary condition:
In the road exit, a car position exceeds the road scope and assert that promptly it rolls the highway section away from, no longer shows; Second car immediately following thereafter becomes a new car; In the road porch, vehicle was located to produce before front truck produces the position with certain probability, and is folded with two car weights before and after preventing;
Described RBF neural network short-term forecasting traffic flow model is through the mode of sample training, and the segment unit magnitude of traffic flow of satisfying the need has more in advance and prediction accurately, and system is provided with an inlet in each unit; Utilize awareness apparatus to detect the situation that gets into vehicle, system design is M rice before each unit, highway section, and vehicle goes according to original cellular Automation Model rule; Y rice in the back; System detects the magnitude of traffic flow A in the dummy model, utilizes RBF neural network short-term forecasting traffic flow model simultaneously, the magnitude of traffic flow in several highway sections before reading; Dope the due magnitude of traffic flow B in this end, unit, highway section this moment; A is compared with B, adjust the parameter in the cellular Automation Model then, comprise acceleration distance, deceleration distance, the random chance of slowing down at random; Make vehicle flowrate in the dummy model in the end tend to the requirement of magnitude of traffic flow B in the Y rice gradually, the vehicle that reaches the highway section intersection shows effect level and smooth and that traffic is constantly revised.
Further, described color ribbon is made up of red, yellow, green, blue gradual change aberration, gets 7 kinds, the conversion step, the degree of blocking up with the average velocity of corresponding road section vehicle as criterion.For example with reference to international standard and Chinese actual traffic situation, should set the corresponding road section average speed being redness below 60 kilometers, be green more than 100 kilometers, and middle is a step by 8 kilometers, and color is gradual change successively.Also can get the scope of other colors and average speed.
Further, described M is 900, and Y is 100.
Further, the measuring-signal of described sensing module also derive from ground induction coil, radar velocity measurement equimagnetic frequently, ripple frequently, the piezoelectric type sensor, but the perception speed of a motor vehicle, vehicle number, the vision-based detection sensing can obtain information such as vehicle, car look in addition.
Further; Also comprise estimating and induce module, utilize to estimate and induce module, on the basis of collecting current Real-time Road information; Analysis such as carry out that zone selection, the judgement of blocking up, time are estimated finally draws and arrives the shortest route induction information of destination required time.
When the present invention uses; Highway section with in the middle of per two monitoring cameras is an elementary cell; Detect the essential information of porch, every unit vehicle, utilize the mode of going of inner one to two kilometer on traffic flow model prediction vehicle and carry out three-dimensional simulation to show, constitute the virtual region dynamic map in the unit.
At first, utilize ground induction coil, radar equimagnetic frequency, ripple frequency, piezoelectric type sensor, each highway section inlet of perception gets into the speed of a motor vehicle, the vehicle number of vehicle; Utilize the unusual condition on vehicle, color and the current road surface of Video Detection vehicle, like ponding, sleet etc.; Utilize trackside monitoring camera and road inspection car follow shot condition of road surface, and these data transfer are arrived data management module.
Data management module carries out corresponding format conversion, classification with the parameter that obtains, and to the vehicle attribute data, carries out the unification of unit, and the rejecting abnormalities data are submitted to forecasting traffic flow module and vehicle simulation module then.To environmental data, carry out the order of severity and differentiate, submit to the map generation module.To video data, carry out the H264 coding, to language data, carry out the AAC coding, submit to map and put on display module.
After the traffic flow parameter prediction module receives information of vehicles, the mode predicted link situation of utilizing cellular automaton to combine with RBF neural network short-term forecasting traffic flow model, and the information of forecasting of each car submitted to the map generation module.
After the vehicle simulation module receives data such as the size, color of vehicle, compare, select the most similar auto model, submit to the map generation module with auto model in the self model storehouse.
The map generation module receives the data of traffic flow parameter prediction module and vehicle simulation module; Utilize OPENGL technology, generate three-dimensional map interface, on the interface except that the road that three-dimensional simulation is arranged; Also have and the corresponding driving vehicle of reality, then cartographic information is submitted to map and put on display module.
Map is put on display the information that module receives the map generation module, and the evaluation speed of adding up each bar travels down vehicle is being redness below 60 kilometers; Be green more than 100 kilometers; Middle is a step by 8 kilometers, and color is the rule of gradual change successively, gives every highway section color matching.Can understand the congestion of whole road network macroscopic view through watching colour bar, the reality that also can further, the highway section of amplification appointment, the driving details of watching microcosmic through selecting to zoom out the visual angle.
