CN116383678B - Method for identifying abnormal speed change behavior frequent road sections of operating passenger car - Google Patents
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Abstract
The invention discloses a method for identifying frequent road sections of abnormal speed change behaviors of a passenger car, and relates to a method for identifying frequent road sections of abnormal speed change behaviors of a passenger car. The invention aims to solve the problems that the existing traffic safety hidden danger road section investigation method belongs to statistical analysis after accident occurrence, hidden danger road sections are not recognized and early-warned before accident occurrence, and the economic cost is high and the precision is low. The method for identifying the frequent road sections of the abnormal speed change behavior of the operating passenger car comprises the following specific processes: step 1, basic data acquisition; step 2, correcting the coordinates; step 3, calculating basic data; step 4, identifying abnormal speed change behaviors; and 5, identifying the frequently abnormal speed change road sections. The invention belongs to the technical field of public transportation operation management.
Description
Technical Field
The invention relates to a method for identifying a common road section of abnormal speed change behavior of a commercial passenger car. The invention belongs to the technical field of public transportation operation management.
Background
As an important public transportation mode, the commercial passenger car has the characteristics of large passenger capacity, high transportation efficiency, comfortable riding environment and the like, and is relatively suitable for middle-short distance travel. Abnormal speed change behaviors such as sudden brake stepping or sudden accelerator stepping of a passenger car driver in the driving process can cause discomfort or injury of passengers, and accidents such as rear-end collision, side turning and the like can also occur, so that more serious personal casualties and property loss are caused. The abnormal speed change behavior on the actual road often has certain space distribution regularity, the road section where the abnormal speed change behavior is frequently generated has higher potential safety hazard, and the identification of the road section where the abnormal speed change behavior is frequently generated of the operating passenger car can timely early warn a driver on one hand, so that traffic accidents are avoided; on the other hand, the common road section information can be provided for a road management department to reform the hidden danger road section so as to improve the driving safety of the road.
The current abnormal behavior judgment of the vehicle mostly adopts a video monitoring or multi-sensor data fusion method, the required data has higher scale and quality requirements, and the economic cost of acquisition equipment and storage equipment is higher. The method has less research on the identification of the road sections with frequent abnormal behaviors of the vehicle, and the related research is to judge the accident risk road sections on the basis of the existing traffic accident data, belongs to statistical analysis after the accident, and does not identify and early warn the hidden trouble road sections before the accident occurs.
The commercial buses have the characteristics of fixed lines, dense shifts and high operation similarity among shifts, and the current commercial buses are all equipped with global positioning systems (Global Positioning System and GPS), so that public buses can obtain data such as positions, speeds, accelerations and the like when a large number of commercial buses run on the fixed lines. The abnormal movement behavior of the vehicle can be reflected in the GPS data, if the identification of the road section frequently suffering from the abnormal speed change behavior can be carried out based on the GPS data, other equipment is not required to be additionally purchased for data acquisition, so that the economic cost can be saved, and the accumulated large amount of data can be conveniently utilized for checking the road hidden danger road section.
Disclosure of Invention
The invention aims to solve the problems that the existing traffic safety hidden danger road section investigation method belongs to statistical analysis after accident occurrence, hidden danger road sections are not recognized and early-warned before accident occurrence, the economic cost is high and the accuracy is low, and provides a method for recognizing frequent road sections of abnormal speed change behaviors of a commercial passenger car so as to improve the accuracy and reliability of a frequent road section recognition technology.
The method for identifying the frequent road sections of the abnormal speed change behavior of the operating passenger car comprises the following specific processes:
Step 1, basic data acquisition;
Step 2, carrying out coordinate deviation correction based on the basic data;
Step3, correcting and calculating the speed and the acceleration based on the basic data and the coordinates;
Step4, identifying abnormal speed change behaviors based on the speed and the acceleration;
And 5, carrying out frequent abnormal speed change road section identification based on the abnormal speed change behavior.
