CN110087280A - A kind of traffic density evaluation method based on beacon message - Google Patents
A kind of traffic density evaluation method based on beacon message Download PDFInfo
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- CN110087280A CN110087280A CN201910401712.7A CN201910401712A CN110087280A CN 110087280 A CN110087280 A CN 110087280A CN 201910401712 A CN201910401712 A CN 201910401712A CN 110087280 A CN110087280 A CN 110087280A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W40/00—Communication routing or communication path finding
- H04W40/24—Connectivity information management, e.g. connectivity discovery or connectivity update
- H04W40/244—Connectivity information management, e.g. connectivity discovery or connectivity update using a network of reference devices, e.g. beaconing
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W40/00—Communication routing or communication path finding
- H04W40/24—Connectivity information management, e.g. connectivity discovery or connectivity update
- H04W40/248—Connectivity information update
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/025—Services making use of location information using location based information parameters
- H04W4/026—Services making use of location information using location based information parameters using orientation information, e.g. compass
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/025—Services making use of location information using location based information parameters
- H04W4/027—Services making use of location information using location based information parameters using movement velocity, acceleration information
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
- H04W4/46—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for vehicle-to-vehicle communication [V2V]
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The present invention relates to a kind of traffic density evaluation method based on beacon message, belongs to vehicular ad hoc network field.This method comprises: S1: vehicle is collected by beacon message and the traffic information of processing surrounding vehicles, building vehicle information of neighbor nodes table calculates the neighbor node quantity of vehicle according to the information in information of neighbor nodes table；S2: according to the information of neighbor nodes table of building, the distribution situation of vehicle neighbor node is classified, remaining time of the neighbor node within the scope of estimation vehicle communication is calculated in conjunction with the distribution classification of each neighbor node, information of neighbor nodes table is updated according to remaining time；S3: establishing the vehicle headway distribution function of vehicle, seeks the maximal possibility estimation of corresponding distribution function to estimate traffic density.
Description
Technical field
The invention belongs to vehicular ad hoc network fields, are related to a kind of traffic density evaluation method based on beacon message.
Background technique
With the increase of automobile quantity, vehicle safety, traffic congestion and driving experience have become by public concern
Three problems.Establish an intelligence, these problems can be effectively relieved in the shipment control system of networking, therefore intelligence hand over
Way system (Intelligent Transportation System, ITS) comes into being.Vehicular ad hoc network is intelligent friendship
The important component of way system, vehicle in vehicle-mounted net technology can realize the communication between vehicle, root by wireless communication
Road conditions can be predicted according to the information vehicle received, reduce the probability that traffic accident occurs, while can also assist driving, improvement is driven
The driving for the person of sailing is experienced.
Traffic density is one of the important indicator of assessment road traffic condition, while also to the communication of vehicular ad hoc network
Performance has larger impact.Current most of traffic density estimation methods are that density estimation, example are completed based on infrastructure
Such as, a kind of traffic density appraisal procedure is to generate the full convolutional neural networks model of multiple row using deep learning training, by mileage chart
It is inputted as information is used as, the acquisition traffic density distribution map exported according to network model.Although this method daytime can accurately and
Estimation tasks are rapidly completed, but at night since the precision of images is substantially reduced, so that there are large errors for traffic density.It is another
Traffic density appraisal procedure is road video to be obtained by video data collection device, and extract three frames in video in remote terminal
Image records due to takeing a long time and handles video to calculate traffic density, this density estimation method
It cannot be used for real-time vehicle density estimation.Above method needs will test equipment (such as inductive loop detector or traffic monitoring
Video camera) different location is mounted on to obtain corresponding information of vehicles, and be affected by external condition, the processing time is longer,
Applicability is not strong.
With the rise of vehicular ad hoc network, vehicle node can be in communication with each other based on wireless channel, this makes vehicle
The infrastructure that any fixation can not depended on is collected and processing traffic information, and vehicle can be according to receiving in the process of moving
Beacon message complete traffic density estimation.It, can be according to vehicle after traffic density of the vehicle around obtaining in vehicle-mounted net
Density formulates corresponding communication mechanism, and to improve the performance of In-vehicle networking, therefore the estimation of traffic density has important grind
Study carefully meaning.
Summary of the invention
In view of this, the purpose of the present invention is to provide a kind of traffic density evaluation method based on beacon message, application
Traffic density estimation in vehicular ad hoc network based on inter-vehicular communication.