If traffic administration person or road management person use native system; When macroscopic aspect is observed the road network operation conditions, interested in the concrete microcosmic traffic situation in a certain highway section, can obtain authority through the authenticated configuration module; Retrieve for examination the fixedly video pictures of monitoring camera shooting of local trackside; Also can command mobile video source such as road inspection car specifying the highway section follow shot, dispatch control or on-site law-enforcing etc. are convenient in on-the-spot details or the road blind area of watching fixing monitoring to be difficult to catch.
If the driving driver uses native system, capable of using estimating induced module, collecting on the basis of current Real-time Road information, and analysis such as carry out that zone selection, the judgement of blocking up, time are estimated finally draws the shortest route induction information of arrival destination required time.
Beneficial effect
Than prior art, beneficial effect of the present invention is:
(1) the present invention includes sensing module, data management module, map generation module, map exhibition module, vehicle simulation module, authenticated configuration module and traffic flow parameter prediction module; Utilize a spot of road awareness apparatus, obtain the traffic parameter of each highway section mouth in the road network, can hold following traffic information of road network situation and prediction is in time selected the best to the driver route map in real time;
(2) traffic flow parameter prediction module of the present invention adopts the mode that cellular Automation Model combines with RBF neural network short-term forecasting traffic flow model; Can calculate the jam situation and following jam situation in highway section; The driver understands road network situation rapidly; Select suitable route, save working time, the joint carbocyclic ring is protected; The traffic administration personnel can hold the road network situation in real time, know concrete traffic parameter and road conditions video through window, take necessary counter-measure;
(3) the present invention is an elementary cell with the highway section in the middle of per two monitoring cameras, has made full use of freeway surveillance and control device distribution characteristic, during use the system reform simple, cost is low;
(4) the present invention the prediction of vehicle flowrate is adopted is quickened rule, the rule of slowing down, the slowing down and four rules of moving at random, Forecasting Methodology is more near actual conditions, it is more accurate to predict;
(5) the inventive method is simple, and is reasonable in design, is easy to realize.
Description of drawings
Fig. 1 is a system architecture synoptic diagram of the present invention.
Embodiment
Describe the present invention below in conjunction with concrete accompanying drawing.
As shown in Figure 1, a kind of traffic zone dynamic map monitoring and controlling forecast system comprises that sensing module, data management module, map generation module, map put on display module, vehicle simulation module, authenticated configuration module and traffic flow parameter prediction module.
Sensing module is responsible for acquired signal; Be used to detect the information of each porch, unit, highway section vehicle; The measuring-signal of sensing module also derives from ground induction coil, radar velocity measurement equimagnetic frequency, ripple frequency, piezoelectric type sensor; But the perception speed of a motor vehicle, vehicle number, the vision-based detection sensing can obtain the unusual condition on vehicle, Che Se and current road surface in addition, like information such as ponding, sleet.Via the data management module analysis, data management module carries out corresponding format conversion, classification with the parameter that obtains, to the vehicle attribute data; Carry out the unification of unit, the rejecting abnormalities data are submitted to the traffic flow parameter prediction module then and are handled; To environmental data, carry out the order of severity and differentiate, submit to map generation module, map exhibition module and vehicle simulation module and constitute dynamic map; For video data, carry out the H264 coding, to language data; Carry out the AAC coding, submit to map and put on display module.
The measuring-signal of sensing module is mainly derived from the monitoring camera on the highway; Native system is an elementary cell with the highway section in the middle of per two monitoring cameras; Detect the essential information of each elementary cell porch vehicle; Utilize traffic flow parameter prediction module prediction vehicle the go mode inner and carry out three-dimensional simulation to show, constitute the virtual region dynamic map in the unit.
The map generation module carries out the dynamic map platform to be safeguarded, mainly carries out processing such as three-dimensional simulation, projection conversion, map registration, the sign of blocking up, and can from the macroscopic view to the microcosmic, show the road network operation conditions at many levels.
Map is put on display module has a color ribbon to characterize the degree of blocking up of corresponding road section at the other mark of road automatically; Color ribbon is made up of red, yellow, green, blue gradual change aberration, desirable 7 kinds, the conversion step, the degree of blocking up with the average velocity of corresponding road section vehicle as criterion.With reference to international standard and Chinese actual traffic situation, should set the corresponding road section average speed being redness below 60 kilometers, be green more than 100 kilometers, middle is a step by 8 kilometers, color is gradual change successively.
