CN111275961A - Urban traffic running state feature calculation method based on floating car data - Google Patents
Urban traffic running state feature calculation method based on floating car data Download PDFInfo
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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
- G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
- G08G1/0112—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from the vehicle, e.g. floating car data [FCD]
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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
- G08G1/0133—Traffic data processing for classifying traffic situation
Abstract
The invention provides a method for calculating urban traffic running state characteristics based on floating car data, which is used for solving the problem of limitation of urban traffic running state characteristic calculation in the prior art. The method comprises the steps of preprocessing collected floating car data, carrying out grid division on a corresponding map, extracting node pairs to construct a node relation model, and finally carrying out traffic running state feature calculation. The method can realize regional traffic state characteristic calculation only by floating car data without depending on a GIS electronic map; the input data and the output result can be stored in a database and can be called at any time, so that the method is convenient and quick; the method comprises the steps of obtaining a road network model depending on node pairs, depicting the geographic features of the road network, wherein the geographic features can be stored in a database, do not need repeated calculation and are not limited by time, and the real-time traffic running state, the monthly, weekly, daily, hourly and 15-minute traffic running states can be obtained.
Description
Technical Field
The invention belongs to the field of urban traffic, and particularly relates to an urban traffic running state feature calculation method based on floating car data.
Background
With the continuous expansion of cities, urban traffic plays an increasingly important role in the development of cities. In order to enable the urban traffic to run smoothly, it is necessary to grasp the traffic running condition at any time and reflect it by characteristic calculation, evaluation, and the like of the urban traffic running state.
In the prior art, urban traffic running state feature calculation mainly aims at road sections, important nodes and the like, and features in an area are rarely considered; meanwhile, the traffic running state features often need to be based on an accurate road network electronic map, such as a Geographic Information System (GIS) electronic map, which has strong limitations; therefore, the urban traffic operation condition cannot be effectively, accurately and timely reflected.
Disclosure of Invention
In order to improve the accuracy of urban traffic operation supervision and overcome the limitation problem of urban traffic operation state feature calculation in the prior art, the invention provides an urban traffic operation state feature calculation method based on floating car data, which is used for calculating the traffic operation state features of an area road network, such as average speed, speed ratio, average delay, total delay, delay ratio and the like, does not depend on a GIS electronic map, and only obtains the urban area traffic operation state features through the floating car data, so that the calculation speed is higher, and the result is more accurate.
In order to achieve the purpose, the invention adopts the following technical scheme.
The invention provides a method for calculating urban traffic running state characteristics based on floating car data, which comprises the following steps:
step S1, floating car data preprocessing;
step S2, dividing map grids;
step S3, node pair extraction;
and step S4, calculating the traffic running state characteristics.
Further, the floating car data in the step S1 at least includes longitude, latitude, speed, and time fields, and the sampling interval does not exceed 10S.
Further, the characteristics of the traffic running state in the urban area comprise total delay, average delay, delay ratio, average speed and speed ratio.
Further, the preprocessing of the floating car data in the step S1 specifically includes the following steps:
and step S11, converting the floating car data into a universal WGS-84 coordinate system.
And step S12, removing the data with the speed larger than the threshold value, classifying the data according to the floating car labels, extracting the data of each track and removing the data which are continuously in the parking state.
Further, the gridding division of the map in the step S2 specifically includes: and dividing the region of the floating car data corresponding range into a plurality of small areas, numbering each small area, and recording the coordinate range.
Further, the node pair extraction in step S3 includes the following sub-steps:
step S31, extracting the intersection point of the track and the grid boundary through the floating car track data passing through the grid boundary;
step S32, clustering grid boundary intersection points through an improved DBSCAN algorithm to obtain possible road network access points;
step S33, judging the possible road network access points obtained by clustering, eliminating clusters with the number of clustering middle points smaller than a threshold value n, taking the rest clusters as road access points, wherein the center positions of the clusters are road center positions, and the diameters of the clusters are road widths;
step S34, calculating the track number of each node in and out of each grid according to the track data of the floating car, and screening out node relation pairs with the track number larger than a threshold value l as a road network communication rule in the grid; and constructing a road network node pair relation model by extracting node pair relation data.
