CN106652564A - Traffic flow cellular automaton modeling method under car networking environment - Google Patents
Traffic flow cellular automaton modeling method under car networking environment Download PDFInfo
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/16—Anti-collision systems
- G08G1/166—Anti-collision systems for active traffic, e.g. moving vehicles, pedestrians, bikes
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Abstract
The invention provides a traffic flow cellular automaton modeling method under a car networking environment. The traffic flow cellular automaton modeling method is applicable to vehicle operation rules under the car networking environment, and automaton modeling simulation is combined for realizing simulation on traffic flow, so that traffic flow characteristics are analyzed. On the basis of a Nasch model, characteristics of the car networking environment are combined, single lane operation rules are established, road operation conditions are simulated; and then a double lane model is built, and road traffic condition is more really reflected. The novel method provided by the invention has the advantages that prediction on speed and lane changing condition of a preceding vehicle is added, the traffic flow in a traffic scene can be effectively simulated, and traffic simulation reality is improved; and results show that a vehicle under the car networking environment has higher operation speed, road capacity is greatly improved, and traffic condition is improved.
Description
Technical field
The present invention relates to the traffic flow cellular automaton modeling method under a kind of car networking environment.
Background technology
Start the fast traffic lane that China's automobile pollution enters growth, at the same time, traffic accident from twentieth century end
Occurrence probability also increases year by year.According to the statistics of Traffic Administration Bureau of the Ministry of Public Security, 2010, there is traffic accident 3906164 in the whole nation, together
Than rising 35.9%.Wherein, because road traffic accident causes 219521 of casualties, direct property loss reaches
9.3 hundred million yuan.Safety guarantee is that vehicle runs most basic requirement, although cause traffic accident be the reason for generation it is many,
But the realization first evolved with bus or train route cooperative system of vehicle-mounted active safety system, incontrovertible can be the safety fortune of vehicle
Row provides more reliable decision informations and paths chosen scheme, improves the security of vehicle operation.Car networking is used as the popular back of the body
Scape, is surging forward；It is the key of various countries and technology company's research with regard to the exploration of car networking technology.
The research of present situation car networking, mostly hardware facility such as deviation warning, adaptive cruise, front hit warning function etc.
The optimization of vehicle-mounted function or driver test system and improvement, and the vehicle operation rule in the case of car networking environmental information transparence
The research for defining aspect is relatively fewer.
The content of the invention
It is not enough based on more than, the invention provides the traffic flow cellular automaton modeling method under a kind of car networking environment,
Suitable for vehicle operation rule under car networking environment, with reference to cellular automata modeling, the parameter of quantitative-qualitative analysis traffic flow three
Relation, increased the prediction to front vehicle speed and lane-change situation, improve the authenticity of traffic simulation.
The technology used in the present invention is as follows：A kind of traffic flow cellular automaton modeling method under car networking environment is right
Road conditions are made and being defined as follows：Vehicle is by driver's control；Each vehicle cellular can be communicated with front, obtain front truck
Accurate driving information, and itself transport information is communicated with rear car；Driver is according to the accurate traffic environment information for obtaining
Correct judgement and decision-making are made, method is as follows：
(1). bicycle road cellular automata modeling rule
Step one：Accelerate, vn(t+1)=min (vn(t)+1,vmax) corresponding in actual life, driver is expected with maximum speed
The characteristic of degree traveling；
Step 2：Slow down,
vn(t+1)=min (vn(t+1),dn+v'n+1(t+1))
v'n+1(t+1)=min (vn+1(t),vmax-1,dn+1(t))
Driver in order to hide with front truck bump against and take deceleration measure, because car networking environment is for speed and vehicle location
Transparence, make two workshop minimum spacings be changed into the velocity amplitude sum of the following distance of t two and front truck at the t+1 moment；
V' in formulan+1(t+1) --- represent prediction of speed of the front truck at the t+1 moment；
dn(t) --- represent in t, the space number of this car n cars and front truck n+1；
Step 3：Random slowing down,
Due to the vehicle deceleration that various uncertain factors (as pavement behavior is bad, driver's phychology is not equal) are caused, when
When speed is less than a certain threshold value, vehicle is not slowed down；During more than a certain threshold value, deceleration measure is taken with certain probability；
RD in formula --- represent random chance；
P --- the random number between 0～1 is represented, as p ＜ RD, random slowing down is carried out；
Step 4：Location updating, xn(t+1)=xn(t)+vn(t+1)；Vehicle moves forward according to the speed after adjustment；
(2). two-way traffic cellular automata modeling rule
Two-way traffic can lane-change model car motion process, according to " lane-change-acceleration-deceleration-random slowing down-location updating "
Consequent succession, lane-change rear vehicle on respective track according to bicycle road update rule operation
Lane-change rule is as follows:
D in formulanother--- represent another track in n truck positions and the space number in this track front truck n+1 workshop；
dnback--- represent the space number of track n cars and n-1 workshops；
vi,n+1(t) --- represent t, speed of the n+1 car in i tracks；
Vehicle updates according to above-mentioned rule operation, so as to analyze traffic stream characteristics.