The vehicle simulation module is carried out the virtual demonstration of vehicle '; Carry out the processing such as three-dimensional modeling, speed of a motor vehicle adjusting, car look registration, vehicle classification of vehicle; The rule that the described speed of a motor vehicle is regulated is according to specific traffic flow model; With less detection data performance road network operation conditions, and guarantee smoothly going of highway section, unit intersection model vehicle, vehicle detects through monitor video and obtains; Can be divided into middle minibus, motor bus, jubilee wagon, medium truck, high capacity waggon, super-huge lorry, seven kinds of vehicles of container car, corresponding seven kinds of three-dimensional models; After the vehicle simulation module receives data such as the size, color of vehicle, compare, select the most similar auto model, submit to the map generation module with auto model in the self model storehouse.
If traffic administration person or road management person use native system; When macroscopic aspect is observed the road network operation conditions, interested in the concrete microcosmic traffic situation in a certain highway section, can obtain authority through the authenticated configuration module; Retrieve for examination the fixedly video pictures of monitoring camera shooting of local trackside; Also can command mobile video source such as road inspection car specifying the highway section follow shot, dispatch control or on-site law-enforcing etc. are convenient in on-the-spot details or the road blind area of watching fixing monitoring to be difficult to catch.
The mode that the traffic flow parameter prediction module adopts cellular Automation Model to combine with RBF neural network short-term forecasting traffic flow model;
Cellular Automation Model is all done careful calculating to the rule of going of each car, is that system's each car that satisfies the need in the segment unit is done the theoretical foundation that three-dimensional simulation shows; In the traffic flow model of cellular automaton; The time of vehicle ', space and speed are by discretize on the track; In
process, model is pressed following four regular parallel evolutionaries:
1) quickens rule: in the larger distance of the place ahead, do not have car;
, the characteristic of going with maximal rate corresponding to driver expectation in the reality;
2) rule of slowing down: in the nearer distance in the place ahead, car is arranged;
, the measure that the driver takes to slow down for fear of bumping with front truck;
3) slowing down at random: with Probability p;
; Represent various uncertain factors; For example pavement behavior is bad, different phychology fluctuations of driver etc., the vehicle deceleration that may cause, but the speed of a motor vehicle is all the time greater than a back car;
4) motion:
, vehicle according to adjusted speed to overtake.
Here, Xn represents the position of n car; Vn representes the speed of n car, this car distance in earlier stage after the every frame of dynamic map refreshes in this speed characterization system; Dn representes the distance between n car and the front truck n+1.
System handles each car on each frame of dynamic map, reads information of vehicles, according to aforementioned four rules, calculate car in the position of next frame, state such as speed, acceleration, and in next frame, carry out corresponding demonstration.
Accomplish model and numerical simulation and also must confirm boundary condition, the present invention adopts improved opening boundary condition:
In the road exit, a car position exceeds the road scope and assert that promptly it rolls the highway section away from, no longer shows; Second car immediately following thereafter becomes a new car; In the road porch, vehicle was located to produce before front truck produces the position with certain probability, and is folded with two car weights before and after preventing.
RBF neural network short-term forecasting traffic flow model is through the mode of sample training, and the segment unit magnitude of traffic flow of satisfying the need has more in advance and prediction accurately, and system is provided with an inlet in each unit; Utilize awareness apparatus to detect the situation that gets into vehicle, preceding 900 meters of system designs in each unit, highway section, vehicle goes according to original cellular Automation Model rule; At back 100 meters; System detects the magnitude of traffic flow A in the dummy model, utilizes RBF neural network short-term forecasting traffic flow model simultaneously, the magnitude of traffic flow in several highway sections before reading; Dope the due magnitude of traffic flow B in this end, unit, highway section this moment; A is compared with B, adjust the parameter in the cellular Automation Model then, comprise acceleration distance, deceleration distance, the random chance of slowing down at random; Make vehicle flowrate in the dummy model in the end tend to the requirement of magnitude of traffic flow B in 100 meters gradually, the vehicle that reaches the highway section intersection shows effect level and smooth and that traffic is constantly revised.