Further, the calculation formula of the intersection point of the extraction trajectory and the grid boundary in step S31 is as follows:
in the formulae (1) and (2),
represents that the jth track passes through the r th track in the e directionkThe intersection points of the grids, x and y respectively represent longitude and latitude coordinates of the intersection points, k represents the number of the grids, m represents the number of columns of the grids, and (x) represents the number of the grids1,y1) And (x)2,y2) Representing the latitude and longitude coordinates of the southwest corner and the northeast corner of the area respectively.
Further, the steps of the improved DBSCAN clustering algorithm in step S32 are as follows:
step S321, inputting a search radius epsilon and a minimum core density point value MinPts;
step S322, judging whether the input point is a core object, if the sum of the number of the input points in the search radius is larger than MinPts, the input point is the core object, otherwise, the input point is not the core object;
step S323, finding out all direct density reachable points of the core object in the search radius;
step S324, determining whether all the input points have been traversed, if yes, going to step S325, otherwise, going to step S322;
step S325, finding the maximum density connection object set aiming at all the direct density reachable points of the core object in the search radius, and combining the density reachable points;
step S326, repeating step S325 until all the core objects are traversed, and obtaining a set of all the clusters;
step S327, count each cluster setCalculating the aggregate gravity center shift rate, and judging whether the aggregate gravity center shift rate is greater than a threshold value
If yes, go to step S328; if not, the cluster set corresponding to the set gravity center offset rate is a final cluster;
step S328, for all the parameters greater than
Calculates a new search radius and MinPts value, and proceeds to step S322.
Further, the calculation formula of the center of gravity shifting rate in step S327 is as follows:
DR(Class)＝|MC(Class)-GC(Class)|/Line(Class) (3)
in the formula (3), mc (class) is the arithmetic center of the class, gc (class) is the geometric center of the class, and line (class) is the spatial range of the class.
Further, the calculation process of extracting the traffic running state feature in the area in step S4 specifically includes:
step S41, sorting the transit time according to the floating car data of each node pair from small to large, and calculating the transit time of each node pair with 10% quantiles as free flow time;
step S42, sorting the floating car data pair speed of each node pair from small to large, and calculating the speed of 90% quantile of each node pair as the free flow speed;
and step S43, calculating the traffic running state characteristics of each region according to the calculated free flow time and free flow speed.
According to the technical scheme, the urban traffic running state characteristic calculation method based on the floating car data has the following beneficial effects that: regional traffic state feature calculation can be realized only by floating car data without depending on a GIS electronic map; the input data and the output result can be stored in a database and can be called at any time, so that the method is convenient and quick; the method comprises the steps of obtaining a road network model depending on node pairs, depicting geographic features of a road network, wherein the geographic features can be stored in a database without repeated calculation, and the obtained traffic state features are not limited by time, obtaining real-time traffic running states, and obtaining traffic running states of every month, every week, every day, every hour and every 15 minutes.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic flow chart of a method for calculating characteristics of an urban traffic running state according to an embodiment of the invention;
FIG. 2 is a schematic diagram of a road network node-to-node relationship model of an urban traffic operation state in a five-ring range of Beijing City, which is constructed by the application example of the present invention, throughout the whole day of a working day;
FIG. 3 is a velocity ratio distribution diagram of urban traffic operation status features extracted in an exemplary application of the present invention;
FIG. 4 is a graph showing an average speed distribution among the extracted characteristics of the traffic operating states of the urban area according to an exemplary embodiment of the present invention;
FIG. 5 is a diagram illustrating a total delay distribution in urban traffic operation status features extracted in an exemplary application of the present invention;
FIG. 6 is a graph showing the distribution of delay ratios in urban area traffic behavior features extracted in an application example of the present invention;
fig. 7 is a distribution diagram of average delay in the urban traffic operation state features extracted in the application example of the invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or coupled. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
For the convenience of understanding the embodiments of the present invention, the following description will be further explained by taking several specific embodiments as examples in conjunction with the drawings, and the embodiments are not to be construed as limiting the embodiments of the present invention.