The beneficial effects of the invention are as follows:
According to the method for identifying the frequently-occurring road sections of the abnormal speed change behavior of the commercial passenger car, disclosed by the invention, the coordinate deviation correction is completed by means of map matching by virtue of the vehicle-mounted GPS data which is easy to acquire, so that the negative influence caused by the hardware error of the acquisition equipment is reduced, and the accuracy of the operation basic parameters of the commercial passenger car is improved; determining acceleration threshold values corresponding to the speeds by fitting an acceleration threshold curve so as to judge abnormal speed change behaviors of the operating passenger car; and further, the recognition of frequent road sections of frequent abnormal speed change behaviors is realized based on a density-based noise space clustering method (DBSCAN). The technology can provide road safety pre-warning for operating passenger car drivers, and avoid traffic accidents in road sections frequently caused by abnormal speed change behaviors; meanwhile, the potential safety hazard road section position is provided for the road management department, and assistance is provided for road section reconstruction.
Drawings
FIG. 1 is a flow chart of the present invention.
Detailed Description
The first embodiment is as follows: the method for identifying the frequently-occurring road sections of the abnormal speed change behavior of the operating passenger car comprises the following specific processes:
Step 1, basic data acquisition;
Step 2, carrying out coordinate deviation correction based on the basic data;
Step3, correcting and calculating the speed and the acceleration based on the basic data and the coordinates;
Step4, identifying abnormal speed change behaviors based on the speed and the acceleration;
And 5, carrying out frequent abnormal speed change road section identification based on the abnormal speed change behavior.
The second embodiment is as follows: the first difference between the present embodiment and the specific embodiment is that in the step 1, basic data is collected; the specific process is as follows:
Step 1.1, extracting GPS data of all operating shifts in the past continuous N days of a commercial bus line, wherein the GPS data comprises acquisition time, sampling interval, longitude and latitude, vehicle ID and shift direction information, and the suggested N value is 180;
step 1.2, the space distribution of the frequently-occurring road sections of the abnormal speed change behavior in the up-down direction of the operating passenger car has obvious difference, and the up-down direction operating road of the operating passenger car is selected as a research object, so that the method is also applicable to the down-direction operating road;
Step 1.3, defining an operation shift set of the uplink direction of the line as i= { I: i=1, 2, 3., I }; shift i has K GPS data acquisition points in total, and the K data acquisition point coordinates of shift i are expressed as Wherein lng and lat respectively represent longitude and latitude, and the unit is degree; the GPS sampling interval is expressed as T, and the unit is s; k=1, 2,3,;
and 1.4, extracting a GIS road network of a running line passing region of the operating passenger car, wherein the GIS road network comprises a road name, a road number and road grade information.
Other steps and parameters are the same as in the first embodiment.
And a third specific embodiment: the difference between the embodiment and the specific embodiment is that, in the step 2, coordinate rectification is performed based on the basic data (because the acquisition of the GPS coordinates is biased, it cannot be guaranteed that the coordinate points fall on the road network accurately, for the sake of more accuracy, the coordinates after map matching are considered to be more accurate, and here, the conversion of the map matching into planar coordinates is more convenient for subsequent calculation); the specific process is as follows:
2.1, adopting a map matching algorithm based on a hidden Markov model to finish map matching of the GPS data acquisition points and the GIS road network, wherein the matched road network is the road network where the GPS data acquisition points are actually located;
Step 2.2. The coordinates of the GPS data acquisition points after map matching are the actual positions of the GPS data acquisition points on the road network, and are expressed as
Step 2.3. Matching the coordinates of the mapUnified conversion to planar coordinates/>(For example, the coordinates after map matching can be uniformly converted into plane coordinates by adopting a projection mode, the projection mode can be a UTM 3-degree banded projection mode), x and y are respectively the projection distances from the GPS data acquisition point to the central meridian and the equator in a coordinate system taking the intersection point of the equator and the central meridian as the origin of coordinates, and the unit is m.