After the traffic flow parameter prediction module receives information of vehicles, the mode predicted link situation of utilizing cellular automaton to combine with RBF neural network short-term forecasting traffic flow model, and the information of forecasting of each car submitted to the map generation module.The map generation module receives the data of traffic flow parameter prediction module and vehicle simulation module; Utilize OPENGL technology, generate three-dimensional map interface, on the interface except that the road that three-dimensional simulation is arranged; Also have and the corresponding driving vehicle of reality, then cartographic information is submitted to map and put on display module.
If the driving driver uses native system, capable of using estimating induced module, collecting on the basis of current Real-time Road information, and analysis such as carry out that zone selection, the judgement of blocking up, time are estimated finally draws the shortest route induction information of arrival destination required time.
The monitoring camera spacing of domestic highway is generally 1 to 2 kilometer, and native system is an elementary cell with the highway section in the middle of per two monitoring cameras.
The flow of traffic flow is meant at moment t in
;
is short time span; Be generally and get 5--15min, through the vehicle number of a certain highway section observation station.The traffic flow forecasting ultimate principle is described below:
If:
be in the road network on the i bar highway section certain observation station α at the integrated flow to the t of section
constantly;
is predetermined period; In the prediction of short-term traffic flow; General Δ t≤15 min; Equally;
,
represent the flow in its forward and backward n period respectively.To certain concrete problem; Because
and a fix, therefore will in following presents
is the historical statistical data of identical period of same place; With i each m section of upstream and downstream highway section that the highway section is adjacent, its label is i ± j, j=1,2 ..., m.
Short-time traffic flow forecast is exactly to know i highway section and i+ j highway section P flow constantly in the past according to oneself
Reach relevant statistics, refer to the traffic flow data of same time period in this prediction highway section past,
Obtain the estimated value of i highway section flow
in following k the time period, and call predictor to these
predicted values.
Predictor mainly comprises the data of time and two aspects, space: temporal data are meant the flow and the history average in several time intervals in past in i highway section; Data on the space are meant the current of the up and down highway section adjacent with the i highway section and each flow constantly of past.
Native system is introduced the algorithm of RBF neural network.RBF neural network full name RBF (Radial Basis Function RBF) neural network is 3 layers of feedforward network with single latent layer.RBF network analog local adjustment in the human brain, cover the acceptance domain neural network structure of (or claiming receptive field, Receptive Field) each other, proved that the RBF network can be competent at arbitrary accuracy and approach any continuous function.
To i bar highway section in the road network; The flow value of preceding 15 minutes each k bar highway section inlets of front and back of system statistics dopes the current due flow value in i bar highway section
and is designated as A in conjunction with the RBF neural network algorithm; Simultaneously, calculate the magnitude of traffic flow B of the traffic flow last generation that generates through cellular Automation Model in the highway section; Compare A and B.
Get X=A-B.X has characterized the gap of the magnitude of traffic flow algorithm that the magnitude of traffic flow that cellular Automation Model simulates and neural network algorithm study draws.
Simulated automotive goes and goes out for 100 meters to the end, highway section in system, calculates X, and acceleration distance and deceleration distance parameter in the adjustment cellular Automation Model.To arbitrary car, establishing current acceleration distance is that deceleration distance does, after the adjustment
Be scale-up factor; And, adjust according to X and
,
unit greater than 0.Adjustment back simulating vehicle promptly goes by new parameter.
Value depends in the system
;
; The selection of unit.The arbitrary unit of above-mentioned parameter is changed, can in the system initialization process, select K bar highway section in advance, and order equals 1, the above-mentioned adjustment formula of substitution is verified then.If find that the adjusted magnitude of traffic flow is greater than RBF neural network prediction value; Then
; Otherwise, then
.Make both differences minimum after repeatedly adjusting, so can the value of obtaining.