With the development of the floating car technology, the intelligent traffic field is supported and perfected by the data of the floating car which is not separated from the intelligent traffic field in the aspects of signal control, state recognition, congestion monitoring and the like. The floating car technology has the characteristics of accuracy, instantaneity, stability and the like, so that the floating car technology is often used as basic data to be widely applied to various aspects of urban traffic. However, there has been no feature calculation of the running state of urban area traffic based on floating car data. The invention provides a method for calculating urban traffic running state characteristics based on floating car data and a node pair model, which finds out the road position and width of a passing area through the floating car data only and a noisy density-based clustering method (DBSCAN) and connects the road position and width as a node pair; the method comprises the steps of carrying out refined operation on traffic running state characteristics such as average speed, speed ratio, average delay, total delay and delay ratio of an urban area road network based on a node pair model, and calculating the traffic running state characteristics of the urban area without depending on a GIS electronic road network map by adopting the steps of floating car data preprocessing, map grid division, node pair extraction, traffic running state characteristic calculation and the like.
According to one embodiment of the invention, a city traffic running state feature calculation method based on floating car data is provided. Fig. 1 is a schematic flow chart of a method for calculating characteristics of an urban traffic running state according to this embodiment. As shown in fig. 1, the method comprises the steps of:
step S1, floating car data preprocessing;
step S2, dividing map grids;
step S3, node pair extraction;
and step S4, calculating the traffic running state characteristics.
Wherein, in the step S1:
the floating car data at least comprises fields of longitude and latitude, speed, time and the like, and the sampling interval does not exceed 10s, so that the consistency of the track data of the floating car can be ensured.
Further, the floating car data preprocessing specifically comprises the following steps:
and step S11, converting the floating car data into a universal WGS-84 coordinate system.
And step S12, removing the data with the speed larger than the threshold value, classifying the data according to the floating car labels, extracting the data of each track and removing the data which are continuously in the parking state.
In the step S2:
and dividing the region of the floating car data corresponding range into a plurality of small areas, numbering each small area, and recording the coordinate range.
The small region may be of any shape. Preferably, the region of the floating car data corresponding range is divided into small regions with side lengths of 0.5-2 km by a gridding method, the small regions are displayed as small grids corresponding to the small regions on a map, and the number, coordinate range and other characteristics of each grid are recorded.
In the step S3:
and extracting geographic information attributes, namely node pair positions and association relation, from the floating car data.
Further, the node pair extraction includes the following sub-steps:
and step S31, extracting the intersection points of the track and the grid boundary through the floating car track data passing through the grid boundary.
Further, the calculation formula of the intersection point of the extraction trajectory and the grid boundary is as follows:
in the formulae (1) and (2),
represents that the jth track passes through the r th track in the e directionkThe intersection points of the grids, x and y respectively represent longitude and latitude coordinates of the intersection points, k represents the number of the grids, m represents the number of columns of the grids, and (x) represents the number of the grids1,y1) And (x)2,y2) Latitude and longitude respectively representing southwest corner and northeast corner of the regionAnd (4) marking.
The time passing through the intersection point can be obtained by a two-point linear equation.
And step S32, clustering the grid boundary intersection points through an improved DBSCAN algorithm to obtain possible road access points.
