A traffic flow guidance and control method based on cloud computing

Abstract

The present invention discloses a traffic flow guidance and control method based on cloud computing, which relates to the field of traffic control and includes the following steps: setting up a traffic flow guidance management platform, obtaining basic road data, constructing a road traffic image, and obtaining the traffic flow data information of each section therein and its collection time; obtaining historical traffic flow data information and judging the congestion levels corresponding to each section; obtaining the congestion levels of each section according to the obtained traffic flow data information and generating a traffic situation image; presetting whether there is a correlation among the congestion levels of each section and other sections in the traffic situation image; generating a corresponding guidance plan according to the currently obtained traffic flow data information of this section and generating a corresponding auxiliary guidance plan according to the traffic flow data information of other sections with a correlation; adjusting the traffic lights and lanes of the corresponding sections according to the obtained guidance plan and auxiliary guidance plan to complete the traffic flow guidance and control; the present invention improves the efficiency of traffic flow guidance.

Description

Technical Field

[0001] The present invention relates to the field of traffic control, and specifically to a traffic flow guidance and control method based on cloud computing. Background Art

[0002] With the development of society and the improvement of people's living standards, people's travel modes are constantly changing. With the continuous increase in the number of vehicles, traffic management has become increasingly important. To strengthen urban traffic management, maintain good road traffic order, and ensure the safe and unobstructed travel of the masses are issues that we need to solve. Therefore, it is necessary to conduct guidance and control according to the traffic flow on the road;

[0003] A traffic flow guidance system and its control method for traffic accident sites with the publication number of CN109537489B discloses a traffic flow guidance system for traffic accident sites, including a bottom plate; a box body, the bottom of the box body is fixed on the top of the bottom plate; a telescopic groove, the bottom of the telescopic groove is fixed on the top of the box body, and a sliding plate is slidably connected between the two sides of the inner wall of the telescopic groove. The present invention relates to the technical field of traffic command equipment. This traffic flow guidance system and its control method for traffic accident sites have multiple control functions, avoiding the waste of time caused by different numbers of vehicles on different roads. When the sign rotates, road guidance will be carried out to avoid vehicles changing lanes and cutting in line randomly, improving the triggerability of traffic accidents. Moreover, through the settings of the display screen and the speaker, information display and sound prompts can be carried out, improving the convenience of road guidance, having multiple road guidance functions for the understanding of drivers in different positions, improving the smoothness of vehicle driving, and avoiding road congestion;

[0004] A method and system for optimizing urban traffic congestion with the publication number of CN116959275A discloses a method and system for optimizing urban traffic congestion, which relates to the field of traffic management technology. The method includes obtaining a detected road image and marking the detected road; determining a detected lane area according to a lane matching relationship; obtaining the traffic flow queue length in the detected lane area; judging whether the traffic flow queue length is greater than a congestion benchmark length; if it is greater, defining the lane as a congested lane, and determining an associated lane corresponding to the congested lane according to an intersection matching relationship, and defining the vehicle queue length of the associated lane as an associated length; determining a reference duration corresponding to the associated length according to a duration matching relationship; determining an associated demand duration according to all the reference durations, and determining a congestion guidance duration according to the signal light cycle duration and the associated demand duration, and controlling the signal light corresponding to the congested lane to have a green light for the congestion guidance duration. This application has the effect of facilitating the handling of traffic congestion situations.

[0005] However, only guiding the traffic flow on congested sections has problems such as low traffic flow guiding efficiency. In addition, guiding the congested traffic flow based on the adjacent sections without judging according to the congestion conditions of the corresponding sections and including them in the guiding process also affects the guiding efficiency to a certain extent. Therefore, how to improve the efficiency in the process of traffic flow guiding is a problem that needs to be solved. For this reason, a traffic flow guiding and control method based on cloud computing is provided herein. Summary of the Invention

[0006] In order to solve the above technical problems, the purpose of the present invention is to provide a traffic flow guiding and control method based on cloud computing.