Method proposed by the present invention, increased the prediction to front vehicle speed and lane-change situation, can effectively in traffic scene
Traffic flow emulated, improve traffic simulation authenticity.As a result show：Using vehicle under the car networking environment under this method
With the higher speed of service, road passage capability is greatly enhanced, improves traffic.
Description of the drawings
Fig. 1 is the system operation interface-bicycle road system running environment figure under MATLAB environment；
Fig. 2 is the system operation interface-two-way traffic system running environment figure under MATLAB environment；
Fig. 3 is two-way traffic lane-change schematic diagram；
Fig. 4 is p=0.3, the space-time diagram of k=0.05；
Fig. 5 is p=0.3, the space-time diagram of k=0.2；
Fig. 6 is p=0.3, the space-time diagram of k=0.4；
Fig. 7 is p=0.1, the space-time diagram of k=0.02；
Fig. 8 is p=0.1, the space-time diagram of k=0.2；
Fig. 9 is p=0.1, the space-time diagram of k=0.4；
Figure 10 is p=0.5, the space-time diagram of k=0.02；
Figure 11 is p=0.5, the space-time diagram of k=0.2；
Figure 12 is p=0.5, the space-time diagram of k=0.4；
Figure 13 is different slowing down condition down-off-density maps；
Figure 14 is speed-density figure under the conditions of different slowing downs；
Figure 15 is lane-change probabilistic model figure.
Specific embodiment
Below according to Figure of description citing, the present invention will be further described：
Embodiment 1
Road conditions are made and being defined as follows by the traffic flow cellular automaton modeling method under a kind of car networking environment：Car
By driver's control；Each vehicle cellular can be communicated with front, obtain the accurate driving information of front truck, and itself is handed over
Communication breath is communicated with rear car；Driver makes correct judgement and decision-making, side according to the accurate traffic environment information for obtaining
Method is as follows：
(1). bicycle road cellular automata modeling rule
Step one：Accelerate, vn(t+1)=min (vn(t)+1,vmax) corresponding in actual life, driver is expected with maximum speed
The characteristic of degree traveling；
Step 2：Slow down,
vn(t+1)=min (vn(t+1),dn+v'n+1(t+1))
v'n+1(t+1)=min (vn+1(t),vmax-1,dn+1(t))
Driver in order to hide with front truck bump against and take deceleration measure, because car networking environment is for speed and vehicle location
Transparence, make two workshop minimum spacings be changed into the velocity amplitude sum of the following distance of t two and front truck at the t+1 moment；
V' in formulan+1(t+1) --- represent prediction of speed of the front truck at the t+1 moment；
dn(t) --- represent in t, the space number of this car n cars and front truck n+1；
Step 3：Random slowing down,
Due to the vehicle deceleration that various uncertain factors (as pavement behavior is bad, driver's phychology is not equal) are caused, when
When speed is less than a certain threshold value, vehicle is not slowed down；During more than a certain threshold value, deceleration measure is taken with certain probability；
RD in formula --- represent random chance；
P --- the random number between 0～1 is represented, as p ＜ RD, random slowing down is carried out；
Step 4：Location updating, xn(t+1)=xn(t)+vn(t+1)；Vehicle moves forward according to the speed after adjustment；
(2). two-way traffic cellular automata modeling rule
Two-way traffic can lane-change model car motion process, according to " lane-change-acceleration-deceleration-random slowing down-location updating "
Consequent succession, lane-change rear vehicle on respective track according to bicycle road update rule operation
Lane-change rule is as follows:
D in formulanother--- represent another track in n truck positions and the space number in this track front truck n+1 workshop；
dnback--- represent the space number of track n cars and n-1 workshops；
vi,n+1(t) --- represent t, speed of the n+1 car in i tracks；
Vehicle updates according to above-mentioned rule operation, so as to analyze traffic stream characteristics.