In order to achieve the above objectives, the invention provides the following technical scheme:
A kind of traffic density evaluation method based on beacon message, which is characterized in that this method specifically includes the following steps:
S1: the traffic information of surrounding vehicles, building are collected and handled to vehicle by beacon message (Beacon Message)
Vehicle information of neighbor nodes table calculates the neighbor node quantity of vehicle according to the information in information of neighbor nodes table；
S2: according to the information of neighbor nodes table of building, the distribution situation of vehicle neighbor node is classified, in conjunction with each
The distribution classification of neighbor node calculates remaining time of the neighbor node within the scope of estimation vehicle communication, is updated according to remaining time
Information of neighbor nodes table；
S3: establishing the vehicle headway distribution function of vehicle, seeks the maximal possibility estimation of corresponding distribution function to estimate vehicle
Density.
Further, the step S1 specifically includes the following steps:
S11: neighbor node periodic broadcast beacon message, beacon message include vehicle location, speed, driving direction and
Neighbor node number, wherein neighbor node number is divided into the neighbor node number positioned at node front and back；
S12: vehicle establishes information of neighbor nodes table according to the beacon message received, based on the location information in information table
Vehicle calculates the relative position of each neighbor node and vehicle, filters out farthest adjacent apart from vehicle in vehicle front and rear
Occupy node；
S13: vehicle calculates the neighbor node number within the scope of a jump, according to most according to data in information of neighbor nodes table
The neighbor node number of a remote hop node, calculates the quantity of two-hop neighbor node.
Further, the step S2 specifically includes the following steps:
S21: the distribution situation of neighbor node is divided into according to the position of neighbor node front and back, velocity magnitude and driving direction
Six classes；
S22: the speed of vehicle is set as Vo, spread scope R, the speed of k-th of neighbor node of vehicle is Vk, with vehicle
Distance is Rk, in conjunction with six class distribution situations of neighbor node, when calculating retention of k-th of neighbor node in vehicle spread scope
Between tk, its calculation formula is:
Wherein, the location information of P expression storage neighbor node, P are that 1 expression is located at vehicle front, and P is expressed as vehicle for -1
Rear；The directional information of D expression storage neighbor node, D are that 1 expression is identical as direction of traffic, and D is -1 expression and direction of traffic phase
Instead；Q indicates the velocity information of storage neighbor node, and Q is that 1 expression speed is bigger than vehicle, and Q indicates that speed is smaller than vehicle for -1；
S23: setting the remaining time of neighbor node to the life span of corresponding node in vehicle information of neighbor nodes table,
If in the beacon message arrival for updating corresponding node in interval, it is believed that the node leaves vehicle communication range, deletes information
Nodal information in table.
Further, the step S3 specifically includes the following steps:
S31: vehicle A is set to estimate vehicle, x1Indicate that vehicle A mono- jumps the neighbor node number in range, y1Indicate farthest neighbour
It occupies node B and vehicle A is separated by a distance, n and r1For constant, neighbours' section that vehicle A mono- jumps physical presence in range is respectively indicated
The actual range that points and node B and vehicle A are separated by, calculating in vehicle A spread scope is R, and traffic density is ρ/km
Situation, the neighbor node number x of vehicle A1=n, and the distance y for jumping farthest neighbor node B to one1=r1Probability P (y1=r1,x1
=n)；
S32: setting vehicle C as the farthest hop neighbor node of node B, x2Indicate that node B mono- jumps the neighbor node in range
Number, y2Indicate the spread scope boundary of vehicle A to the distance of vehicle C, m and r2For constant, respectively indicates node B mono- and jump in range
The neighbor node number of physical presence and the spread scope boundary of vehicle A calculate vehicle A two-hop neighbors to the actual range of vehicle C
Number of nodes is n+m, and the distance for arriving the farthest neighbor node C of double bounce is r2Probability P (the y of+R1=r1,x1=n, y2=r2,x2=
m)；
S33: being based on maximal possibility estimation, maximizes probability P (y1=m1,x1=n1,y2=m2,x2=n2) traffic density ρ
For best estimate, its calculation formula is:
Wherein, K indicates number of samples used by estimating, ni+miIndicate the two-hop neighbors that vehicle is estimated in i-th of sample
Number of nodes.
The beneficial effects of the present invention are: the present invention can be applied in vehicular ad hoc network based on inter-vehicular communication
Traffic density estimation.Compared with conventional truck density estimation method, application range of the present invention is wider, and environmental suitability is stronger, estimates
Calculation precision is higher, and speed is faster.
Other advantages, target and feature of the invention will be illustrated in the following description to a certain extent, and
And to a certain extent, based on will be apparent to those skilled in the art to investigating hereafter, Huo Zheke
To be instructed from the practice of the present invention.Target of the invention and other advantages can be realized by following specification and
It obtains.