Other steps and parameters are the same as in the first or second embodiment.
The specific embodiment IV is as follows: the difference between the present embodiment and one to three embodiments is that the velocity and the acceleration are calculated based on the basic data and the coordinate deviation correction in the step 3; the specific process is as follows:
step 3.1, the speed after correction of the k data acquisition point coordinates of shift i is expressed as The unit is m/s; calculation speed/>The formula is shown as follows;
wherein: The x-axis coordinates of plane coordinates after correction of the k+1 data acquisition point coordinates and the k data acquisition point coordinates of shift i are respectively given in m;
The y-axis coordinates of plane coordinates after correction of the k+1 data acquisition point coordinates and the k data acquisition point coordinates of shift i are respectively given in m;
step 3.2, the acceleration after correction of the k data acquisition point coordinates of shift i is expressed as The unit is m/s 2; calculate acceleration/>The formula is shown as follows;
wherein: the speed after correction is obtained for the k-1 data acquisition point coordinates of shift i.
Other steps and parameters are the same as in one to three embodiments.
Fifth embodiment: the first to fourth embodiments of the present invention are different from the first to fourth embodiments in that the abnormal shift behavior is identified based on the speed and the acceleration in the step 4; the specific process is as follows:
Step 4.1, acceleration data which is greater than or equal to 0 in all data acquired by I shifts are put into a set C +,
Acceleration data less than 0 of all the I shifts acquired data is put into the set C -,
The maximum value of shift operation speed is denoted as v max;
Wherein: v n is the speed of the nth data point in set C +, Acceleration v m, which is the nth data point in set C +, is the speed of the mth data point in set C -,/>Acceleration for the mth data point in set C -;
Step 4.2, dividing the speed intervals [0, v max ] into a set Q by taking 1m/s as intervals,
First, theThe interval is denoted as Q σ, th/>The mid-point of each interval is/>
Wherein,Mathematical symbols rounded up;
Among the data acquisition points included in the interval Q σ are The data acquisition Point belongs to set C +,/>The data acquisition points belong to the set C -;
Step 4.3. Section Data acquisition points belonging to the sets C + and C - are arranged from small to large according to acceleration values, and are respectively put into the sets/>And/>
Step 4.4. Bring togetherThe 95 th quantile of the medium-plus-velocity value is expressed as/>Aggregation/>The 95 th quantile of the medium-plus-velocity value is expressed as/>
As a discrimination value of abnormal shift behavior at each speed section (here, the 95 th quantile of the acceleration value is taken as a threshold, namely, shift behavior which exceeds the acceleration value and is considered to be abnormal and biased, and then the acceleration threshold is given by fitting a polynomial to determine whether the abnormal shift behavior is the abnormal shift behavior), only behavior corresponding to 5% of acceleration is regarded as the biased shift behavior;
Step 4.5, the point set of distinguishing the positive acceleration and the negative acceleration is represented as P +、P-, and the following formula is shown;
drawing an acceleration threshold curve by data points in a point set P +、P- respectively, wherein the abscissa is the speed, and the ordinate is the acceleration;
Step 4.6, respectively fitting a polynomial curve to the point set P +、P- by adopting a least square method, taking the speed v as an independent variable, the acceleration a as a dependent variable, and setting polynomial curve functions as a +(v)、a- (v) respectively;
Step 4.7. Polynomial a +(v)、a- (v) is fitted with 2 to 5 orders (the highest order M, H of polynomial a +(v)、a- (v) is fitted with 2.3 and 4.5, respectively), and the square sum of deviation L (alpha) and L (beta) are the minimum targets, and the fitting polynomial coefficient alpha 0,α1,...,αM、β0,β1,...,βH and the highest order M, H are determined;
And 4.8. The fitted polynomial a +(v)、a- (v) is respectively used as a positive acceleration threshold curve, so as to judge whether the operating passenger car has abnormal speed change behaviors.
