Traffic status identification system based on big data collection and analysis

Abstract

The present invention belongs to the field of traffic state discrimination and involves big data analysis technology. It is used to solve the problem that the existing technology cannot improve the accuracy and applicability of traffic discrimination results through refined segmentation. Specifically, it is a traffic state discrimination system based on big data collection and analysis, including a traffic section segmentation module, a state fusion discrimination module and a multivariate feature analysis module connected in sequence; the state fusion discrimination module and the multivariate feature analysis module are both communicatively connected to the database; the present invention can segment the traffic management area and obtain several managed sections, and then differentiate the managed sections according to the positional relationship between the segmentation reference object and the managed section. Using different discrimination methods to perform traffic state discrimination on the reference section and the conventional section respectively can improve the accuracy of the discrimination result, and combine the time characteristics and spatial characteristics to evaluate the periodic traffic state of the traffic management area, thereby improving the traffic state optimization efficiency.

Description

Technical Field

[0001] The present invention belongs to the field of traffic state identification and relates to big data analysis technology, specifically to a traffic state identification system based on big data collection and analysis. Background Art

[0002] Traffic state identification refers to the analysis of traffic flow characteristics to determine the current operating status of the traffic system, including different states such as traffic congestion and smoothness. Traffic flow characteristics include parameters such as traffic volume, speed and density. Changes in these parameters can directly reflect changes in traffic conditions.

[0003] Patent publication number CN106056923B discloses a method for determining road traffic status based on traffic scene radar. This method uses equivalent traffic volume as a basic parameter to divide traffic status intervals, thereby accurately determining road traffic status. This method avoids the traditional problem of ignoring vehicle size and type when considering traffic volume. Its determination is more accurate and scientific, effectively improving traffic efficiency.

[0004] Patent publication number CN106846816B discloses a deep learning-based discretized traffic state identification method. This method, based on deep learning, can fully and realistically describe traffic conditions, eliminate the need for expert participation in traffic state feature selection, and semi-supervisedly and automatically construct a traffic state identification model.

[0005] However, none of the above existing technologies performs fine segmentation of the traffic status judgment area from the perspective of road segmentation, resulting in the above existing technologies being able to only perform traffic status judgment analysis from the perspective of the whole or incomplete segmentation, resulting in the judgment results being unable to reflect the traffic status of each specific road section. Based on this, the above existing technologies also do not have the function of optimizing decision analysis based on the judgment results after fine segmentation, resulting in the traffic status judgment results being only used to reflect the overall traffic pressure, and the application scenario is single. Summary of the Invention

[0006] The purpose of the present invention is to provide a traffic status identification system based on big data collection and analysis, which is used to solve the problem that the existing technology cannot improve the accuracy and applicability of traffic identification results through refined segmentation;

[0007] The technical problem to be solved by the present invention is: how to provide a traffic state identification system based on big data collection and analysis that can improve the accuracy and applicability of traffic identification results through refined segmentation.

[0008] The purpose of the present invention can be achieved through the following technical solutions:

[0009] The traffic state discrimination system based on big data collection and analysis includes a traffic segmentation module, a state fusion discrimination module, and a multivariate feature analysis module connected in sequence. The state fusion discrimination module includes a reference discrimination unit and a conventional discrimination unit. The multivariate feature analysis module includes a temporal feature analysis unit, a spatial feature analysis unit, and a non-habitual feature analysis unit. The state fusion discrimination module and the multivariate feature analysis module are both communicatively connected to a database.

[0010] The traffic section segmentation module segments the traffic management area into sections and obtains a plurality of management sections, and marks the management sections as reference sections or regular sections according to the segmentation reference objects;

[0011] The state fusion discrimination module generates a discrimination cycle of R1 natural days, divides the complete natural day in the discrimination cycle into several discrimination time periods and numbers them in chronological order;

[0012] The reference discrimination unit obtains the traffic volume of the reference road section within the discrimination period and marks it as traffic flow data, obtains the traffic flow discrimination value from the database, and marks the reference road section with traffic flow data less than the traffic flow discrimination value as a smooth road section; and marks the reference road section with traffic flow data not less than the traffic flow discrimination value as a congested road section;

[0013] The conventional determination unit calculates the average speed of all vehicles on the conventional road section in real time and marks it as vehicle speed data, marks the ratio of the vehicle speed data to the maximum speed limit value of the conventional road section as a vehicle speed evaluation value, and marks the conventional road section as a smooth road section or a congested road section according to the vehicle speed evaluation value;

[0014] The multivariate feature analysis module performs feature analysis based on the congestion determination results of the regular road section and the reference road section within the determination period.