Further, the improved DBSCAN clustering algorithm comprises the following steps:
step S321, inputting a search radius epsilon and a minimum core density point value MinPts;
step S322, judging whether the input point is a core object, if the sum of the number of the input points in the search radius is larger than MinPts, the input point is the core object, otherwise, the input point is not the core object;
step S323, finding out all direct density reachable points of the core object in the search radius;
step S324, determining whether all the input points have been traversed, if yes, going to step S325, otherwise, going to step S322;
step S325, finding the maximum density connection object set aiming at all the direct density reachable points of the core object in the search radius, and combining the density reachable points;
step S326, repeating step S325 until all the core objects are traversed, and obtaining a set of all the clusters;
step S327, calculating the aggregate gravity center shift rate of each cluster set, and judging whether the aggregate gravity center shift rate is greater than a threshold value
If yes, go to step S328; if not, the cluster set corresponding to the set gravity center offset rate is a final cluster;
step S328, for all the parameters greater than
Calculates a new search radius and MinPts value, and proceeds to step S322.
In step S327, the calculation formula of the center of gravity shift rate is as follows:
DR(Class)＝|MC(Class)-GC(Class)|/Line(Class) (3)
in the formula (3), mc (class) is the arithmetic center of the class, gc (class) is the geometric center of the class, and line (class) is the spatial range of the class.
And step S33, judging the possible road network access points obtained by clustering, eliminating clusters with the number of the clustering points smaller than a threshold value n, taking the rest clusters as road access points, wherein the center positions of the clusters are road center positions, and the diameters of the clusters are road widths.
Step S34, calculating the track number of each node in and out of each grid according to the track data of the floating car, and screening out node relation pairs with the track number larger than a threshold value l as a road network communication rule in the grid; and constructing a road network node pair relation model by extracting node pair relation data.
In the step S4:
and extracting traffic running state characteristics in the urban area according to the road network node pair relation model and the floating car data obtained in the step S3.
Further, the characteristics of the traffic running state in the urban area comprise characteristics of total delay, average delay, delay ratio, average speed, speed ratio and the like.
Further, the calculation process for extracting the traffic running state features in the area specifically includes:
step S41, sorting the transit time according to the floating car data of each node pair from small to large, and calculating the transit time of each node pair with 10% quantiles as free flow time;
step S42, sorting the floating car data pair speed of each node pair from small to large, and calculating the speed of 90% quantile of each node pair as the free flow speed;
and step S43, calculating the traffic running state characteristics of each region according to the calculated free flow time and free flow speed.
Furthermore, in this step, the calculation formula of the traffic operation state characteristics of each area is as follows:
in formulae (4) to (8), Dk、
rdk、
rvkRespectively representing the total traffic delay, the average traffic delay, the delay ratio, the average speed and the speed ratio of the kth grid area;
representing the free flow time of the jth node pair of the kth mesh; t is tijkThe travel time of the ith track of the jth node pair representing the kth grid; p is a radical ofjRepresenting the number of traces of the jth node pair; q. q.skA number of node pairs representing a kth grid; v. ofijkAverage travel speed of the ith trajectory representing the jth node pair of the kth grid;
representing the free flow velocity of the jth node pair of the kth mesh.
Further, the method for calculating the characteristics of the urban traffic running state may further include:
and step S5, visually displaying.
The step draws all the traffic running state indexes on a map, wherein the darker the color represents the worse the traffic running state represented by the index, and the lighter the color represents the more unblocked the traffic running state represented by the index.
The urban traffic running state feature calculation method of the invention is further explained by a specific application example. Taking the traffic state feature calculation of the whole day of a working day in the five-ring range of Beijing as an example, the following steps are adopted to calculate the urban traffic operation state feature:
and step S1001, preprocessing floating car data.
Floating car data of a whole day of a working day in a five-ring range in Beijing city are collected, the floating car data comprise fields of longitude, latitude, speed, time and the like, and the sampling interval is 10 s; carrying out coordinate conversion on the floating car data to convert the floating car data into an international universal WGS-84 coordinate system; and removing the data with the speed larger than the threshold value, classifying the data according to the floating car labels, extracting the data of each track and removing the data which are continuously in the parking state.