Other steps and parameters are the same as in one to four embodiments.
Specific embodiment six: the difference between the present embodiment and one to fifth embodiments is that the 95 th quantile determining method in the step 4.4 is as follows;
wherein: Representing collections/> Middle/>Acceleration values of bits; Representing collections/> Middle/>Acceleration values of bits.
Other steps and parameters are the same as in one of the first to fifth embodiments.
Seventh embodiment: in the step 4.6, a polynomial curve is fitted to the point set P +、P- by a least squares method, the velocity v is used as an independent variable, the acceleration a is used as a dependent variable, and the polynomial curve functions are respectively a +(v)、a- (v), which is shown in the following formula;
Wherein: m, H represent the highest order of the polynomial curve function a +(v)、a- (v), respectively; α 0,α1,...,αM represents the fitting polynomial coefficients, β 0,β1,...,βH represents the fitting polynomial coefficients; v M denotes the M-order of velocity v, v H denotes the H-order of velocity v, v j denotes the j-order of velocity v, and v l denotes the l-order of velocity v.
Other steps and parameters are the same as in one of the first to sixth embodiments.
Eighth embodiment: this embodiment differs from one of the first to seventh embodiments in that in step 4.7, L (α) and L (β) are calculated as shown in the following formula;
other steps and parameters are the same as those of one of the first to seventh embodiments.
Detailed description nine: the difference between the embodiment and one to eight embodiments is that the polynomial a +(v)、a- (v) fitted in the step 4.8 is used as a positive acceleration threshold curve and a negative acceleration threshold curve respectively, so as to judge whether the operating bus has abnormal speed change behavior; the specific process is as follows:
Step 4.8.1. K=1;
Step 4.8.2 determining the acceleration of shift i after correction of the k data acquisition point coordinates Corresponding speed/>Acceleration threshold value/>And/>(I.e., each velocity value in the acceleration threshold curve corresponds to an acceleration threshold after a +(v)、a- (v) fitting is completed);
step 4.8.3. Judging If so, go to step 4.8.4; otherwise, go to step 4.8.5;
step 4.8.4 determination If yes, judging that the operation passenger car has one abnormal acceleration behavior; otherwise, judging that the operation bus does not have abnormal acceleration behavior;
Step 4.8.5. Judge If yes, judging that the operation passenger car has one abnormal deceleration action; otherwise, judging that the operation bus does not have abnormal deceleration behavior;
step 4.8.6. K=k+1, judging that K is less than or equal to K, if yes, turning to step 4.8.2; otherwise, go to step 4.8.7;
And 4.8.7, after traversing all accelerations, screening out all abnormal speed change behavior points.
Other steps and parameters are the same as in one to eight of the embodiments.