[0015] Furthermore, the specific road segmentation process includes: extracting and marking traffic lights, elevated entrance ramps, elevated exit ramps, ring road entrance ramps and ring road exit ramps within the traffic management area as segmentation reference objects, extending and segmenting all traffic roads intersecting with them within the traffic management area with the segmentation reference objects as endpoints to obtain several management road sections, and the longest segmentation distance of each management road section is set to K1 meters. The management road section with any endpoint containing a segmentation reference object is marked as a reference road section, and the management road section with all endpoints that do not contain a segmentation reference object is marked as a regular road section.

[0016] Furthermore, the specific process of marking a regular road section as a smooth road section or a congested road section includes: obtaining a vehicle speed evaluation range through a database, and comparing the vehicle speed evaluation value with the vehicle speed evaluation range: if there is a vehicle speed evaluation value less than the minimum boundary value of the vehicle speed evaluation range during the judgment period, the regular road section is marked as a congested road section; otherwise, the ratio of the duration of the vehicle speed evaluation value within the judgment period within the vehicle speed evaluation range to the duration of the judgment period is marked as a vehicle speed measurement coefficient, and the vehicle speed measurement threshold is obtained through a database. When the vehicle speed measurement coefficient is less than the vehicle measurement threshold, the regular road section is marked as a smooth road section, and when the vehicle speed measurement coefficient is greater than or equal to the vehicle measurement threshold, the regular road section is marked as a congested road section.

[0017] Furthermore, at the end moment of the judgment period, the time feature analysis unit marks the ratio of the number of markings of the congested road section within the judgment period to the number of managed road sections as the congestion coefficient of the judgment period, obtains the congestion threshold and the congestion marking threshold through the database, marks the judgment period with a congestion coefficient less than the congestion threshold as a smooth marking period, marks the judgment period with a congestion coefficient not less than the congestion threshold as a congestion marking period, marks the ratio of the number of congestion marking periods to the total number of judgment periods within the judgment period as the congestion marking value, when the congestion marking value is less than the congestion marking threshold, marks the time feature of the traffic management area as S1; when the congestion marking value is not less than the congestion marking threshold, marks the time feature of the traffic management area as S2, and sends the time feature of the traffic management area to the spatial feature analysis unit.

[0018] Furthermore, the spatial feature analysis unit marks the number of times a managed section is marked as a congested section as the spatial distribution value of the managed section, performs variance calculation on the spatial distribution values ​​of all managed sections to obtain the spatial distribution coefficient of the traffic management area, obtains the spatial distribution threshold through the database, and marks the spatial feature of the traffic management area as L1 when the spatial distribution coefficient is less than the spatial distribution threshold; and marks the spatial feature of the traffic management area as L2 when the spatial distribution coefficient is not less than the spatial distribution threshold.

[0019] Furthermore, whether the periodic traffic state of the traffic management area meets the requirements is determined by using the time characteristics and spatial characteristics: if the time characteristic of the traffic management area is S1, it is determined that the periodic traffic state of the traffic management area meets the requirements; if the time characteristic and spatial characteristic of the traffic management area are S2 and L1 respectively, it is determined that the periodic traffic state of the traffic management area does not meet the requirements, and a system optimization signal is generated and sent to the mobile phone terminal of the management personnel; if the time characteristic and spatial characteristic of the traffic management area are S2 and L2 respectively, it is determined that the local traffic state of the traffic management area does not meet the requirements, and the K2 management sections with the largest spatial distribution value are marked as inclined sections, and a non-habitual analysis signal is generated and sent to the non-habitual feature analysis unit.

