CN111860396A - Method for identifying and summarizing congestion conditions of current area of vehicle - Google Patents
Method for identifying and summarizing congestion conditions of current area of vehicle Download PDFInfo
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- G06V20/50—Context or environment of the image
- G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
- G06V20/588—Recognition of the road, e.g. of lane markings; Recognition of the vehicle driving pattern in relation to the road
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
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- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/243—Classification techniques relating to the number of classes
- G06F18/24323—Tree-organised classifiers
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
Abstract
The invention provides a method for identifying and summarizing congestion conditions of a current area of a vehicle, which comprises the following steps: step S1, carrying out congestion form classification and standard determination; step S2, determining an attention area and extracting congestion information of the attention area; step S3, clustering and analyzing and merging adjacent road sections with the same or similar congestion properties; in step S4, the congestion status is determined according to the criteria determined in step S1. The invention can quickly and scientifically analyze, organize and express the road condition information of the current attention area of the driver, and solves the problems of strong subjectivity and no pertinence of the existing method.
Description
Technical Field
The invention relates to the technical field of intelligent traffic, in particular to a method for identifying and summarizing current regional congestion conditions of vehicles.
Background
Nowadays, China has entered the era of automobile popularization, and traffic congestion is a common problem in various big cities. According to the investigation that navigation can not be started when most cities go out, but the demand of knowing the road condition ahead still exists, the existing means is mainly through congestion information broadcasted by a broadcasting station or displayed by a road side information board. The former is that the radio station is expressed according to real-time road conditions, and is greatly influenced by experience of broadcasting personnel and standard preference, and if a driver does not listen to broadcasting or a section where a broadcasted non-driver is currently located, the driver cannot obtain effective information; the latter is not installed in all areas in the city or the non-driver concerned area of release, and the information board is mostly simply to release the expression form, can not richly express the road conditions, and the driver can not obtain effective information equally.
The road condition data of the electronic map is collected based on the vehicle-mounted terminal, and the current surrounding road conditions can be provided for a driver. Voice broadcasting is an effective way because it is impossible for a driver to see a picture while driving for safety reasons. However, congestion information of hundreds of surrounding road sections is difficult to be reported one by one in a short time before a driver selects a next intersection, and the reported driver cannot know the overall situation, so that the road condition needs to be summarized highly by an artificial intelligence method, the driver can conveniently and safely know the road condition in time to make a selection, and congestion is avoided.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides a method for identifying and summarizing the congestion condition of the current area of the vehicle, which can quickly, scientifically, generally and organically analyze and express the road condition information of the current attention area of a driver, and solves the problems of strong subjectivity and no pertinence of the existing method.
The embodiment of the invention adopts the technical scheme that:
a method of identifying congestion conditions that summarize a current region of a vehicle, comprising the steps of:
step S1, carrying out congestion form classification and standard determination;
step S2, determining an attention area and extracting congestion information of the attention area;
step S3, clustering and analyzing and merging adjacent road sections with the same or similar congestion properties;
in step S4, the congestion status is determined according to the criteria determined in step S1.
Further, in step S1, the congestion status includes: nodal congestion, linear congestion, and regional congestion; the criteria for congestion patterns include:
the node congestion is a state that the congestion length is smaller than a set length threshold;
the linear congestion is a form in which the congestion length is equal to or greater than a set length threshold;
the regional congestion is a form in which the congested road occupies a regional road equal to or more than a set percentage threshold.
Further, linear congestion is further classified into homogeneous linear congestion, intermittent linear congestion, and fishbone-shaped linear congestion.
Further, in the present invention,
for nodal congestion, the expression of voice broadcast is as follows: road name + location + degree;
for linear congestion, the expression of voice broadcast is as follows: road name + "position-position" + subtype + degree;
for regional congestion, the expression of voice broadcast is as follows: position + range + degree.
Further, in step S3, merging the neighboring objects with high similarity into a whole by using a K-means clustering algorithm, that is, merging the road sections with similar congestion properties; the congestion property refers to a congestion level in the congestion degree; acquiring congestion data of all road section measuring points in an area as k clusters of the clusters, wherein each cluster is described by a centroid; in the Euclidean distance
As the distance between classes, xik、xjkCalculating the distance between the classes by a shortest distance method, wherein p is the number of the objects in the congested road section; congestion data cluster
And
a distance d betweenij(ii) a The method mainly comprises the following steps:
(1) k-means clustering algorithm: firstly, initializing an algorithm, namely, creating k centroids required by the algorithm, then distributing the measured point data to each centroid, iteratively calculating the inter-class distance, returning a distribution result, and obtaining the clustering and classification of the measured point congestion data;
(2) creating and optimizing centroids: firstly, taking k initial points to randomly determine as a mass center; then, taking a cluster, distributing each point in the data set, finding out the centroid of each point closest to the point, finding out the cluster corresponding to the centroid, and distributing the cluster into the centroid; after this step is completed, the centroid of each cluster is updated to be the average of all the points of the cluster;
(3) calculating the distance between the centroids: and (3) importing the data into a K-means clustering algorithm, storing the data into a dM matrix, calculating Euclidean distances, establishing a set to store the generated random centroid, and constructing a data set used by the algorithm.
