CN115086910A - Real-time road segment division method based on dynamic and static characteristics under V2X environment - Google Patents
Real-time road segment division method based on dynamic and static characteristics under V2X environment Download PDFInfo
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Abstract
The invention relates to a real-time road section dividing method based on dynamic and static characteristics under a V2X environment, which comprises the following steps: step 1: setting the points of the change of the road design elements as road division points based on the road geometric data, and performing static division on the road sections; step 2: dividing a small amount of vehicle track data into 4 different traffic states based on the traffic index; and 3, step 3: counting traffic characteristic parameters of minimum road section units in different traffic states to form a characteristic vector of each road section unit, and then performing real-time dynamic division on road sections based on an ordered clustering algorithm; and 4, step 4: and combining the static and dynamic road segment division results to form road segment division results in different traffic states. Compared with the prior art, the method has the advantages that the problem that road segment heterogeneity is neglected in a fixed division method is avoided, and further the effectiveness and accuracy of road segment management and control are improved.
Description
Technical Field
The invention relates to the field of traffic big data processing and mining, in particular to a real-time road section dividing method based on dynamic and static characteristics under a V2X environment.
Background
The road section unit division is a basic step for road management and control, the road section units are considered to be a homogeneous whole and are basic statistical objects for analysis and provision, the effective degree of division affects the benefit of a traffic flow characteristic statistical value, the characteristics of traffic flow are difficult to count when the road section units are too small, the heterogeneity of the traffic characteristics of the road sections with overlong road sections is ignored when the road section units are too small, the analysis of the traffic characteristics is not facilitated, and therefore effective and reasonable basis needs to be selected for road section division.
In recent years, the V2X (Vehicle-to-influencing) technology is widely applied to floors, and the V2X technology can provide space-time continuous individual high-precision operation data, including safety risk data of CAVs (Connected-Automated vehicles) and Vehicle tracks, other non-CAV operation information and cv (Connected vehicles) track data, and provides a new data source for road segment division. Under the background, making full use of the unique personality data, more finely and precisely dividing road segments, serving the formulation of continuous flow active traffic safety strategies, is a real problem to be solved urgently.
At present, the common dividing methods mainly include a fixed-length method and a homogeneous method. The fixed length method is to divide the road into equal length sections without considering any factors of the road. The method is simple to operate, but the road section mutation information is easy to ignore, so that analysis errors are caused. The homogeneous method is to divide the road section units into continuous road sections with the same attribute according to different indexes and standards, and the prior art generally selects the road design index as the division standard. In practice, the area between an upstream detector and a downstream detector is usually chosen as a technical section, subject to the position of the fixed point detector, and the spatial span of this section is usually equal to or greater than 400 meters. It can be found that in the prior art, only one fixed spatial range is adopted, the difference of effective spatial ranges caused by the conflict risks in different scenes is ignored, and the influence of the change of parameters in spatial dimensions on the conflict risks is not fully considered. In recent years, a road section dividing method for establishing multi-index synthesis by using an ordered clustering algorithm appears. However, in the prior art, only static characteristics of road sections are basically considered for fixed division, and the influence of the change of dynamic parameters of traffic flow in the spatial dimension on the heterogeneity of the road sections is not fully considered.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide a real-time road segment division method based on dynamic and static characteristics in a V2X environment.
The purpose of the invention can be realized by the following technical scheme:
a real-time road segment dividing method based on dynamic and static characteristics under a V2X environment comprises the following steps:
step 1: setting the points of the change of the road design elements as road division points based on the road geometric data, and performing static division on the road sections;
step 2: dividing the vehicle trajectory data into 4 different traffic states based on the traffic index;
and step 3: counting traffic characteristic parameters of the minimum road section unit under different traffic states to form a characteristic vector of each road section unit, and then carrying out real-time dynamic division on road sections based on an ordered clustering algorithm;
and 4, step 4: and combining the static and dynamic road segment division results to form road segment division results in different traffic states.
In the step 1, the road design factor changes include changes of an entrance ramp, an exit ramp, a curve and a number of lanes, the position of the ramp is set as a breakpoint for the entrance ramp, the whole curve and a smooth curve are set as the same road section for the curve, and the position of the number of lanes is set as a division point for the number of lanes.
In the step 2, the process of dividing the vehicle trajectory data into 4 different traffic states based on the traffic index specifically includes:
step 201: calculating a road traffic index;
step 202: the road geometric data is divided into four traffic states, namely congestion, congestion dissipation, congestion formation and unblocked traffic, based on the road traffic index.