[0007] The purpose of the present invention can be achieved by the following technical solutions: A traffic flow guiding and control method based on cloud computing includes the following steps:

[0008] Step S1: Set up a traffic flow guiding management platform, obtain the basic road data in the area, construct a road traffic image according to the basic road data, set up data monitoring nodes, obtain the traffic flow data information corresponding to each section in the road traffic image, and mark its acquisition time;

[0009] Step S2: Obtain the historical traffic flow data information, judge the congestion level corresponding to each section, generate the volatility period corresponding to each section according to the congestion level to which the historical traffic flow data information belongs, and store it;

[0010] Step S3: Obtain the congestion level corresponding to each section in the road traffic image at the current moment according to the obtained traffic flow data information, and perform visualization processing on it to generate a traffic situation image;

[0011] Step S4: Preset the traffic flow guiding priority according to the congestion level, obtain the sections with high traffic flow guiding priority in the traffic situation image, preset the basic guiding radius, obtain the congestion level of other sections within the basic guiding radius and the volatility period therein, and judge whether there is a correlation relationship with this section according to the congestion level of other sections at the current moment and the predicted congestion level at the next moment within the volatility period;

[0012] Step S5: Analyze and process the currently obtained traffic flow data information of this section, generate a corresponding guiding plan according to the processing result, and generate an auxiliary guiding plan for other sections having a correlation relationship with this section according to their corresponding traffic flow data information;

[0013] Step S6: Regulate the traffic lights and lanes of the corresponding sections according to the obtained guiding plan and auxiliary guiding plan to complete the traffic flow guiding and control.

[0014] Further, the process of constructing the road traffic image and collecting the traffic flow data information includes:

[0015] Set up a traffic flow diversion management platform, which is provided with an external window of the platform. The external window of the platform obtains the corresponding road distribution image based on satellite image data and is used for staff to input basic road data; the basic road data is the name of the corresponding road section, lane type information, fork information, road section position relationship, and vehicle comprehensive interval; construct a road traffic image based on the road distribution image and basic road data;

[0016] Set data monitoring nodes according to the road traffic image. A corresponding data acquisition terminal is set in the data monitoring node. The data acquisition terminal is used to obtain the traffic flow data information of the corresponding road section. The traffic flow data information includes vehicle flow data and vehicle density data. The vehicle flow data includes the lane flow data corresponding to each lane in the road section; the vehicle density data includes the lane density data corresponding to each lane in the road section.

[0017] Further, the process of obtaining the volatility period of each road section includes:

[0018] The historical traffic flow data information corresponding to each road section in the road traffic image is stored in the traffic flow diversion management platform. Obtain the historical traffic flow data information, set corresponding weight factors according to the importance degree of historical vehicle flow data and historical vehicle density data for road congestion, and obtain vehicle comprehensive data based on the relationship between the historical traffic flow data information and the corresponding weight factors, and obtain its collection time;

[0019] Obtain the vehicle comprehensive interval corresponding to the congestion level of the preset road information in the road traffic image; the congestion levels include non-congested, mildly congested, moderately congested, and severely congested in ascending order of severity; compare and analyze the obtained vehicle comprehensive data with the vehicle comprehensive interval, and judge its congestion level according to the vehicle comprehensive interval to which the vehicle comprehensive data belongs in the corresponding road section information;

[0020] Set a corresponding frequency spectrum curve for the obtained vehicle comprehensive data according to the collection time, extract the frequency spectrum characteristics of the frequency spectrum curve according to the frequency spectrum analysis algorithm, obtain its periodic fluctuation according to the obtained frequency spectrum characteristics, mark according to the congestion level to which the vehicle comprehensive data belongs to each unit time in the periodic fluctuation, generate the volatility period of the corresponding road section, and store it.

[0021] Further, the process of generating a traffic situation image includes:

[0022] Analyze and process the obtained traffic flow data information to obtain the corresponding vehicle comprehensive data, compare and analyze the vehicle comprehensive data obtained in each road section information with the corresponding vehicle comprehensive interval, obtain the congestion level in each road section information, and mark its collection time;

[0023] Obtain road traffic images, visualize the road traffic images according to the congestion levels corresponding to the information of each road section within the current acquisition time, and generate traffic event images; and update the obtained traffic event images in real time according to the acquisition time.

[0024] Furthermore, the process of obtaining the correlation between the road sections includes:

[0025] The traffic diversion management platform sets traffic diversion priorities according to the congestion level and severity from light to heavy, obtains a traffic event image, obtains a road section with a high priority according to the traffic diversion priority, obtains the congestion level of the road section, presets a basic diversion radius according to the congestion level, and obtains the congestion level and volatility period corresponding to other road sections within the basic diversion radius in the traffic event image with the road section as the center;