Claims (6)
1. a traffic zone dynamic map monitoring and controlling forecast system is characterized in that: comprise sensing module, data management module, map generation module, map exhibition module, vehicle simulation module, authenticated configuration module and traffic flow parameter prediction module;
Described sensing module is responsible for acquired signal; Be used to detect the information of each porch, unit, highway section vehicle; Via the data management module analysis; Input traffic flow parameter prediction module is handled, and puts on display module and vehicle simulation module formation dynamic map through map generation module, map, and reflection road network macroscopic view is to the multi-level situation of microcosmic;
The measuring-signal of described sensing module derives from the monitoring camera on the highway; Native system is an elementary cell with the highway section in the middle of per two monitoring cameras; Detect the essential information of each elementary cell porch vehicle; Utilize traffic flow parameter prediction module prediction vehicle the go mode inner and carry out three-dimensional simulation to show, constitute the virtual region dynamic map in the unit;
Described map generation module carries out the dynamic map platform to be safeguarded, mainly carries out processing such as three-dimensional simulation, projection conversion, map registration, the sign of blocking up, and can from the macroscopic view to the microcosmic, show the road network operation conditions at many levels;
Described map is put on display module has a color ribbon to characterize the degree of blocking up of corresponding road section at the other mark of road automatically;
Described vehicle simulation module is carried out the virtual demonstration of vehicle '; Carry out the processing such as three-dimensional modeling, speed of a motor vehicle adjusting, car look registration, vehicle classification of vehicle; The rule that the described speed of a motor vehicle is regulated is according to specific traffic flow model; With less detection data performance road network operation conditions, and guarantee smoothly going of highway section, unit intersection model vehicle, described vehicle detects through monitor video and obtains; Can be divided into middle minibus, motor bus, jubilee wagon, medium truck, high capacity waggon, super-huge lorry, seven kinds of vehicles of container car, corresponding seven kinds of three-dimensional models;
Described authenticated configuration module; Mainly be to obtain authority through the authenticated configuration module; Can retrieve for examination the fixedly video pictures of monitoring camera shooting of local trackside; Also can command mobile video source such as road inspection car specifying the highway section follow shot, on-the-spot details or the road blind area of watching fixing monitoring to be difficult to catch.
2. traffic zone dynamic map monitoring and controlling forecast according to claim 1 system is characterized in that: the mode that described traffic flow parameter prediction module adopts cellular Automation Model to combine with RBF neural network short-term forecasting traffic flow model;
Described cellular Automation Model is all done careful calculating to the rule of going of each car, is that system's each car that satisfies the need in the segment unit is done the theoretical foundation that three-dimensional simulation shows; In the traffic flow model of cellular automaton; The time of vehicle ', space and speed are by discretize on the track; In
process, model is pressed following four regular parallel evolutionaries:
1) quickens rule: in the larger distance of the place ahead, do not have car;
, the characteristic of going with maximal rate corresponding to driver expectation in the reality;
2) rule of slowing down: in the nearer distance in the place ahead, car is arranged;
, the measure that the driver takes to slow down for fear of bumping with front truck;
3) slowing down at random: with Probability p;
; Represent various uncertain factors; For example pavement behavior is bad, different phychology fluctuations of driver etc., the vehicle deceleration that may cause, but the speed of a motor vehicle is all the time greater than a back car;
4) motion:
, vehicle is according to adjusted speed to overtake;
Here, Xn represents the position of n car; Vn representes the speed of n car, this car distance in earlier stage after the every frame of dynamic map refreshes in this speed characterization system; Dn representes the distance between n car and the front truck n+1;
System handles each car on each frame of dynamic map, reads information of vehicles, according to aforementioned four rules, calculate car in the position of next frame, state such as speed, acceleration, and in next frame, carry out corresponding demonstration;
Accomplish model and numerical simulation and also must confirm boundary condition, the present invention adopts improved opening boundary condition:
In the road exit, a car position exceeds the road scope and assert that promptly it rolls the highway section away from, no longer shows; Second car immediately following thereafter becomes a new car; In the road porch, vehicle was located to produce before front truck produces the position with certain probability, and is folded with two car weights before and after preventing;
Described RBF neural network short-term forecasting traffic flow model is through the mode of sample training, and the segment unit magnitude of traffic flow of satisfying the need has more in advance and prediction accurately, and system is provided with an inlet in each unit; Utilize awareness apparatus to detect the situation that gets into vehicle, system design is M rice before each unit, highway section, and vehicle goes according to original cellular Automation Model rule; Y rice in the back; System detects the magnitude of traffic flow A in the dummy model, utilizes RBF neural network short-term forecasting traffic flow model simultaneously, the magnitude of traffic flow in several highway sections before reading; Dope the due magnitude of traffic flow B in this end, unit, highway section this moment; A is compared with B, adjust the parameter in the cellular Automation Model then, comprise acceleration distance, deceleration distance, the random chance of slowing down at random; Make vehicle flowrate in the dummy model in the end tend to the requirement of magnitude of traffic flow B in the Y rice gradually, the vehicle that reaches the highway section intersection shows effect level and smooth and that traffic is constantly revised.