Detailed description of the invention
To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention is made below in conjunction with attached drawing excellent
The detailed description of choosing, in which:
Fig. 1 is that the traffic density of the present invention based on beacon message estimates flow chart；
Fig. 2 is that neighbor node of the present invention updates flow chart；
Fig. 3 is neighbor node distribution situation figure of the present invention；
Fig. 4 is vehicle distribution map of the present invention；
Fig. 5 is the absolute error analogous diagram of method of the present invention.
Specific embodiment
Illustrate embodiments of the present invention below by way of specific specific example, those skilled in the art can be by this specification
Other advantages and efficacy of the present invention can be easily understood for disclosed content.The present invention can also pass through in addition different specific realities
The mode of applying is embodied or practiced, the various details in this specification can also based on different viewpoints and application, without departing from
Various modifications or alterations are carried out under spirit of the invention.It should be noted that diagram provided in following embodiment is only to show
Meaning mode illustrates basic conception of the invention, and in the absence of conflict, the feature in following embodiment and embodiment can phase
Mutually combination.
As shown in Figure 1, the acquisition of traffic density is broadly divided into three steps in the present invention, the present invention provides one kind to be based on first
The neighbor node estimation method of beacon message establishes information of neighbor nodes table based on the beacon message received, and according in table
Information calculates the neighbor node within the scope of vehicle double bounce；It then, will according to the position of node front and back, driving direction and velocity magnitude
The distribution situation of neighbor node is divided into six classes, corresponding remaining time is calculated for the distribution of every class, to update information table；Most
After establish vehicle space distribution function, obtain the estimated value of traffic density by seeking the maximal possibility estimation of distribution function.
A kind of traffic density evaluation method based on neighbor node of the present invention, comprising the following steps:
(1) it establishes information of neighbor nodes table and seeks neighbor node number
1) neighbor node periodic broadcast beacon message, beacon message include the position of node itself, speed, driving direction
And neighbor node number, wherein neighbor node number is divided into the neighbor node number positioned at node front and back；
2) vehicle establishes the information table of neighbor node according to the beacon message received, specific as shown in table 1, vehicle label
It is the unique identification of each neighbor node distribution for estimation vehicle；Position is neighbor node and the relative position for estimating vehicle, table
Show that it is located at estimation vehicle front or rear；Distance is that neighbor node and estimation vehicle are separated by a distance；Before neighbor node
Side or rear vehicle number is then the hop neighbor node for estimating vehicle neighbor node；Remaining time is the node in table
Life span, if remaining time is reduced to zero, still the beacon message of corresponding node does not arrive, then deletes the letter of the node
Breath.
1 information of neighbor nodes table of table
3) according to location information and range information in table 1, estimate that vehicle can filter out the neighbours of forefront and rearmost
Its information is stored in table 2 by node.If in table 1, estimating that the number of nodes of vehicle front is nfront, the number of nodes being located behind is
nback, then have n=nfront+nback。
The farthest information of neighbor nodes table of table 2
It is respectively as follows: in conjunction with the two-hop neighbor node number of the available vehicle front of information and rear in table 2
Estimate neighbor node number total within the scope of vehicle double bounce are as follows:
Ntotal=Nfront+Nback=nfront+nback+mfront+mback=n+mfront+mback (2)
(2) information of neighbor nodes table is updated
Steps are as follows for neighbor node update, referring to fig. 2:
1) information for whether having beacon message source node in information table is judged after receiving beacon message, if it exists then more
New corresponding node information then increases a line newly to memory node information in information table if it does not exist.
2) remaining time is the key that information table updates, and specific step is as follows for calculating:
A. the distribution situation of neighbor node is divided into six according to the position of neighbor node front and back, velocity magnitude and driving direction
Class, it is specific as shown in Figure 3.
B. the speed of pick-up is Vo, spread scope R, the speed of k-th of neighbor node of vehicle is Vk, with vehicle away from
From for Rk, in conjunction with six kinds of distribution situations of neighbor node, calculate remaining time of k-th of neighbor node in vehicle spread scope
tk, its calculation formula is:
Wherein, the location information of P expression storage neighbor node, P are that 1 expression is located at vehicle front, and P is expressed as vehicle for -1
Rear；The directional information of D expression storage neighbor node, D are that 1 expression is identical as direction of traffic, and D is -1 expression and direction of traffic phase
Instead；Q indicates the velocity information of storage neighbor node, and Q is that 1 expression speed is bigger than vehicle, and Q indicates that speed is smaller than vehicle for -1；
3) remaining time is that the life span of beacon table interior joint does not still correspond to if remaining time is reduced to zero
The beacon message of node arrives, then it is assumed that the node has sailed out of the communication range of estimation vehicle, deletes the information of the node.