[0020] Furthermore, when the non-habitual feature analysis unit receives the non-habitual analysis signal, it analyzes the non-habitual features of the traffic management area: the number of times the judgment period of the same time period number is marked as a congestion mark period is marked as the habitual analysis value of the time period number, and the K3 time period numbers with the largest habitual analysis value are marked as habitual numbers; the remaining time period numbers are marked as non-habitual numbers, the number of times the management section is marked as a congestion section in the judgment period corresponding to the non-habitual number is marked as the non-habitual congestion value, and the K4 management sections with the largest non-habitual congestion value are marked as inclined sections; an inclined management signal is generated and the inclined management signal and the inclined section are sent to the mobile phone terminal of the management personnel.

[0021] The present invention has the following beneficial effects:

[0022] The traffic segmentation module can be used to segment the traffic management area and obtain several managed segments. Then, the managed segments are marked differently based on the positional relationship between the segmentation reference and the managed segments. Due to the geographical characteristics of the reference segments, their traffic pressure is generally higher than that of the regular segments. Therefore, using different discrimination methods to distinguish the traffic status of the reference segments and the regular segments can improve the accuracy of the discrimination results.

[0023] The state fusion discrimination module can be used to discriminate and analyze the traffic state of the traffic management area. Traffic flow monitoring is used to discriminate the traffic state of the reference road section, and average speed monitoring is used to discriminate the traffic state of the regular road section. At the same time, the traffic discrimination results are recorded in a periodic time-segment monitoring manner to provide data support for the multivariate feature analysis process.

[0024] The multivariate feature analysis module can be used to perform feature analysis based on the congestion judgment results of regular sections and reference sections within the judgment period, and the periodic traffic status of the traffic management area can be evaluated by combining time characteristics and spatial characteristics. Based on the evaluation results, optimization decision analysis can be performed to improve the efficiency of traffic status optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0026] Figure 1 is a block diagram of the overall system of the present invention;

[0027] Figure 2Schematic diagram of road segmentation according to the present invention;

[0028] Figure 3 This is a working block diagram of the multivariate feature analysis module of the present invention. DETAILED DESCRIPTION

[0029] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0030] like Figure 1 As shown, the traffic state discrimination system based on big data collection and analysis includes a traffic section segmentation module, a state fusion discrimination module and a multivariate feature analysis module connected in sequence. The state fusion discrimination module includes a reference discrimination unit and a conventional discrimination unit. The multivariate feature analysis module includes a time feature analysis unit, a spatial feature analysis unit and a non-habitual feature analysis unit. Both the state fusion discrimination module and the multivariate feature analysis module are connected to the database for communication.

[0031] The traffic section segmentation module is used to segment the traffic management area and obtain several managed sections. The specific segmentation process includes: extracting and marking traffic lights, elevated entrance ramps, elevated exit ramps, ring road entrance ramps and ring road exit ramps in the traffic management area as segmentation references, extending and segmenting all traffic roads intersecting with the segmentation references in the traffic management area to obtain several managed sections. The longest segmentation distance of each managed section is set to K1 meters. The managed section with a segmentation reference at any endpoint is marked as a reference section, and the managed section without a segmentation reference at all endpoints is marked as a regular section; the managed sections are differentiated according to the positional relationship between the segmentation reference and the managed section. Due to the geographical characteristics of the reference section, its traffic bearing pressure is generally higher than that of the regular section. Therefore, using different judgment methods to judge the traffic status of the reference section and the regular section respectively can improve the accuracy of the judgment results.

[0032] It should be noted that the segmentation reference is shared by all the management sections intersecting with it; for example, Figure 2As shown, there is an existing viaduct G extending in parallel with the ground road D. The ground road D is connected to the viaduct G through an viaduct entrance ramp. The viaduct entrance ramp is marked as a segmentation reference F. The segmentation reference F is extended in both directions for K1 meters in the forward direction of the viaduct G and then segmented to obtain the first management section 1 and the second management section 2. The segmentation reference F is extended in both directions for K1 meters in the forward direction of the ground road D and then segmented to obtain the third management section 3 and the fourth management section 4. The obtained first management section 1, second management section 2, and third management section 4 are The endpoints of section 3 and the fourth management section 4 both contain the split reference object F, that is, the split reference object F is shared by the first management section 1, the second management section 2, the third management section 3, and the fourth management section 4, and the first management section 1, the second management section 2, the third management section 3, and the fourth management section 4 are all reference sections; when the first management section 1 is extended and split again from the endpoint away from the split reference object F to obtain the fifth management section 5, the two endpoints of the fifth management section 5 do not contain the split reference object F, that is, the fifth management section 5 is a regular section.