Detailed description ten: the present embodiment differs from one of the first to ninth embodiments in that the step 5 is to perform frequent abnormal shift section identification based on the abnormal shift behavior; the specific process is as follows:
the method comprises the steps of researching the spatial distribution situation of abnormal speed change behaviors, recording the occurrence position of the abnormal speed change behaviors as one-time abnormal speed change behavior data, and performing spatial clustering on the abnormal speed change behavior data by adopting a DBSCAN clustering method;
step 5.1, defining parameters; the specific process is as follows:
step 5.1.1. The coordinates of the occurrence position of the singular abnormal shift behavior, r_p (x, y), are denoted as p ψ (x, y), the abnormal shift behavior data are assembled into a set D, and the total number is denoted as z, and are denoted as
Step 5.1.2. Calculating abnormal speed change behavior sample points with Euclidean distanceAnd/>Distance betweenThe formula is shown as follows;
wherein: γ, η ε [1, z ];
sample points/>, respectively The x-axis coordinate of the plane coordinate, m;
sample points/>, respectively The y-axis coordinate of the plane coordinate, m;
Step 5.1.3. The neighborhood of the abnormal speed change behavior point p ψ is represented as N pψ, the neighborhood radius is epsilon, and the following formula is shown;
Step 5.1.4. Point p ψ neighborhood The minimum sample point number in the range is expressed as lambda, and the condition that the point p ψ serving as a core sample point needs to be met is shown in the following formula;
wherein: representing the number of sample points in the neighborhood taking p ψ as a core point;
Step 5.1.5. Evaluating the clustering effect of the clustering samples by using the contour coefficients, and abnormal speed change behavior sample points The contour coefficient calculation of (2) is shown in the following formula;
wherein: For sample points/> Average distance to other points within the cluster;
For sample points/> Average distance to points in other clusters;
Step 5.1.6. Use of overall contour coefficient average As a sample overall clustering effect evaluation index, the specific calculation is shown in the following formula;
Step 5.2, a specific clustering step; the specific process is as follows:
Step 5.2.1, initializing a neighborhood radius epsilon, and according to the abnormal speed change behavior characteristic, not suggesting to select a larger epsilon, and taking epsilon=100deg.M;
Step 5.2.2, defining the minimum number of sample points in the cluster according to the road grade, wherein the minimum number of sample points can be taken as lambda 1, the maximum value as lambda 2 (the minimum number of sample points can be defined in different minimum value ranges according to the road grade, such as expressway, primary road and secondary road … …) and lambda is an integer and the value range is [ lambda 1,λ2 ];
step 5.2.3. Initializing, wherein the parameter h=0;
step 5.2.4.λ=λ 1 +h, input ε, λ;
5.2.5. Adopting a DBSCAN clustering method to complete spatial clustering on all abnormal variable speed behavior points p ψ (x, y) in the set D;
step 5.2.6. Calculating the profile coefficients of the points of the sample of the abnormal speed-changing behaviors Average value of overall profile coefficient
Step 5.2.7, judging lambda 1+h≤λ2, if yes, h=h+1, and returning to step 5.2.4; otherwise, enter step 5.2.8;
Step 5.2.8. Calculating the overall profile factor maximum The formula is shown as follows;
Each lambda is used as input to correspondingly calculate an average value of the overall profile coefficients, and the step 5.2.8 is to take the maximum value of the overall profile coefficients;
step 5.2.9 determining The corresponding parameter λ is λ 0, i.e. the final key parameter λ=λ 0;
step 5.3, determining a clustering parameter epsilon=100deg.m of DBSCAN, wherein lambda=lambda 0 is the optimal cluster;
Step 5.4, outputting the number of clusters and the number of sample points in each cluster after optimal clustering of all abnormal speed change behavior points p ψ (x, y) in the set D, wherein the number of clusters is the number of frequent abnormal speed change road sections, and the number of sample points in each cluster is the number of abnormal speed change behaviors of the road sections;
And 5.5. Sample points are distributed on the road network, so that the clustered cluster shape (the clustered cluster shape is a road section because all the sample points are on the road network) is attached to the road network, the obtained cluster is a frequently-occurring abnormal speed change road section, and the edge points of the cluster along the road network direction are the starting and ending points of the frequently-occurring abnormal speed change road section.
Other steps and parameters are the same as in one of the first to ninth embodiments.