(3) traffic density is estimated
Referring to fig. 4, traffic density estimation steps are as follows for vehicle distribution situation:
1) vehicle A is set to estimate vehicle, x1Indicate that vehicle A mono- jumps the neighbor node number in range, y1Indicate farthest neighbours
Node B and vehicle A are separated by a distance, n and r1For constant, the neighbor node that vehicle A mono- jumps physical presence in range is respectively indicated
The actual range that several and node B and vehicle A are separated by is in the case that R traffic density is ρ/km in vehicle A spread scope,
One jumps the neighbor node number x in range1The probability of=n are as follows:
Then in x1Under conditions of=n, the distance between a farthest hop neighbor node B and vehicle A y1≤r1Probability are as follows:
Combinatorial formula (4) can be obtained further:
The neighbor node number x of the available vehicle A of derivative is sought to formula (6)1=n, and farthest neighbor node B is jumped to one
Distance y1=r1Probability:
2) vehicle C is set as the farthest hop neighbor node of node B, x2Indicate that node B mono- jumps the neighbor node number in range,
As the second hop neighbor of vehicle A number of nodes, y2Indicate the transmission range boundary of vehicle A to the distance of vehicle C, m and r2For constant,
Respectively indicate node B mono- jump range in physical presence neighbor node number and vehicle A spread scope boundary to vehicle C reality
Distance, due to y1=r1Indicate (r1, R) in without node (otherwise the node will become vehicle A farthest hop neighbor node).
Therefore the two-hop neighbor node of vehicle A must be positioned at (R, R+r2) in, then in x1=n, y1=r1In the case where, the second of vehicle A
Hop neighbor number of nodes x2The probability of=m are as follows:
Calculating vehicle A two-hop neighbor node number in conjunction with formula (7) is n+m, and is to the distance of the farthest neighbor node C of double bounce
r2The probability of+R, its calculation formula is:
3) it is based on maximal possibility estimation, maximizes probability P (y1=m1,x1=n1,y2=m2,x2=n2) traffic density ρ be
Best estimate, steps are as follows for specific calculating:
According to probability P (y1=m1,x1=n1,y2=m2,x2=n2) maximum likelihood function of the foundation about traffic density ρ,
Calculation formula are as follows:
Logarithm is taken to obtain formula (9):
To lnL (ρ) derivation:
Enabling formula (10) to be equal to 0 can obtain:
So the optimum estimation value of traffic densityAre as follows:
Wherein K indicates number of samples used by estimating, ni+miIndicate the two-hop neighbors section that vehicle is estimated in i-th of sample
Points, can be calculated according to formula (1).
Effect of the invention is further described below with reference to emulation experiment:
Emulation experiment is completed using SUMO and NS3.26.One highway mobility model is generated in SUMO software,
The distance between vehicle obeys the exponential distribution that average density is ρ/km in model, and the propagation radius of vehicle is set as
250m, movement speed are set as 30m/s, and the data package size of beacon message is 500byte, transmission rate 6Mbit/s, broadcast
Period is 0.05s.The best estimate that traffic density evaluation method is sought is compared with global density p, and is calculated absolute
Error, its calculation formula isRepeatedly emulation record data, and it is exhausted by being sought to each emulation data
The mean absolute error (Mean Absolute Error, MAE) that error averaged is calculated.Simulation result such as Fig. 5 institute
Show, with the increase of sample estimate amount, the accuracy based on estimation density is gradually risen.For example, in ρ=10/km,
By the way that sample size is increased to 10 from 1, estimate that the MAE of density is reduced to 16% or so from 40%.As number of samples k=10
When, estimate that the MAE of density is lower than 10% in most cases, it is seen that in the case where estimation vehicle propagates the limited situation of radius R, estimation side
Method is estimated that accuracy is higher using more samples
Finally, it is stated that the above examples are only used to illustrate the technical scheme of the present invention and are not limiting, although referring to compared with
Good embodiment describes the invention in detail, those skilled in the art should understand that, it can be to skill of the invention
Art scheme is modified or replaced equivalently, and without departing from the objective and range of the technical program, should all be covered in the present invention
Scope of the claims in.