[0033] The state fusion discrimination module is used to perform traffic state discrimination analysis on the traffic management area: it generates a discrimination cycle of R1 natural days, where R1 is a numerical constant and the specific value of R1 is set by the management personnel; it divides the complete natural day in the discrimination cycle into several discrimination time periods and numbers them in chronological order;

[0034] During the discrimination period, the reference road section is subjected to traffic status discrimination analysis by using a reference discrimination unit: the traffic volume of the reference road section during the discrimination period is obtained and marked as traffic flow data, a traffic flow discrimination value is obtained from a database, and a reference road section with traffic flow data less than the traffic flow discrimination value is marked as a smooth road section; a reference road section with traffic flow data not less than the traffic flow discrimination value is marked as a congested road section;

[0035] During the discrimination period, the conventional road section is subjected to traffic status discrimination analysis by the conventional discrimination unit: the average speed of all vehicles in the conventional road section is calculated in real time and marked as vehicle speed data, the ratio of the vehicle speed data to the maximum speed limit value of the conventional road section is marked as a vehicle speed evaluation value, the vehicle speed evaluation range is obtained through the database, and the vehicle speed evaluation value is compared with the vehicle speed evaluation range: if there is a vehicle speed evaluation value less than the minimum boundary value of the vehicle speed evaluation range during the discrimination period, the conventional road section is marked as a congested road section; otherwise, the ratio of the duration of the vehicle speed evaluation value within the discrimination period being within the vehicle speed evaluation range to the duration of the discrimination period is marked as a vehicle speed measurement coefficient, the vehicle speed measurement threshold is obtained through the database, the conventional road section is marked as a smooth road section when the vehicle speed measurement coefficient is less than the vehicle measurement threshold, and the conventional road section is marked as a congested road section when the vehicle speed measurement coefficient is greater than or equal to the vehicle measurement threshold;

[0036] Traffic status discrimination and analysis is conducted in the traffic management area. Traffic status discrimination is conducted on the reference road sections using traffic flow monitoring. Traffic status discrimination is conducted on the regular road sections using average vehicle speed monitoring. At the same time, traffic discrimination results are recorded using periodic time-segment monitoring to provide data support for the multivariate feature analysis process.

[0037] The multivariate feature analysis module is used to perform feature analysis based on the congestion judgment results of the regular road section and the reference road section within the judgment period.

[0038] like Figure 3 As shown, the time feature analysis unit, the space feature analysis unit and the non-habitual feature analysis unit are communicatively connected in sequence.

[0039] At the end of the judgment period, the time characteristics of the traffic management area are analyzed by the time characteristic analysis unit: the ratio of the number of marks of the congested road section to the number of managed road sections within the judgment period is marked as the congestion coefficient of the judgment period, the congestion threshold and the congestion mark threshold are obtained through the database, the judgment period with a congestion coefficient less than the congestion threshold is marked as a smooth mark period, the judgment period with a congestion coefficient not less than the congestion threshold is marked as a congestion mark period, the ratio of the number of congestion mark periods to the total number of judgment periods within the judgment period is marked as a congestion mark value, when the congestion mark value is less than the congestion mark threshold, the time characteristics of the traffic management area are marked as S1; when the congestion mark value is not less than the congestion mark threshold, the time characteristics of the traffic management area are marked as S2, and the time characteristics of the traffic management area are sent to the spatial characteristic analysis unit;

[0040] At the end of the discrimination cycle, the spatial characteristics of the traffic management area are analyzed by the spatial characteristic analysis unit: the number of times the managed road section is marked as a congested road section is marked as the spatial distribution value of the managed road section, the variance of the spatial distribution values ​​of all managed road sections is calculated to obtain the spatial distribution coefficient of the traffic management area, and the spatial distribution threshold is obtained through the database. When the spatial distribution coefficient is less than the spatial distribution threshold, the spatial characteristic of the traffic management area is marked as L1, and when the spatial distribution coefficient is not less than the spatial distribution threshold, the spatial characteristic of the traffic management area is marked as L2;

[0041] If the time characteristic of the traffic management area is S1, it is determined that the periodic traffic state of the traffic management area meets the requirements;

[0042] If the time characteristics and spatial characteristics of the traffic management area are S2 and L1 respectively, it is determined that the periodic traffic state of the traffic management area does not meet the requirements, and a system optimization signal is generated and sent to the mobile phone terminal of the manager;

[0043] If the temporal and spatial characteristics of the traffic management area are S2 and L2 respectively, the local traffic state of the traffic management area is determined to be unsatisfactory, and the K2 managed road sections with the largest spatial distribution values ​​are marked as inclined road sections. At the same time, a non-habitual analysis signal is generated and sent to the non-habitual feature analysis unit.