Embodiment 2
Cellular Automata analysis under car networking environment
(1) simulated environment method to set up
According to the rule of above-mentioned introduction, with MATLAB a simulation section is built.In emulation initial time, system initialization
The positional information and velocity information of vehicle, after emulation starts, in each simulation step length, according to model rule to vehicle
Position and speed are updated, until simulation time terminates.
The explanation by taking bicycle road model as an example.Thought and image processing module based on MATLAB matrixes, with the matrix of 1x100
One-lane road cellular is represented, car is indicated with value 1, value 0 is indicated without car, that is, the road for being emulated is made up of 100 cellulars,
The long 5m of each cellular, maximal rate vmax=5.Fig. 1 is the system operation interface under MATLAB environment, and wherein black represents one
Bar bicycle road, the point of white to be represented and have vehicle on the cellular position.Run buttons are represented and start emulation, and Stop buttons represent stopping
Emulation, Quit buttons represent and exit emulation, wherein the digital record in the upper left corner time of system emulation.
Below emulation adopts refined model, it was therefore concluded that.So-called refinement cellular models are exactly, the refinement of road cellular, to make every
Length representated by individual cellular shortens, and the cellular number that vehicle takes also just mutually strains big.
(2) analysis of simulation result
(1) bicycle road model emulation interpretation of result ----space-time diagram is refined
Simulated environment：Road is made up of 1000 cellulars, and other specification is identical with refinement bicycle road model.
Fig. 4-Figure 12 is the refined model when density is 0.2,0.3,0.5 under car networking environment under different slowing down probability
Space-time diagram.It can be seen that as density increases, wagon flow enters metastable state region；Under the conditions of equal densities, with slowing down it is general
Rate increases, and the traffic behavior for stopping and going is more obvious.When density very little, state of the vehicle in freely travelling, slowing down probability
It is unobvious to Influence of Bicycle；When density is increased to a certain degree, traffic flow reaches metastable condition, now vehicle operation shape
State is chaotic, and slowing down probability has a huge impact to vehicle operation；As traffic current density further increases, congestion in road is tight
Weight, vehicle traveling is subject to very big obstruction, and slowing down probability affects also unobvious on it.
(2) bicycle road model emulation interpretation of result is refined ----flow-density relationship analysis
Figure 13 lists the flow-density map of the improved model under NS models and car networking environment, both primary condition
It is identical that (12) road length 200, maximal rate is.The slowing down probability of NS models is p=0.3, improved model given herein
Slowing down probability is respectively p=0.1, p=0.3, p=0.5.
Known by figure, under NS environment, critical density and maximum vehicle flowrate are all less.Under the conditions of the different slowing downs of contrast (p=0.1,
P=0.3, p=0.5) car networking environment improved model curve, it can be seen that when vehicle density is relatively low or higher, slowing down
Impact of the probability to the magnitude of traffic flow be not notable.This is because when vehicle density is less, road vehicle is few, space headway
Greatly, the random slowing down of certain car does not interfere with the normally travel of other vehicles；When vehicle density is excessive, due to space headway
Little, overall operation speed is low, and the change of random slowing down probability has not affected the change of overall flow.Density is in 0.1-0.7
Between when, under the conditions of equal densities, as random slowing down probability increases, flow is decreased obviously, critical intensity value also by
0.37 is reduced to 0.3.
(3) bicycle road model emulation interpretation of result is refined ----Velocity-density relation is analyzed
Figure 14 lists the speed-density figure of NS models and the improved model under car networking environment, both primary condition
It is identical that (12) road length 200, maximal rate is.As seen from the figure, there is lower speed, and speed pair under traditional NS environment
Variable density is more sensitive.The speed-density curve of car networking improved model under the conditions of the different slowing downs of contrast, it is known that the same terms
Under, slowing down probability is bigger, and speed is less.When density less (being less than 0.1) or larger (being higher than 0.7), slowing down probability is to it
Affect negligible.
(4) two-lane model simulation analysis are refined
For the lane-change model of two-way traffic, the arrival rate for constantly changing vehicle is the volume of traffic in section, using lane-change probability
The lane-change situation of vehicle, such as Figure 15 under to characterize car networking environment.