Further, in step S4, based on the congestion shape object formed after the approximate road segments have been merged, a Sigmoid classification function and Logistic regression are used for discrimination; the method specifically comprises the following steps:
(1) determining an optimal regression coefficient by adopting a Sigmoid function gradient rising method; the specific calculation formula is as follows:
let the input of this function (1) be z, then the formula is as follows:
z＝w0x0+w1x1+w2x2+…+wnxn(2)
the above formula is in the form of a linear equation, the vector form of which can be written as z ═ wTx; wherein the vector x is the input data of the classifier, and the vector w is the best classification parameter to be found; by passing
To obtain the gradient value of the function; if the step length of the operator movement is recorded as alpha, the iterative formula of the gradient ascent algorithm is as follows
(2) And (4) judging the decision tree boundary, namely the boundary of the form classification in the step S1, and performing Logistic regression classification on the traffic jam form.
The invention achieves the following beneficial effects: the intelligent traffic jam recognition system can effectively recognize the form of traffic jam in an artificial and intelligent manner, does not need a driver to recognize a picture during driving, and intelligently recognizes the form of the traffic jam and broadcasts the form to the driver in a voice manner; the method and the system can summarize the congested road conditions of the road, can effectively reduce the number of voice broadcast, and can only broadcast the congested road conditions of the region of interest of a driver, so that the driver can conveniently and quickly know the traffic conditions of the region of interest of the driver.
Drawings
FIG. 1 is a flow chart of a method in an embodiment of the invention.
Fig. 2 is a schematic diagram of a node congestion state according to an embodiment of the present invention.
FIG. 3 is a schematic diagram of linear congestion according to an embodiment of the present invention.
Fig. 4 is a schematic diagram of a regional congestion state in an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The method for identifying and summarizing the congestion condition of the current area of the vehicle, provided by the embodiment, comprises the following steps:
step S1, carrying out congestion form classification and standard determination;
the congestion in the city is expressed in three forms of point, line and plane, namely three forms of nodal congestion, linear congestion and regional congestion; the criteria for congestion patterns include:
the node congestion is a form of congestion length less than 100 meters;
linear congestion is a form in which the congestion length is equal to or greater than 100 meters;
the regional congestion is a form that the congested road occupies 60% or more of the regional road;
linear congestion is further divided into homogeneous linear congestion, discontinuous linear congestion and fishbone-shaped linear congestion; the discontinuous linear congestion refers to that a plurality of road sections in a road are discontinuously congested, the fishbone-shaped linear congestion refers to linear congestion, and congestion conditions also exist in some import and export ramps or branches of a trunk road;
the elements of the voice broadcast include: road name, location, extent, range; the degree adopts national standard GB 50220-2011;
for nodal congestion, the expression of voice broadcast is as follows: road name + location + degree;
for linear congestion, the expression of voice broadcast is as follows: road name + "position-position" + subtype + degree;
for regional congestion, the expression of voice broadcast is as follows: position + range + degree;
see table 1;
TABLE 1
Table 2 shows the degree of traffic congestion;
TABLE 2
Step S2, determining an attention area and extracting congestion information of the attention area;
for a driver, the vehicle position can be acquired through an on-board device such as a GPS, and a half-hour travel isochrone area of the current vehicle position is taken as a driver attention area;
for broadcasting stations and traffic police departments, attention areas can be selected by self;
the congestion information of the concerned area can be obtained from the electronic map through the Internet + technology;
step S3, clustering and analyzing and merging adjacent road sections with the same or similar congestion properties;
in the step, a K-means clustering algorithm is adopted to merge adjacent objects with high similarity into a whole, namely, road sections with similar congestion properties are merged; the congestion property refers to a congestion level in the congestion degree; acquiring congestion data of all road section measuring points in an area as k clusters of the clusters, wherein each cluster is described by a centroid; in the Euclidean distance
As the distance between classes, xik、xjkCalculating the distance between the classes by a shortest distance method, wherein p is the number of the objects in the congested road section; congestion data cluster
And
a distance d betweenijSmaller distances indicate more similarity between them; the main contents comprise:
(1) k-means clustering algorithm: firstly, initializing an algorithm, namely, creating k centroids required by the algorithm, then distributing the measuring point data to each centroid, iteratively calculating the inter-class distance, wherein the iteration times depend on whether the distribution calculation of the data points changes, returning the distribution result, and obtaining the cluster of the measuring point congestion data and classifying;
(2) creating and optimizing centroids: firstly, taking k initial points to randomly determine as a mass center; then, taking a cluster, distributing each point in the data set, finding out the centroid of each point closest to the point, finding out the cluster corresponding to the centroid, and distributing the cluster into the centroid; after this step is completed, the centroid of each cluster is updated to be the average of all the points of the cluster;
(3) calculating the distance between the centroids: and (3) importing the data into a K-means clustering algorithm, storing the data into a dM matrix, calculating Euclidean distances, establishing a set to store the generated random centroid, and constructing a data set used by the algorithm.