The present invention is capable of other and further embodiments and its several details are capable of modification and variation in light of the present invention, as will be apparent to those skilled in the art, without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (9)
1. A method for identifying abnormal speed change behavior frequent road sections of a commercial passenger car is characterized by comprising the following steps of: the method comprises the following specific processes:
Step 1, basic data acquisition;
Step 2, carrying out coordinate deviation correction based on the basic data;
Step3, correcting and calculating the speed and the acceleration based on the basic data and the coordinates;
Step4, identifying abnormal speed change behaviors based on the speed and the acceleration;
Step 5, carrying out frequent abnormal speed change road section identification based on abnormal speed change behaviors; the specific process is as follows:
Step 5.1, defining parameters;
The specific process is as follows:
step 5.1.1. The coordinates of the occurrence position of the singular abnormal shift behavior, r_p (x, y), are denoted as p ψ (x, y), the abnormal shift behavior data are assembled into a set D, and the total number is denoted as z, and are denoted as
Step 5.1.2. Calculating abnormal speed change behavior sample points with Euclidean distanceAnd/>Distance between/>Represented by the following formula (7);
wherein: γ, η ε [1, z ];
sample points/>, respectively The x-axis coordinate of the plane coordinate, m;
sample points/>, respectively The y-axis coordinate of the plane coordinate, m;
step 5.1.3. the neighborhood of the abnormal shift behavior point p ψ is expressed as The neighborhood radius is epsilon, and the formula (8) is shown below;
Step 5.1.4. Point p ψ neighborhood The minimum sample point number in the range is expressed as lambda, and the condition that the point p ψ as the core sample point needs to be met is shown in the following formula (9);
wherein: representing the number of sample points in the neighborhood taking p ψ as a core point;
Step 5.1.5. Evaluating the clustering effect of the clustering samples by using the contour coefficients, and abnormal speed change behavior sample points The contour coefficient calculation of (2) is shown in the following formula (10);
wherein: For sample points/> Average distance to other points within the cluster;
For sample points/> Average distance to points in other clusters;
Step 5.1.6. Use of overall contour coefficient average As a sample overall clustering effect evaluation index, specifically calculating a sample overall clustering effect as shown in the following formula (11);
step 5.2, a specific clustering step;
The specific process is as follows:
Step 5.2.1, initializing a neighborhood radius epsilon, and taking epsilon=100deg.M;
5.2.2, defining the minimum number of sample points in the cluster according to the road grade, wherein the minimum number of sample points in the cluster is lambda 1, the maximum number is lambda 2, lambda is an integer, and the value range is [ lambda 1,λ2 ];
step 5.2.3. Initializing, wherein the parameter h=0;
step 5.2.4.λ=λ 1 +h, input ε, λ;
Step 5.2.5, spatial clustering is completed on all abnormal variable speed behavior points p ψ (x, y) in the set D by adopting a DBSCAN clustering method;
step 5.2.6. Calculating the profile coefficients of the points of the sample of the abnormal speed-changing behaviors Average value of overall contour coefficient/>
Step 5.2.7, judging lambda 1+h≤λ2, if yes, h=h+1, and returning to step 5.2.4; otherwise, enter step 5.2.8;
Step 5.2.8. Calculating the overall profile factor maximum Represented by the following formula (12);
step 5.2.9 determining The corresponding parameter λ is λ 0, i.e. the final key parameter λ=λ 0;
step 5.3, determining a clustering parameter epsilon=100deg.m of DBSCAN, wherein lambda=lambda 0 is the optimal cluster;
Step 5.4, outputting the number of clusters and the number of sample points in each cluster after optimal clustering of all abnormal speed change behavior points p ψ (x, y) in the set D, wherein the number of clusters is the number of frequent abnormal speed change road sections, and the number of sample points in each cluster is the number of abnormal speed change behaviors of the road sections;
And 5.5. Sample points are distributed on the road network, so that the clustered cluster shape is attached to the road network, the obtained cluster is a frequently-occurring abnormal speed change road section, and the edge point of the cluster along the road network direction is the starting and ending point of the frequently-occurring abnormal speed change road section.