As seen from the figure, when flow-rate ratio is less, road vehicle distributes very evenly, and the room between car and car is basic
Remain vmax, now vehicle and lane-change not being needed, two-way traffic has also reformed into two separate bicycle roads.With flow
Continuation increase, the orderly distribution of vehicle is gradually suppressed due to the increase of flow.Spacing distribution can be produced when chaotic
Meet the condition of lane-change, the probability of vehicle lane-changing can increase therewith.With the further increase of flow, traffic flow can enter metastable
State, traffic now is more chaotic, it is possible to cause the phenomenon for lane-change probability local maximum occur.When flow is further continued for
During increase, the spacing between vehicle all diminishes, and the chance of lane-change also reduces therewith, and lane-change probability is also just gradually reduced.
Due under car networking environment to traffic information relative transparent, to road conditions accuracy of judgement, thus with density increase,
There is bigger lane-change probability compared with conventional environment.
Claims (1)
1. road conditions are made and being defined as follows by the traffic flow cellular automaton modeling method under a kind of car networking environment：Vehicle
By driver's control；Each vehicle cellular can be communicated with front, obtain the accurate driving information of front truck, and by itself traffic
Information is communicated with rear car；Driver makes correct judgement and decision-making according to the accurate traffic environment information for obtaining, and it is special
Levy and be, method is as follows：
(1) bicycle road cellular automata modeling rule
Step one：Accelerate, vn(t+1)=min (vn(t)+1,vmax) corresponding in actual life, driver is expected with maximal rate row
The characteristic sailed；
Step 2：Slow down,
vn(t+1)=min (vn(t+1),dn+v'n+1(t+1))
v'n+1(t+1)=min (vn+1(t),vmax-1,dn+1(t))
Driver bump against with front truck and take deceleration measure to hide, because car networking environment it is saturating for speed and vehicle location
Brightization, makes two workshop minimum spacings be changed into the velocity amplitude sum of the following distance of t two and front truck at the t+1 moment；
V' in formulan+1(t+1) --- represent prediction of speed of the front truck at the t+1 moment；
dn(t) --- represent in t, the space number of this car n cars and front truck n+1；
Step 3：Random slowing down,
Due to the vehicle deceleration that various uncertain factors (as pavement behavior is bad, driver's phychology is not equal) are caused, work as speed
During less than a certain threshold value, vehicle is not slowed down；During more than a certain threshold value, deceleration measure is taken with certain probability；
RD in formula --- represent random chance；
P --- the random number between 0～1 is represented, as p ＜ RD, random slowing down is carried out；
Step 4：Location updating, xn(t+1)=xn(t)+vn(t+1)；Vehicle moves forward according to the speed after adjustment；
(2) two-way traffic cellular automata modeling rule
Two-way traffic can lane-change model car motion process, according to the suitable of " lane-change-acceleration-deceleration-random slowing down-location updating "
Sequence is developed, and lane-change rear vehicle updates rule operation on respective track according to bicycle road
Lane-change rule is as follows:
D in formulanother--- represent another track in n truck positions and the space number in this track front truck n+1 workshop；
dnback--- represent the space number of track n cars and n-1 workshops；
vi,n+1(t) --- represent t, speed of the n+1 car in i tracks；
Vehicle updates according to above-mentioned rule operation, so as to analyze traffic stream characteristics.
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| CN110910642A (en) * | 2019-12-02 | 2020-03-24 | 安徽百诚慧通科技有限公司 | Bus route analysis method considering hybrid traffic system |
| CN111243309A (en) * | 2020-01-10 | 2020-06-05 | 北京航空航天大学 | Expressway traffic flow full-sample trajectory reconstruction method based on automatic driving vehicle movement detection |
| CN111127953A (en) * | 2020-01-10 | 2020-05-08 | 长沙理工大学 | Vehicle ramp merging method based on network connection automatic driving environment |
| CN111754769A (en) * | 2020-05-22 | 2020-10-09 | 浙江工业大学 | Road network traffic flow characteristic simulation method based on Manhattan city network with long-range continuous edges |
| CN112216148A (en) * | 2020-09-21 | 2021-01-12 | 西安工程大学 | Lane changing guiding method for double-lane vehicle under vehicle-road cooperation |
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| CN114582127A (en) * | 2022-03-07 | 2022-06-03 | 中国公路工程咨询集团有限公司 | Traffic flow model simulation method and system and abnormal traffic event prediction method |
| CN117690288A (en) * | 2023-11-23 | 2024-03-12 | 中山大学·深圳 | Mixed traffic flow simulation method and system considering bus stops |
| CN117690288B (en) * | 2023-11-23 | 2024-08-16 | 中山大学·深圳 | Mixed traffic flow simulation method and system considering bus stops |
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