[0026] Mark the congestion levels corresponding to other road sections, obtain the unit time of the volatility cycle to which each other road section belongs according to the volatility cycle of each other road section and the current collection time, and obtain the congestion level corresponding to the next unit time of the volatility cycle according to the current unit time, and record it as the predicted congestion level; mark the predicted congestion level corresponding to other road sections; obtain the location of the corresponding road section and other road sections within the basic diversion radius in the traffic event image and the number of forks between them, and mark the number of forks corresponding to other road sections;

[0027] The congestion levels, predicted congestion levels and number of forks corresponding to other road sections within the basic diversion radius are processed respectively, and the correlation data corresponding to different data information between each road section is obtained based on the big data algorithm. The obtained correlation data is multiplied by the congestion level, predicted congestion level and number of forks respectively and the sum is obtained to obtain comprehensive correlation data; a correlation threshold is preset, and when the comprehensive correlation data is greater than or equal to the correlation threshold, it is indicated that there is correlation between the two road sections; when the comprehensive correlation data is less than the correlation threshold, there is no correlation between the two road sections;

[0028] Other associated road segments are temporarily stored according to their comprehensive associated data.

[0029] Furthermore, the process of obtaining the diversion plan corresponding to the road section includes:

[0030] Obtain the vehicle flow data and vehicle density data collected for the corresponding road section, obtain the lane types corresponding to the lane flow data and lane density data therein, process the lane flow data and lane density data according to the lane types, obtain the corresponding average lane flow and average density data according to the lane types, obtain the average vehicle comprehensive data corresponding to the lane types, compare and analyze the average vehicle comprehensive data corresponding to different lane types, obtain the difference data, set a difference threshold according to the smallest average vehicle comprehensive data, compare and analyze the difference data with the difference threshold, and when the difference data is greater than the difference threshold, set flexible lane guidance for this road section; when the difference data is less than or equal to the difference threshold, do not set flexible lane guidance for this road section;

[0031] There is a preset traffic light control adjustment curve of the average vehicle comprehensive data with respect to the passing time in the vehicle flow guidance management platform. Map the average vehicle comprehensive data corresponding to the lane types in this road section into the traffic light control adjustment curve to obtain the passing time and set flexible traffic light guidance;

[0032] Generate a guidance plan based on the flexible lane guidance and flexible traffic light guidance obtained for the corresponding road section.

[0033] Furthermore, the process of setting the auxiliary guidance plan according to the relevance result includes:

[0034] Obtain other road sections that are relevant to it, and obtain the current traffic flow data information and their positional relationships from the other relevant road sections; the positional relationships include traffic flow import relationships and traffic flow export relationships;

[0035] When the positional relationship between the other road section and its corresponding one is a traffic flow import relationship, obtain the average vehicle comprehensive of this road section, map it into the traffic light control adjustment curve, and obtain its passing time; set a slow passing time coefficient according to the current congestion level of this road section, and analyze and process the passing time of this road section according to the obtained slow passing time coefficient to obtain the auxiliary passing time of this road section;

[0036] When the positional relationship between the other road section and its corresponding one is a traffic flow export relationship, obtain its passing time, and set an accelerated passing time coefficient according to the current congestion level of this road section; analyze and process the passing time of this road section according to the obtained accelerated passing time coefficient to obtain the auxiliary passing time of this road section;

[0037] Generate an auxiliary guidance plan based on the auxiliary passing times corresponding to the other road sections that are relevant to the corresponding road section.

[0038] Furthermore, the process of vehicle flow guidance and control includes:

[0039] Regulate the traffic lights and lane control signals of the corresponding sections according to the obtained traffic guidance plan and auxiliary traffic guidance plan. Adjust the traffic lights corresponding to each section according to the generated passing time and auxiliary passing time to complete the traffic flow guidance and control.

[0040] Compared with the prior art, the beneficial effects of the present invention are:

[0041] By setting a fluctuating period for each section, obtaining the predicted congestion level within the corresponding time, the congestion level at the current time, and the number of intersections between each section according to the fluctuating period, obtaining the correlation data between each section, and judging whether there is a correlation between the corresponding sections according to the correlation data. When the corresponding section needs to conduct traffic flow guidance, it can be assisted by the sections associated with it, thereby improving the efficiency to a certain extent during the traffic flow guidance process of the corresponding section; in addition, by setting a corresponding fluctuating period for each section and predicting the congestion level within different unit times, it is possible to not only judge whether there is an abnormality in the traffic flow data information within the section according to the prediction result, but also take preventive measures in advance. Description of the Drawings

[0042] Figure 1 It is a schematic diagram of a traffic flow guidance and control method based on cloud computing according to an embodiment of the present application. Detailed Embodiment