2. The method for identifying abnormal speed change behavior frequent road segments of a commercial passenger car according to claim 1, wherein the method comprises the following steps: the basic data is acquired in the step 1; the specific process is as follows:
Step 1.1, extracting GPS data of all operating shifts in the past continuous N days of a commercial bus line, wherein the GPS data comprises acquisition time, sampling interval, longitude and latitude, vehicle ID and shift direction information;
Step 1.2, the space distribution of the frequently-occurring road sections of the abnormal speed change behavior in the up-down direction of the operating passenger car has obvious difference, and the up-down direction operating road of the operating passenger car is selected as a research object and is also suitable for the down-down direction operating road;
Step 1.3, defining an operation shift set of the uplink direction of the line as i= { I: i=1, 2, 3., I }; shift i has K GPS data acquisition points in total, and the K data acquisition point coordinates of shift i are expressed as
Wherein lng and lat respectively represent longitude and latitude, and the unit is degree; the GPS sampling interval is expressed as T, and the unit is s; k=1, 2,3,;
and 1.4, extracting a GIS road network of a running line passing region of the operating passenger car, wherein the GIS road network comprises a road name, a road number and road grade information.
3. The method for identifying abnormal speed change behavior frequent road segments of a commercial passenger car according to claim 2, wherein the method comprises the following steps: in the step 2, coordinate deviation correction is performed based on basic data; the specific process is as follows:
2.1, adopting a map matching algorithm based on a hidden Markov model to finish map matching of the GPS data acquisition points and the GIS road network, wherein the matched road network is the road network where the GPS data acquisition points are actually located;
Step 2.2. The coordinates of the GPS data acquisition points after map matching are the actual positions of the GPS data acquisition points on the road network, and are expressed as
Step 2.3. Matching the coordinates of the mapUnified conversion to planar coordinates/>And x and y are projection distances from the GPS data acquisition point to the central meridian and the equator in a coordinate system taking the intersection point of the equator and the central meridian as the origin of coordinates, wherein the unit is m.
4. The method for identifying abnormal speed change behavior frequent road segments of a commercial passenger car according to claim 3, wherein the method comprises the following steps: in the step 3, the speed and the acceleration are calculated based on basic data and coordinate deviation correction; the specific process is as follows:
step 3.1, the speed after correction of the k data acquisition point coordinates of shift i is expressed as The unit is m/s; calculation speed/>The formula is shown as follows;
wherein: The x-axis coordinates of plane coordinates after correction of the k+1 data acquisition point coordinates and the k data acquisition point coordinates of shift i are respectively given in m;
The y-axis coordinates of plane coordinates after correction of the k+1 data acquisition point coordinates and the k data acquisition point coordinates of shift i are respectively given in m;
step 3.2, the acceleration after correction of the k data acquisition point coordinates of shift i is expressed as The unit is m/s 2; calculate acceleration/>The formula is shown as follows;
wherein: the speed after correction is obtained for the k-1 data acquisition point coordinates of shift i.
5. The method for identifying abnormal speed change behavior frequent road segments of a commercial passenger car according to claim 4, wherein the method comprises the following steps: in the step 4, abnormal speed change behavior identification is carried out based on the speed and the acceleration; the specific process is as follows:
Step 4.1, acceleration data which is greater than or equal to 0 in all data acquired by I shifts are put into a set C +,
Acceleration data less than 0 of all the I shifts acquired data is put into the set C -,
The maximum value of shift operation speed is denoted as v max;
Wherein: v n is the speed of the nth data point in set C +, Acceleration v m, which is the nth data point in set C +, is the speed of the mth data point in set C -,/>Acceleration for the mth data point in set C -;
Step 4.2, dividing the speed intervals [0, v max ] into a set Q by taking 1m/s as intervals,
First, theThe interval is denoted as Q σ, th/>The mid-point of each interval is/>
Wherein,Mathematical symbols rounded up;