Claims (4)
1. a kind of traffic density evaluation method based on beacon message, which is characterized in that this method specifically includes the following steps:
S1: the traffic information of surrounding vehicles is collected and handled to vehicle by beacon message, constructs vehicle information of neighbor nodes table,
The neighbor node quantity of vehicle is calculated according to the information in information of neighbor nodes table；
S2: according to the information of neighbor nodes table of building, the distribution situation of vehicle neighbor node is classified, in conjunction with each neighbours
The distribution classification of node calculates remaining time of the neighbor node within the scope of estimation vehicle communication, according to remaining time more new neighbor
Informational table of nodes；
S3: establishing the vehicle headway distribution function of vehicle, seeks the maximal possibility estimation of corresponding distribution function to estimate that vehicle is close
Degree.
2. a kind of traffic density evaluation method based on beacon message according to claim 1, which is characterized in that the step
Rapid S1 specifically includes the following steps:
S11: neighbor node periodic broadcast beacon message, beacon message include vehicle location, speed, driving direction and neighbours
Number of nodes, wherein neighbor node number is divided into the neighbor node number positioned at node front and back；
S12: vehicle establishes information of neighbor nodes table according to the beacon message received, based on the location information vehicle in information table
The relative position for calculating each neighbor node and vehicle is filtered out in vehicle front and rear apart from the farthest neighbours' section of vehicle
Point；
S13: vehicle calculates the neighbor node number within the scope of a jump according to data in information of neighbor nodes table, according to farthest one
The neighbor node number of hop node calculates the quantity of two-hop neighbor node.
3. a kind of traffic density evaluation method based on beacon message according to claim 1, which is characterized in that the step
Rapid S2 specifically includes the following steps:
S21: the distribution situation of neighbor node is divided into six according to the position of neighbor node front and back, velocity magnitude and driving direction
Class；
S22: the speed of vehicle is set as Vo, spread scope R, the speed of k-th of neighbor node of vehicle is Vk, at a distance from vehicle
For Rk, in conjunction with six class distribution situations of neighbor node, calculate remaining time t of k-th of neighbor node in vehicle spread scopek,
Its calculation formula is:
Wherein, the location information of P expression storage neighbor node, P is that 1 expression is located at vehicle front, after P is expressed as vehicle for -1
Side；The directional information of D expression storage neighbor node, D are that 1 expression is identical as direction of traffic, and D is -1 expression and direction of traffic phase
Instead；Q indicates the velocity information of storage neighbor node, and Q is that 1 expression speed is bigger than vehicle, and Q indicates that speed is smaller than vehicle for -1；
S23: setting the remaining time of neighbor node to the life span of corresponding node in vehicle information of neighbor nodes table, if
Update the beacon message arrival of not corresponding node in interval, it is believed that the node leaves vehicle communication range, deletes in information table
Nodal information.
4. a kind of traffic density evaluation method based on beacon message according to claim 1, which is characterized in that the step
Rapid S3 specifically includes the following steps:
S31: vehicle A is set to estimate vehicle, x1Indicate that vehicle A mono- jumps the neighbor node number in range, y1Indicate farthest neighbours' section
Point B and vehicle A are separated by a distance, n and r1For constant, the neighbor node number that vehicle A mono- jumps physical presence in range is respectively indicated
The case where actual range being separated by with node B and vehicle A, calculating in vehicle A spread scope is R, and traffic density is ρ/km,
The neighbor node number x of vehicle A1=n, and the distance y for jumping farthest neighbor node B to one1=r1Probability P (y1=r1,x1=n)；
S32: setting vehicle C as the farthest hop neighbor node of node B, x2Indicate that node B mono- jumps the neighbor node number in range, y2
Indicate the spread scope boundary of vehicle A to the distance of vehicle C, m and r2For constant, respectively indicates node B mono- and jump reality in range
The spread scope boundary of existing neighbor node number and vehicle A calculate vehicle A two-hop neighbor node to the actual range of vehicle C
Number is n+m, and the distance for arriving the farthest neighbor node C of double bounce is r2Probability P (the y of+R1=r1,x1=n, y2=r2,x2=m)；
S33: being based on maximal possibility estimation, maximizes probability P (y1=m1,x1=n1,y2=m2,x2=n2) traffic density ρ be most
Good estimated value, its calculation formula is:
Wherein, K indicates number of samples used by estimating, ni+miIndicate the two-hop neighbor node that vehicle is estimated in i-th of sample
Number.
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| CN111709583A (en) * | 2020-06-18 | 2020-09-25 | 北京字节跳动网络技术有限公司 | User retention time generation method and device, electronic equipment and medium |
| CN111709583B (en) * | 2020-06-18 | 2023-05-23 | 抖音视界有限公司 | User retention time generation method, device, electronic equipment and medium |
| CN115065957A (en) * | 2022-06-08 | 2022-09-16 | 重庆邮电大学 | Distributed vehicle traffic management method under vehicle-mounted self-organizing network |
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