3. traffic zone dynamic map monitoring and controlling forecast according to claim 1 system; It is characterized in that: described color ribbon is made up of red, yellow, green, blue gradual change aberration; Get 7 kinds, the conversion step, the degree of blocking up with the average velocity of corresponding road section vehicle as criterion.
4. traffic zone dynamic map monitoring and controlling forecast according to claim 2 system, it is characterized in that: described M is 900, Y is 100.
5. traffic zone dynamic map monitoring and controlling forecast according to claim 1 system; It is characterized in that: the measuring-signal of described sensing module also derives from ground induction coil, radar velocity measurement equimagnetic frequency, ripple frequency, piezoelectric type sensor; But the perception speed of a motor vehicle, vehicle number, the vision-based detection sensing can obtain information such as vehicle, car look in addition.
6. traffic zone dynamic map monitoring and controlling forecast according to claim 5 system; It is characterized in that: also comprise estimating and induce module; Utilization is estimated and is induced module; Collecting on the basis of current Real-time Road information, analysis such as carry out that zone selection, the judgement of blocking up, time are estimated finally draws the shortest route induction information of arrival destination required time.
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Cited By (38)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103095815A (en) * | 2012-12-31 | 2013-05-08 | 普天新能源有限责任公司 | Mobile device positioning method and mobile device positioning device |
| CN103699761A (en) * | 2014-01-13 | 2014-04-02 | 中国人民解放军国防科学技术大学 | Method and device for displaying general artificial social situation based on data flow mapping |
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000242884A (en) * | 1999-02-23 | 2000-09-08 | Toyota Central Res & Dev Lab Inc | Traffic flow simulation system |
| CN1611917A (en) * | 2003-10-28 | 2005-05-04 | 日本先锋公司 | Device, system and method for reporting a traffic condition and program and recording medium |
| EP1657693A2 (en) * | 2004-11-16 | 2006-05-17 | Microsoft Corporation | Traffic forecasting employing modeling and analysis of probabilistic interdependencies and contextual data |
| CN101127158A (en) * | 2006-08-18 | 2008-02-20 | 株式会社查纳位资讯情报 | Predictive traffic information creating method, predictive traffic information creating apparatus, and traffic information display terminal |
| CN101465058A (en) * | 2009-01-05 | 2009-06-24 | 天津大学 | Method for making decision of inductive unit of intelligent dynamic route inductive system |
| US20100070253A1 (en) * | 2008-09-12 | 2010-03-18 | Yosuke Hirata | Method and system for traffic simulation of road network |
| KR20110124905A (en) * | 2010-05-12 | 2011-11-18 | 한국항공대학교산학협력단 | Data processing method for transportation and communication integrated simulation |
-
2012
- 2012-08-28 CN CN201210309274.XA patent/CN102819954B/en not_active Expired - Fee Related
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000242884A (en) * | 1999-02-23 | 2000-09-08 | Toyota Central Res & Dev Lab Inc | Traffic flow simulation system |
| CN1611917A (en) * | 2003-10-28 | 2005-05-04 | 日本先锋公司 | Device, system and method for reporting a traffic condition and program and recording medium |
| EP1657693A2 (en) * | 2004-11-16 | 2006-05-17 | Microsoft Corporation | Traffic forecasting employing modeling and analysis of probabilistic interdependencies and contextual data |
| CN101127158A (en) * | 2006-08-18 | 2008-02-20 | 株式会社查纳位资讯情报 | Predictive traffic information creating method, predictive traffic information creating apparatus, and traffic information display terminal |
| US20100070253A1 (en) * | 2008-09-12 | 2010-03-18 | Yosuke Hirata | Method and system for traffic simulation of road network |
| CN101465058A (en) * | 2009-01-05 | 2009-06-24 | 天津大学 | Method for making decision of inductive unit of intelligent dynamic route inductive system |
| KR20110124905A (en) * | 2010-05-12 | 2011-11-18 | 한국항공대학교산학협력단 | Data processing method for transportation and communication integrated simulation |
Non-Patent Citations (2)
| Title |
|---|
| 李勃等: "基于监控视频的运动车辆行为分析算法", 《仪器仪表学报》, 15 June 2006 (2006-06-15) * |
| 邢业平等: "基于WLAN路况视频监控系统设计", 《计算机工程与设计》, no. 23, 16 December 2008 (2008-12-16) * |
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