Among the data acquisition points included in the interval Q σ are The data acquisition Point belongs to set C +,/>The data acquisition points belong to the set C -;
Step 4.3. Section Data acquisition points belonging to the sets C + and C - are arranged from small to large according to acceleration values, and are respectively put into the sets/>And/>
Step 4.4. Bring togetherThe 95 th quantile of the medium-plus-velocity value is expressed as/>Aggregation/>The 95 th quantile of the medium-plus-velocity value is expressed as/>
As a distinguishing value of abnormal speed change behaviors in each speed interval, only behaviors corresponding to 5% of acceleration are regarded as deflection excitation speed change behaviors;
Step 4.5, the point set of distinguishing the positive acceleration and the negative acceleration is represented as P +、P-, and the following formula is shown;
drawing an acceleration threshold curve by data points in a point set P +、P- respectively, wherein the abscissa is the speed, and the ordinate is the acceleration;
Step 4.6, respectively fitting a polynomial curve to the point set P +、P- by adopting a least square method, taking the speed v as an independent variable, the acceleration a as a dependent variable, and setting polynomial curve functions as a +(v)、a- (v) respectively;
Step 4.7. Polynomial a +(v)、a- (v) is fitted with 2 to 5 steps respectively, and the square sum of deviation L (alpha) and L (beta) are taken as the minimum target, so as to determine the fitting polynomial coefficient alpha 0,α1,...,αM、β0,β1,...,βH and the highest order M, H;
And 4.8. The fitted polynomial a +(v)、a- (v) is respectively used as a positive acceleration threshold curve, so as to judge whether the operating passenger car has abnormal speed change behaviors.
6. The method for identifying abnormal speed change behavior frequent road segments of a commercial passenger car according to claim 5, wherein the method comprises the following steps: the 95 th quantile determining method in the step 4.4 is shown in the following formula;
wherein: Representing collections/> Middle/>Acceleration values of bits; Representing collections/> Middle/>Acceleration values of bits.
7. The method for identifying abnormal speed change behavior frequent road segments of a commercial passenger car according to claim 6, wherein the method comprises the following steps: in the step 4.6, a polynomial curve is fitted to the point set P +、P- by a least square method, the velocity v is taken as an independent variable, the acceleration a is taken as an independent variable, and polynomial curve functions are respectively set as a +(v)、a- (v), and the following formula is shown;
wherein: m, H represent the highest order of the polynomial curve function a +(v)、a- (v), respectively; α 0,α1,...,αM represents the fitting polynomial coefficients, β 0,β1,…,βH represents the fitting polynomial coefficients; v M denotes the M-order of velocity v, v H denotes the H-order of velocity v, v j denotes the j-order of velocity v, and v l denotes the l-order of velocity v.
8. The method for identifying abnormal speed change behavior frequent road segments of a commercial passenger car according to claim 7, wherein the method comprises the following steps: in the step 4.7, L (alpha) and L (beta) are calculated as shown in the following formula;
9. The method for identifying abnormal speed change behavior frequent road segments of a commercial passenger car according to claim 8, wherein the method comprises the following steps: the fitted polynomial a +(v)、a- (v) in the step 4.8 is respectively used as a positive acceleration threshold curve and a negative acceleration threshold curve, so that whether the operating passenger car has abnormal speed change behaviors is judged; the specific process is as follows:
Step 4.8.1. K=1;
Step 4.8.2 determining the acceleration of shift i after correction of the k data acquisition point coordinates Corresponding speed/>Acceleration threshold value/>And/>
Step 4.8.3. JudgingIf so, go to step 4.8.4; otherwise, go to step 4.8.5;
step 4.8.4 determination If yes, judging that the operation passenger car has one abnormal acceleration behavior; otherwise, judging that the operation bus does not have abnormal acceleration behavior;
Step 4.8.5. Judge If yes, judging that the operation passenger car has one abnormal deceleration action; otherwise, judging that the operation bus does not have abnormal deceleration behavior;
step 4.8.6. K=k+1, judging that K is less than or equal to K, if yes, turning to step 4.8.2; otherwise, go to step 4.8.7;
And 4.8.7, after traversing all accelerations, screening out all abnormal speed change behavior points.
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