Step S4, judging the congestion form according to the standard determined in step S1;
judging by adopting a Sigmoid classification function and Logistic regression based on a congestion form object formed after merging the approximate road sections; the method specifically comprises the following steps:
(1) determining an optimal regression coefficient by adopting a Sigmoid function gradient rising method; the specific calculation formula is as follows:
let the input of this function (1) be z, then the formula is as follows:
z＝w0x0+w1x1+w2x2+…+wnxn(2)
the above formula is in the form of a linear equation, the vector form of which can be written as z ═ wTx; wherein the vector x is the input data of the classifier, and the vector w is the best classification parameter to be found; the main idea of the algorithm is to find the maximum value in a certain function curve, and the optimal path is the gradient change direction; can pass through
To obtain the gradient value of the function; if the step length of the operator movement is recorded as alpha, the iterative formula of the gradient ascent algorithm is as follows
(2) And judging the decision tree boundary, namely the boundary of form classification (nodal congestion, linear congestion and regional congestion) in the table 1, and performing Logistic regression classification on the traffic congestion form.
Fig. 2, 3, and 4 show the forms of nodal congestion, linear congestion, and regional congestion, respectively, and thick lines in the drawings indicate congested links or roads.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to examples, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.
Claims (6)
1. A method of identifying congestion conditions summarizing a current region of a vehicle, comprising the steps of:
step S1, carrying out congestion form classification and standard determination;
step S2, determining an attention area and extracting congestion information of the attention area;
step S3, clustering and analyzing and merging adjacent road sections with the same or similar congestion properties;
in step S4, the congestion status is determined according to the criteria determined in step S1.
2. The method for identifying congestion conditions for a current area of a generalized vehicle as recited in claim 1,
in step S1, the congestion mode includes: nodal congestion, linear congestion, and regional congestion; the criteria for congestion patterns include:
the node congestion is a state that the congestion length is smaller than a set length threshold;
the linear congestion is a form in which the congestion length is equal to or greater than a set length threshold;
the regional congestion is a form in which the congested road occupies a regional road equal to or more than a set percentage threshold.
3. The method for identifying congestion conditions for a current area of a generalized vehicle as recited in claim 2,
linear congestion is further classified into homogeneous linear congestion, intermittent linear congestion, and fishbone-shaped linear congestion.
4. The method for identifying congestion conditions for a current area of a generalized vehicle as recited in claim 2,
for nodal congestion, the expression of voice broadcast is as follows: road name + location + degree;
for linear congestion, the expression of voice broadcast is as follows: road name + "position-position" + subtype + degree;
for regional congestion, the expression of voice broadcast is as follows: position + range + degree.
5. The method for identifying congestion in a current area of a generalized vehicle as recited in claim 2, 3 or 4,
in the step S3, merging the adjacent objects with high similarity into a whole by adopting a K-means clustering algorithm, namely merging road sections with similar congestion properties; the congestion property refers to a congestion level in the congestion degree; acquiring congestion data of all road section measuring points in an area as k clusters of the clusters, wherein each cluster is described by a centroid; in the Euclidean distance
As the distance between classes, xik、xjkCalculating the distance between the classes by a shortest distance method, wherein p is the number of the objects in the congested road section; congestion data cluster
And
a distance d betweenij(ii) a The method mainly comprises the following steps:
(1) k-means clustering algorithm: firstly, initializing an algorithm, namely, creating k centroids required by the algorithm, then distributing the measured point data to each centroid, iteratively calculating the inter-class distance, returning a distribution result, and obtaining the clustering and classification of the measured point congestion data;
(2) creating and optimizing centroids: firstly, taking k initial points to randomly determine as a mass center; then, taking a cluster, distributing each point in the data set, finding out the centroid of each point closest to the point, finding out the cluster corresponding to the centroid, and distributing the cluster into the centroid; after this step is completed, the centroid of each cluster is updated to be the average of all the points of the cluster;
(3) calculating the distance between the centroids: and (3) importing the data into a K-means clustering algorithm, storing the data into a dM matrix, calculating Euclidean distances, establishing a set to store the generated random centroid, and constructing a data set used by the algorithm.
6. The method for identifying and summarizing the congestion conditions in the current area of the vehicle as claimed in claim 2, 3 or 4, wherein in step S4, the congestion shape object formed after the approximate road sections are merged is judged by using Sigmoid classification function and Logistic regression; the method specifically comprises the following steps:
(1) determining an optimal regression coefficient by adopting a Sigmoid function gradient rising method; the specific calculation formula is as follows:
let the input of this function (1) be z, then the formula is as follows:
z＝w0x0+w1x1+w2x2+…+wnxn(2)
the above formula is in the form of a linear equation, the vector form of which can be written as z ═ wTx; wherein the vector x is the input data of the classifier, and the vector w is the best classification parameter to be found; by passing
To obtain the gradient value of the function; if the step length of the operator movement is denoted as alpha, the gradient ascent algorithm is iteratedThe formula is as follows
(2) And (4) judging the decision tree boundary, namely the boundary of the form classification in the step S1, and performing Logistic regression classification on the traffic jam form.
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