[0044] At the end of the judgment period, the non-habitual features of the traffic management area are analyzed by the non-habitual feature analysis unit: the number of times the judgment period of the same period number is marked as a congestion mark period is marked as the habitual analysis value of the period number, and the K3 period numbers with the largest habitual analysis value are marked as habitual numbers; the remaining period numbers are marked as non-habitual numbers, the number of times the management section is marked as a congestion section in the judgment period corresponding to the non-habitual number is marked as the non-habitual congestion value, and the K4 management sections with the largest non-habitual congestion value are marked as inclined sections; it should be noted that K1, K2, K3 and K4 are all numerical constants, and the specific values ​​of K1, K2, K3 and K4 are set by the management personnel themselves; an inclined management signal is generated and the inclined management signal and the inclined section are sent to the mobile phone terminal of the management personnel; feature analysis is performed based on the congestion judgment results of the regular sections and the reference sections within the judgment period, and the periodic traffic status of the traffic management area is evaluated in combination with the time characteristics and spatial characteristics, so as to optimize the decision analysis based on the evaluation results and improve the efficiency of traffic status optimization.

[0045] The traffic status discrimination system based on big data collection and analysis, when working, divides the traffic management area into sections and obtains several management sections, marks the management sections as reference sections or regular sections, generates a discrimination cycle of R1 natural days, divides the complete natural days in the discrimination cycle into several discrimination time periods and number them in chronological order, performs traffic status discrimination analysis on the reference sections through the reference discrimination unit and marks the reference sections as congested sections or unobstructed sections; performs traffic status discrimination analysis on the regular sections through the regular discrimination unit and marks the regular sections as congested sections or unobstructed sections; performs feature analysis based on the congestion discrimination results of the regular sections and the reference sections within the discrimination cycle, and evaluates the periodic traffic status of the traffic management area by combining time characteristics and spatial characteristics, so as to perform optimization decision analysis based on the evaluation results and improve the efficiency of traffic status optimization.

[0046] The above content is merely an example and explanation of the structure of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the scope of protection of the present invention.