Step S1002, divide the area in the five-ring range of beijing into 30 (one) × 30 (one) rectangular grids, number 900 grids, and record the coordinate range of each grid.
In step S1003, node pairs are extracted.
And (4) extracting node pair relation data according to the calculation formulas and the operation methods in the steps S31 to S34, and constructing a road network node pair relation model.
FIG. 2 is a schematic diagram of a road network node-to-node relation model of an urban traffic operation state in a five-ring range of Beijing City, which is constructed in the present step, throughout the day of a working day.
And step S1004, extracting characteristics of the traffic running state in the region, including characteristics such as total delay, average delay, delay ratio, average speed, speed ratio and the like, according to the road network node pair relation model and the floating car data obtained in the step S1003. The specific extraction process is the same as steps S41 to S43.
Step S1005, drawing each traffic operation state index on the map, wherein the darker the color represents the worse the traffic operation state represented by the index, and the lighter the color represents the more unblocked the traffic operation state represented by the index.
FIG. 3 is a velocity ratio distribution diagram of the urban area traffic operation state feature extracted in the present application example; fig. 4 is a graph showing an average speed distribution among the extracted urban area traffic operation state features in the present application example; FIG. 5 is a diagram illustrating a total delay distribution in the urban area traffic operation status features extracted in the present application example; FIG. 6 is a diagram showing a distribution of delay ratios in urban area traffic operation state features extracted in the present application example; fig. 7 is a diagram showing an average delay distribution diagram in the urban area traffic operation state feature extracted in the present application example. As shown in fig. 3 to 7, the speed within the range of four rings of working days in beijing is relatively low, the delay ratio and the average delay are relatively large, and the traffic state is relatively poor; the average speed on the expressway is higher, but the total delay is also higher, and the overall traffic state presents the characteristics of poor interior and excellent exterior. The atlas of fig. 3 to fig. 7 reflects the traffic state characteristics of beijing city intuitively, and provides method support for studying and judging the urban traffic state and identifying congestion.
According to the technical scheme, the urban traffic running state feature calculation method based on the floating car data has the following beneficial effects that: regional traffic state feature calculation can be realized only by floating car data without depending on a GIS electronic map; the input data and the output result can be stored in a database and can be called at any time, so that the method is convenient and quick; the method comprises the steps of obtaining a road network model depending on node pairs, depicting geographic features of a road network, wherein the geographic features can be stored in a database without repeated calculation, and the obtained traffic state features are not limited by time, obtaining real-time traffic running states, and obtaining traffic running states of every month, every week, every day, every hour and every 15 minutes.
Those of ordinary skill in the art will understand that: the figures are merely schematic representations of one embodiment, and the blocks or flow diagrams in the figures are not necessarily required to practice the present invention.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for apparatus or system embodiments, since they are substantially similar to method embodiments, they are described in relative terms, as long as they are described in partial descriptions of method embodiments. The above-described embodiments of the apparatus and system are merely illustrative, and the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Those of ordinary skill in the art will understand that: the components in the devices in the embodiments may be distributed in the devices in the embodiments according to the description of the embodiments, or may be correspondingly changed in one or more devices different from the embodiments. The components of the above embodiments may be combined into one component, or may be further divided into a plurality of sub-components.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A city traffic running state feature calculation method based on floating car data is characterized by comprising the following steps:
step S1, floating car data are collected and preprocessed;
step S2, performing grid division on a region map corresponding to the floating car data;
step S3, extracting node pairs on the networked map and constructing a node pair relation model;
and step S4, calculating urban traffic running state characteristics according to the node pair relation model and the floating car data.
2. The method according to claim 1, wherein the floating car data in step S1 includes at least longitude, latitude, speed, and time fields, and the sampling interval does not exceed 10S.
3. The method according to claim 1, wherein the characteristics of the urban traffic running states include total delay, average delay, delay ratio, average speed and speed ratio.