In step 201, the formula of the road traffic index is:
wherein v is f For the speed of the free flow of the road section, v i Is the road section travel speed.
In step 202, the process of dividing the road geometric data into four traffic states based on the road traffic index specifically includes:
selecting 80km/h as the speed v of the free flow of the road section f And calculating the average speed of the road section every 5 minutes, and further analyzing the traffic state of the road geometric data:
when the TCI value is less than 25, the traffic state is congestion;
congestion is formed when the TCI value is between 25 and 50 and before the congestion state;
dissipating congestion when the TCI value is between 25 and 50 and after congestion;
when the TCI value is greater than 50 or negative, the traffic state is unobstructed.
In the step 3, the process of performing real-time dynamic division of the road sections based on the ordered clustering algorithm specifically comprises the following steps:
step 301: taking 10m as a particle size as a minimum statistical unit, counting each characteristic parameter of the minimum unit under different traffic states to form a characteristic vector x of each road section unit i The characteristic parameters comprise flow, flow standard deviation, average speed, speed variation coefficient, speed standard deviation, density, upstream and downstream section speed difference, lane change rate and brake rate;
step 302: after the characteristic vector is subjected to data normalization, the data of the whole road section form an ordered sequence (x) 1 ，x 2 ，...，x n )；
Step 303: inputting the ordered sequence of numbers into an ordered clustering algorithm to obtain the number of segments tau 0 ；
Step 304: determining the final number of segments τ 0 Then, the sequence (x) of the road segments obtained in step 303 is repeated 1 ，x 2 ，...，x n ) And the number of segments τ 0 And inputting the data into an ordered clustering algorithm for calculation to obtain a dynamic division result of the road section.
In the step 303, the ordered sequence is input into the ordered clustering algorithm to obtain the number τ of segments 0 The process specifically comprises the following steps:
for a whole road obtained in step 302Ordered sequence of segment data (x) 1 ，x 2 ，...，x n ) Setting possible mutation points as tau (tau is more than or equal to 2 and less than or equal to n-1), and calculating the sum of squared deviations before and after the mutation points; the sum of squared deviations before and after the mutation point is:
wherein the content of the first and second substances,
and
is the mean value, x, of the two parts before and after the mutation point i Is the ith number in the ordered sequence, V τ As the sum of squared deviations before the mutation point, V n-τ Is the sum of squared deviations after the mutation point, and n is the length of the array;
obtaining the total deviation square sum S of the mutation point at the moment n ；
When the sum of the squares of the total deviations is minimal, i.e. the sum of the squares of the total deviations
Then, the catastrophe point T is the optimum division point, can be inferred as the division point, the sum of the squares of the total deviations is the main basis for dividing the number of the road sections, and the proper road section division number T is selected 0 Calculating the total dispersion square sum of 3-20 road sections according to the road sections of each traffic state, wherein the total dispersion square sum is continuously reduced along with the increase of the number of the divided sections, which shows that the more the divided sections are, the smaller the difference in the road sections is, and taking the numerical value of which the difference value tends to be smooth as the final road section division number tau 0 。
The sum of the squared deviations before and after the mutation point is respectively as follows:
wherein the content of the first and second substances,
and
respectively the mean value, x, of the front and back parts of the mutation point i Is the ith number in the ordered sequence, V τ As the sum of squared deviations before the mutation point, V n-τ The sum of squares of deviations after the mutation point.
The sum of squared deviations S n The calculation formula of (2) is as follows:
S n (τ)＝V τ +V n-τ
wherein, V τ As the sum of squared deviations before the mutation point, V n-τ The sum of squares of deviations after the mutation point.
In the step 4, the process of combining the results of dynamic and static partitioning specifically comprises the following steps:
and merging two adjacent division points with the distance of less than 10 meters into one division point, and respectively converting the two adjacent division points with the distance of not less than 10 meters into two division points to form road section division results in different traffic states.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention provides a dynamic and static road section dividing method framework, which avoids the problem of neglecting road section heterogeneity in a fixed dividing method and improves the effectiveness of road section control;
2. the method adopts a fine-grained basic unit, can ensure the homogeneity of the divided objects, provides a basis for management and control, and is more accurate in the management and control of the road sections;
3. the invention provides a new idea of road section division by taking the static characteristics and the dynamic characteristics of the road as the basis of road section division, and has better innovation.
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FIG. 1 is a flow chart of the present invention.