[0047] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0048] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. Traffic status identification system based on big data collection and analysis, characterized by: It includes a traffic section segmentation module, a state fusion discrimination module, and a multivariate feature analysis module connected in sequence. The state fusion discrimination module includes a reference discrimination unit and a conventional discrimination unit. The multivariate feature analysis module includes a time feature analysis unit, a spatial feature analysis unit, and a non-habitual feature analysis unit. The state fusion discrimination module and the multivariate feature analysis module are both connected to a database for communication. The traffic section segmentation module segments the traffic management area into sections and obtains a plurality of management sections, and marks the management sections as reference sections or regular sections according to the segmentation reference objects; The state fusion discrimination module generates a discrimination cycle of R1 natural days, divides the complete natural day in the discrimination cycle into several discrimination time periods and numbers them in chronological order; The reference discrimination unit obtains the traffic volume of the reference road section within the discrimination period and marks it as traffic flow data, obtains the traffic flow discrimination value from the database, and marks the reference road section with traffic flow data less than the traffic flow discrimination value as a smooth road section; The reference road section whose traffic flow data is not less than the traffic flow discrimination value is marked as a congested road section; The conventional determination unit calculates the average speed of all vehicles on the conventional road section in real time and marks it as vehicle speed data, marks the ratio of the vehicle speed data to the maximum speed limit value of the conventional road section as a vehicle speed evaluation value, and marks the conventional road section as a smooth road section or a congested road section according to the vehicle speed evaluation value; The multivariate feature analysis module performs feature analysis based on the congestion determination results of the regular road section and the reference road section within the determination period; The specific road segmentation process includes: extracting traffic lights, elevated entrance ramps, elevated exit ramps, ring road entrance ramps, and ring road exit ramps within the traffic management area and marking them as segmentation reference objects; extending and segmenting all traffic roads intersecting with the segmentation reference objects within the traffic management area to obtain several management road segments; the longest segmentation distance of each management road segment is set to K1 meters; marking a management road segment with a segmentation reference object at any endpoint as a reference road segment, and marking a management road segment with no segmentation reference objects at all endpoints as a regular road segment; Whether the periodic traffic state of the traffic management area meets the requirements is determined by using the time characteristics and spatial characteristics: if the time characteristic of the traffic management area is S1, it is determined that the periodic traffic state of the traffic management area meets the requirements; if the time characteristic and spatial characteristics of the traffic management area are S2 and L1 respectively, it is determined that the periodic traffic state of the traffic management area does not meet the requirements, and a system optimization signal is generated and sent to the mobile phone terminal of the management personnel; if the time characteristic and spatial characteristics of the traffic management area are S2 and L2 respectively, it is determined that the local traffic state of the traffic management area does not meet the requirements, and the K2 management sections with the largest spatial distribution values ​​are marked as inclined sections, and a non-habitual analysis signal is generated and sent to the non-habitual feature analysis unit; The non-habitual feature analysis unit analyzes the non-habitual features of the traffic management area when receiving the non-habitual analysis signal: the number of times the judgment period of the same period number is marked as a congestion mark period is marked as the habitual analysis value of the period number, and the K3 period numbers with the largest habitual analysis value are marked as habitual numbers; the remaining period numbers are marked as non-habitual numbers, the number of times the management section is marked as a congestion section in the judgment period corresponding to the non-habitual number is marked as the non-habitual congestion value, and the K4 management sections with the largest non-habitual congestion value are marked as inclined sections; an inclined management signal is generated and the inclined management signal and the inclined section are sent to the mobile phone terminal of the management personnel.

2. The traffic status identification system based on big data collection and analysis according to claim 1 is characterized in that: The specific process of marking a regular road section as a smooth road section or a congested road section includes: obtaining the vehicle speed evaluation range through the database, and comparing the vehicle speed evaluation value with the vehicle speed evaluation range: if there is a vehicle speed evaluation value less than the minimum boundary value of the vehicle speed evaluation range during the judgment period, the regular road section is marked as a congested road section; otherwise, the ratio of the duration of the vehicle speed evaluation value within the judgment period within the vehicle speed evaluation range to the duration of the judgment period is marked as the vehicle speed measurement coefficient, and the vehicle speed measurement threshold is obtained through the database. When the vehicle speed measurement coefficient is less than the vehicle measurement threshold, the regular road section is marked as a smooth road section, and when the vehicle speed measurement coefficient is greater than or equal to the vehicle measurement threshold, the regular road section is marked as a congested road section.

3. The traffic status identification system based on big data collection and analysis according to claim 2 is characterized in that: The time feature analysis unit marks the ratio of the number of markings of the congested road section to the number of managed road sections within the judgment period as the congestion coefficient of the judgment period at the end moment of the judgment period, obtains the congestion threshold and the congestion marking threshold through the database, marks the judgment period with a congestion coefficient less than the congestion threshold as a smooth marking period, marks the judgment period with a congestion coefficient not less than the congestion threshold as a congestion marking period, marks the ratio of the number of congestion marking periods to the total number of judgment periods within the judgment period as the congestion marking value, marks the time feature of the traffic management area as S1 when the congestion marking value is less than the congestion marking threshold; marks the time feature of the traffic management area as S2 when the congestion marking value is not less than the congestion marking threshold, and sends the time feature of the traffic management area to the spatial feature analysis unit.

4. The traffic status identification system based on big data collection and analysis according to claim 3 is characterized in that: The spatial feature analysis unit marks the number of times a managed section is marked as a congested section as the spatial distribution value of the managed section, performs variance calculation on the spatial distribution values ​​of all managed sections to obtain the spatial distribution coefficient of the traffic management area, obtains the spatial distribution threshold through the database, and marks the spatial feature of the traffic management area as L1 when the spatial distribution coefficient is less than the spatial distribution threshold; and marks the spatial feature of the traffic management area as L2 when the spatial distribution coefficient is not less than the spatial distribution threshold.

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