4. The urban traffic running state feature calculation method according to any one of claims 1 to 3, wherein the preprocessing of the floating car data in the step S1 specifically comprises the following steps:
and step S11, converting the floating car data into a universal WGS-84 coordinate system.
And step S12, removing the data with the speed larger than the threshold value, classifying the data according to the floating car labels, extracting the data of each track and removing the data which are continuously in the parking state.
5. The urban traffic running state feature calculation method according to any one of claims 1 to 3, wherein the gridding division of the map of step S2 is specifically: and dividing the region of the floating car data corresponding range into a plurality of small areas, numbering each small area, and recording the coordinate range.
6. The urban traffic running state feature calculation method according to any one of claims 1 to 3, wherein the node pair extraction in step S3 comprises the following substeps:
step S31, extracting the intersection point of the track and the grid boundary through the floating car track data passing through the grid boundary;
step S32, clustering grid boundary intersection points through an improved DBSCAN algorithm to obtain possible road network access points;
step S33, judging the possible road network access points obtained by clustering, eliminating clusters with the number of clustering middle points smaller than a threshold value n, taking the rest clusters as road access points, wherein the center positions of the clusters are road center positions, and the diameters of the clusters are road widths;
step S34, calculating the track number of each node in and out of each grid according to the track data of the floating car, and screening out node relation pairs with the track number larger than a threshold value l as a road network communication rule in the grid; and constructing a road network node pair relation model by extracting node pair relation data.
7. The method according to claim 6, wherein the calculation formula of the intersection point of the extracted trajectory and the grid boundary in step S31 is as follows:
in the formulae (1) and (2),
representing the j-th track passing through the intersection point of the rk-th grid in the e direction, x and y respectively representing the longitude and latitude coordinates of the intersection point, k representing the number of the grids, m representing the number of the columns of the grid, (x)1,y1) And (x)2,y2) Representing the latitude and longitude coordinates of the southwest corner and the northeast corner of the area respectively.
8. The urban traffic running state feature calculation method according to claim 7, wherein the improved DBSCAN clustering algorithm in step S32 comprises the following steps:
step S321, inputting a search radius epsilon and a minimum core density point value MinPts;
step S322, judging whether the input point is a core object, if the sum of the number of the input points in the search radius is larger than MinPts, the input point is the core object, otherwise, the input point is not the core object;
step S323, finding out all direct density reachable points of the core object in the search radius;
step S324, determining whether all the input points have been traversed, if yes, going to step S325, otherwise, going to step S322;
step S325, finding the maximum density connection object set aiming at all the direct density reachable points of the core object in the search radius, and combining the density reachable points;
step S326, repeating step S325 until all the core objects are traversed, and obtaining a set of all the clusters;
step S327, calculating the aggregate gravity center shift rate of each cluster set, and judging whether the aggregate gravity center shift rate is greater than a threshold value
If yes, go to step S328; if not, the cluster set corresponding to the set gravity center offset rate is a final cluster;
step S328, for all the parameters greater than
Calculates a new search radius and MinPts value, and proceeds to step S322.
9. The method according to claim 8, wherein the formula for calculating the center of gravity shifting rate in step S327 is as follows:
DR(Class)＝|MC(Class)-GC(Class)|/Line(Class) (3)
in the formula (3), mc (class) is the arithmetic center of the class, gc (class) is the geometric center of the class, and line (class) is the spatial range of the class.
10. The urban traffic operation state feature calculation method according to any one of claims 1 to 3, wherein the calculation process of extracting the traffic operation state features in the area at step S4 specifically comprises:
step S41, sorting the transit time according to the floating car data of each node pair from small to large, and calculating the transit time of each node pair with 10% quantiles as free flow time;
step S42, sorting the floating car data pair speed of each node pair from small to large, and calculating the speed of 90% quantile of each node pair as the free flow speed;
and step S43, calculating the traffic running state characteristics of each region according to the calculated free flow time and free flow speed.
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