Fig. 2 is a schematic diagram of a road segment and a static partitioning result according to the present invention, where fig. 2a is a schematic diagram of a partitioned road segment, fig. 2b is a schematic diagram of a result of a static partitioning of an uplink road segment, and fig. 2c is a schematic diagram of a result of a static partitioning of a downlink road segment.
Fig. 3 is a schematic diagram of a dynamic partitioning case of the present invention, in which fig. 3a is a schematic diagram of a sum of squares of the number of partitions and a total amount of samples, and fig. 3b is a schematic diagram of a dynamic partitioning result in a downstream direction in a clear state.
Fig. 4 is a schematic diagram of a result of dynamic and static partitioning according to the present invention, where fig. 4a is a schematic diagram of an uplink clear state result, fig. 4b is a schematic diagram of an uplink congestion formation or dissipation state result, fig. 4c is a schematic diagram of an uplink congestion state result, fig. 4d is a schematic diagram of a downlink clear state result, fig. 4e is a schematic diagram of a downlink congestion formation or dissipation state result, and fig. 4f is a schematic diagram of a downlink congestion state result.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
The invention provides a real-time road section dividing method based on dynamic and static characteristics under a V2X environment, which primarily divides road sections based on road geometric data, extracts traffic parameters based on a small amount of track data, adopts an ordered clustering algorithm to divide the real-time dynamic road sections under different traffic states, and finally combines two dividing results to form a final road section dividing scheme, and the method comprises the following steps:
step 1: setting points of the change of the road design elements as road division points based on road geometric data, wherein the road geometric data comprises an entrance ramp, an exit ramp, the number of lanes and the curvature of a curve, and statically dividing road sections;
step 2: dividing a small amount of vehicle trajectory data into 4 different traffic states including congestion, congestion formation, congestion dissipation and clear based on the traffic index;
and step 3: counting traffic characteristic parameters of minimum road section units in different states to form a characteristic vector of each road section unit, inputting the characteristic vectors which are arranged in sequence into an ordered clustering algorithm, and performing real-time dynamic division on road sections;
and 4, step 4: and combining the results of the static division and the dynamic division to form road section division results in different traffic states.
The characteristics of the main steps and the process parameter range are further limited:
in step 1, a road design element change point is set as a road break point (road division point) to perform static division of a road segment, the road design element includes an entrance/exit ramp, a curve, and a change in the number of lanes, a position where a ramp is located is set as a break point for an entrance/exit ramp, the entire curve and a smooth curve are set as the same road segment for a curve, and a position where the number of lanes is changed is set as a division point for a change in the number of lanes.
In step 2, a road traffic index (TCI) is calculated based on local standards of Shanghai cities, the index divides road geometric data into four traffic states of congestion, congestion dissipation, congestion formation and unblocked traffic, the road traffic index is calculated based on speed, and the formula of the road traffic index is as follows:
wherein v is f For the speed of the free flow of the road section, v i Is the road section travel speed.
Selecting 80km/h as the speed v of the free flow of the road section f Calculating the average speed of the road section every 5 minutes, analyzing the traffic state of the data, and when the TCI value is less than 25, judging that the traffic state is congestion; congestion is formed when the TCI value is between 25 and 50 and before the congestion state; when the TCI value is between 25 and 50Dissipating the congestion after the congestion state; when the TCI value is greater than 50 or negative, the traffic state is unobstructed.
In step 3, taking 10m as a minimum statistical unit, counting each characteristic parameter of the minimum unit in different states, and forming a characteristic vector x of each road section unit i The characteristic parameters comprise flow, flow standard deviation, average speed, speed variation coefficient, speed standard deviation, density, upstream and downstream section speed difference, lane change rate and brake rate, and after data normalization, an ordered sequence (x) is formed by a whole road section 1 ，x 2 ，...，x n ) The sequence of the order number (x) 1 ，x 2 ，...，x n ) Obtaining the number of segments tau from the input ordered clustering algorithm 0 The characteristic parameters and meanings of traffic are shown in table 1:
TABLE 1 traffic characteristic parameter Table
Inputting the ordered sequence of numbers into an ordered clustering algorithm to obtain the final number of segments tau 0 The sequential clustering algorithm is also called optimal segmentation, and is mainly used for optimally segmenting sequences in an arrangement sequence without changing the sequence, which is essentially to segment the sequences to obtain a plurality of subsequences after segmentation, wherein each subsequence is a first-class subsequence, the sequential clustering algorithm enables the similarity between the same classes to be maximum and the similarity between the classes to be minimum, and the sequential data sequence (x) formed by data of the whole road section is subjected to sequential number sequence 1 ，x 2 ，...，x n ) If possible mutation points are tau (tau is more than or equal to 2 and less than or equal to n-1), the sum of squared deviations before and after the mutation points are respectively:
wherein the content of the first and second substances,
and
is the mean value, x, of the two parts before and after the mutation point i Is the ith number in the ordered sequence, V τ As the sum of squared deviations before the mutation point, V n-τ Is the sum of squared deviations after the mutation point, and n is the length of the array;
at this time the sum of squares of total deviations of the mutation points S n Comprises the following steps:
S n (τ)＝V τ +V n-τ ；
obtaining the total deviation square sum S of the mutation point at the moment n ；
When sum of squared total deviations S n Minimum, i.e. sum of squares of total deviations
Then, the catastrophe point T is the optimum division point, can be inferred as the division point, the sum of the squares of the total deviations is the main basis for dividing the number of the road sections, and the proper road section division number T is selected 0 Calculating the total dispersion square sum of 3-20 road sections according to the road sections of each traffic state, wherein the total dispersion square sum is continuously reduced along with the increase of the number of the divided sections, which shows that the more the divided sections are, the smaller the difference in the road sections is, and taking the numerical value of which the difference value tends to be smooth as the final road section division number tau 0 Determining the number of segments τ 0 Thereafter, the road segments are ranked again in order of number (x) 1 ，x 2 ，...，x n ) And inputting the number of the segments into the ordered clustering algorithm for calculation to obtain the dynamic segmentation result of the road section.
In step 4, combining the results of dynamic and static division, combining two adjacent division points with a distance less than 10 meters into one division point, and respectively converting two adjacent division points with a distance not less than 10 meters into two division points to form road segment division results in different traffic states.
As shown in fig. 1, the whole method flowchart includes four steps, namely road static division, traffic state division, road static division and division result combination, to form a final division result.
As shown in fig. 2, in which (2a) is a schematic diagram of dividing a link, (2b) is a result of statically dividing an uplink link, and (2c) is a result of statically dividing a downlink link, different gray scales represent different links, and it can be seen that the links are divided into different links by setting them as division points at all of a curve, an entrance/exit, and a lane change of a road.
As shown in fig. 3, the dynamic partitioning process in the clear state downlink direction is shown, where fig. 3a shows the sum of the number of partitions and the square of the total number of samples, which is used to select the number of partitions, where 9 is selected as the number of partitions, and fig. 3b shows the dynamic partitioning result in the clear state downlink direction.
As shown in fig. 4, results of three states of uplink and downlink of a road segment are shown, the results of static division and dynamic division are integrated to obtain a final division result, fig. 4a is an uplink unblocked state result, fig. 4b is an uplink congestion formation or dissipation state result, fig. 4c is an uplink congestion state result, fig. 4d is a downlink unblocked state result, fig. 4e is a downlink congestion formation or dissipation state result, and fig. 4f is a downlink congestion state result.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.
Claims (10)
1. A real-time road segment dividing method based on dynamic and static characteristics under a V2X environment is characterized by comprising the following steps:
step 1: setting the points of the change of the road design elements as road division points based on the road geometric data, and performing static division on the road sections;
step 2: dividing the vehicle trajectory data into 4 different traffic states based on the traffic index;
and step 3: counting traffic characteristic parameters of minimum road section units in different traffic states to form a characteristic vector of each road section unit, and then performing real-time dynamic division on road sections based on an ordered clustering algorithm;
and 4, step 4: and combining the static and dynamic road segment division results to form road segment division results in different traffic states.
2. The method for real-time road segment division based on dynamic and static characteristics under the V2X environment as claimed in claim 1, wherein the road design factor changes in step 1 include changes of the number of incoming and outgoing ramps, curves and lanes, the position of a ramp is set as a break point for an incoming and outgoing ramp, the whole curve and a smooth curve are set as the same road segment for a curve, and the position of a lane change is set as a division point for a lane change.
3. The method for dividing the road segments in real time based on dynamic and static characteristics under the environment of V2X as claimed in claim 1, wherein the step 2 of dividing the vehicle trajectory data into 4 different traffic states based on the traffic index specifically comprises:
step 201: calculating a road traffic index;
step 202: the road geometric data are divided into four traffic states, namely congestion, congestion dissipation, congestion formation and unblocked traffic, based on the road traffic index.
4. The method for real-time road segment division based on dynamic and static characteristics under the environment of V2X as claimed in claim 3, wherein in the step 201, the formula of the road traffic index is:
wherein v is f For the speed of the free flow of the road section, v i Is the road section travel speed.
5. The method for real-time road segment division based on dynamic and static characteristics under the environment of V2X as claimed in claim 3, wherein the step 202 of dividing the road geometry data into four traffic states based on the road traffic index specifically comprises:
selecting 80km/h as the speed v of the free flow of the road section f And calculating the average speed of the road section every 5 minutes, and further analyzing the traffic state of the road geometric data:
when the TCI value is less than 25, the traffic state is congestion;
congestion is formed when the TCI value is between 25 and 50 and before the congestion state;
dissipating congestion when the TCI value is between 25 and 50 and after congestion;
when the TCI value is greater than 50 or negative, the traffic state is unobstructed.
6. The method for real-time road segment division based on dynamic and static characteristics under the V2X environment according to claim 1, wherein in the step 3, the process of real-time dynamic division of road segments based on the ordered clustering algorithm specifically comprises:
step 301: taking 10m as a particle size as a minimum statistical unit, counting each characteristic parameter of the minimum unit under different traffic states to form a characteristic vector x of each road section unit i The characteristic parameters comprise flow, flow standard deviation, average speed, speed variation coefficient, speed standard deviation, density, upstream and downstream section speed difference, lane changing rate and braking rate;
step 302: after the characteristic vector is subjected to data normalization, the data of the whole road section form an ordered sequence (x) 1 ，x 2 ，...，x n )；
Step 303: inputting the ordered sequence of numbers into an ordered clustering algorithm to obtain the number of segments tau 0 ；
Step 304: determining the final number of segments τ 0 Then, the sequence (x) of the road segments obtained in step 303 is repeated 1 ，x 2 ，...，x n ) And the number of segments τ 0 And inputting the data into an ordered clustering algorithm for calculation to obtain a dynamic division result of the road section.
7. The method for real-time road segment division based on dynamic and static characteristics under the V2X environment according to claim 6, wherein in the step 303, the ordered sequence of numbers is input into an ordered clustering algorithm to obtain the number τ of segments 0 The process specifically comprises the following steps:
an ordered sequence (x) of data for the entire road segment acquired in step 302 1 ，x 2 ，...，x n ) Setting possible mutation points as tau (tau is more than or equal to 2 and less than or equal to n-1), and calculating the sum of squared deviations before and after the mutation points; the sum of squared deviations before and after the mutation point is:
wherein the content of the first and second substances,
and
is the mean value, x, of the two parts before and after the mutation point i Is the ith number in the ordered sequence, V τ As the sum of squared deviations before the mutation point, V n-τ Is the sum of squared deviations after the mutation point, and n is the length of the array;
obtaining the mutation point at the momentSum of squared deviations S of n ；
When the sum of the squares of the total deviations is minimal, i.e. the sum of the squares of the total deviations
Then, the catastrophe point T is the optimum division point, can be inferred as the division point, the sum of the squares of the total deviations is the main basis for dividing the number of the road sections, and the proper road section division number T is selected 0 Calculating the total dispersion square sum of 3-20 road sections according to the road sections of each traffic state, wherein the total dispersion square sum is continuously reduced along with the increase of the number of the divided sections, which shows that the more the divided sections are, the smaller the difference in the road sections is, and taking the numerical value of which the difference value tends to be smooth as the final road section division number tau 0 。
8. The method of claim 7, wherein the sum of squared deviations before and after the discontinuity is respectively:
wherein the content of the first and second substances,
and
is the mean value, x, of the two parts before and after the mutation point i Is the ith number in the ordered sequence, V τ As the sum of squared deviations before the mutation point, V n-τ The sum of squares of deviations after the mutation point.
9. The method of claim 8The real-time road segment division method based on dynamic and static characteristics under the environment of V2X is characterized in that the sum of squares of total dispersion S n The calculation formula of (2) is as follows:
S n (τ)＝V τ +V n-τ
wherein, V τ As the sum of squared deviations before the mutation point, V n-τ The sum of squares of deviations after the mutation point.
10. The method for real-time road segment division based on dynamic and static characteristics under the environment of V2X as claimed in claim 1, wherein the process of combining the results of dynamic and static division in step 4 specifically comprises:
and merging two adjacent division points with the distance of less than 10 meters into one division point, and respectively converting the two adjacent division points with the distance of not less than 10 meters into two division points to form road section division results in different traffic states.
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