[0043] As Figure 1 shown, a traffic flow guidance and control method based on cloud computing includes the following steps:

[0044] Step S1: Set up a traffic flow guidance management platform, obtain the basic road data in the area, construct a road traffic image according to the basic road data, set up data monitoring nodes, obtain the traffic flow data information corresponding to each section in the road traffic image, and mark its collection time;

[0045] Step S2: Obtain the historical traffic flow data information, judge the congestion level corresponding to each section, generate a corresponding fluctuating period for each section according to the congestion level to which the historical traffic flow data information belongs, and store it;

[0046] Step S3: Obtain the congestion level corresponding to each section at the current moment in the road traffic image according to the obtained traffic flow data information, and perform visualization processing on it to generate a traffic situation image;

[0047] Step S4: Preset the vehicle flow guidance priority according to the congestion level, obtain the road sections with a high vehicle flow guidance priority in the traffic situation image, preset the basic guidance radius, obtain the congestion levels of other road sections within the basic guidance radius and the volatility periods therein, and determine whether there is a correlation relationship with this road section according to the current congestion level of other road sections and the predicted congestion level at the next moment within the volatility period;

[0048] Step S5: Analyze and process the vehicle flow data information currently obtained for this road section, generate a corresponding guidance plan according to the processing results, and generate an auxiliary guidance plan for other road sections that have a correlation relationship with this road section according to their corresponding vehicle flow data information;

[0049] Step S6: Regulate the traffic lights and lanes of the corresponding road sections according to the obtained guidance plan and auxiliary guidance plan to complete the vehicle flow guidance and control.

[0050] It should be further noted that in the specific implementation process, the process of setting up the vehicle flow guidance management platform, obtaining the basic road data in the area, and the vehicle flow guidance management platform constructing the road traffic image according to the basic road data and marking the corresponding basic road data in the road traffic image includes:

[0051] Set up a vehicle flow guidance management platform, which is used to analyze and control the urban roads that need vehicle flow guidance. There is an external platform window, which is used for the vehicle flow guidance management platform to obtain the corresponding road distribution image based on satellite image data, and the staff in the vehicle flow guidance management platform input the basic road data; the basic road data includes the name of the road section, lane type information, fork information, road section position relationship, and vehicle comprehensive interval;

[0052] Analyze and process the road distribution image and the corresponding basic road data obtained in the external platform window, match the corresponding basic road data with each road in the road distribution image respectively, mark the road distribution image according to the matching results, and construct the corresponding road traffic image;

[0053] The vehicle flow guidance management platform obtains the completed constructed road traffic image, analyzes and processes it, divides the roads according to the intersections in the road traffic image, obtains the corresponding road section information, and sets corresponding marks according to its road name and division results.

[0054] The process of setting up traffic flow monitoring nodes according to the road traffic image, where the traffic flow monitoring nodes are used to obtain the vehicle flow data and vehicle density data at their corresponding positions, and mark and store the data information obtained in each traffic flow monitoring node includes:

[0055] Obtain a road traffic image, obtain the corresponding road section information therefrom according to the road traffic image, and set data monitoring nodes according to the set road section information; a traffic monitoring terminal and a density monitoring terminal are set in the data monitoring nodes; the data monitoring nodes obtain the lane information therein according to the road section information in the road traffic image, and set corresponding monitoring areas according to the lane information;

[0056] The traffic monitoring terminal is preset with traffic monitoring areas corresponding to each lane information, and obtains video data within the traffic monitoring areas; a preset traffic unit time is set, the obtained video data is subjected to video frame processing according to the traffic unit time, feature extraction is performed on the video frames within each traffic unit time, vehicle image data is obtained, the number of vehicles in the vehicle image data within each lane traffic monitoring area within the traffic unit time is obtained, lane traffic data is obtained, the lane traffic data corresponding to each lane information within the road section information is obtained, and an addition operation is performed on them to obtain the vehicle traffic data within the road section information;

[0057] The density monitoring terminal is preset with density monitoring areas corresponding to each lane information, and obtains video data within the density monitoring areas; a preset density unit time is set, the obtained video data is subjected to video frame processing according to the density unit time, the number of vehicles in the vehicle image data within each lane density monitoring area within each density unit time is obtained, lane density data is obtained, the lane traffic data corresponding to each lane information within the road section information is obtained, and an encryption operation is performed on them to obtain the vehicle density data within the road section information;

[0058] Mark the lane types corresponding to the obtained lane traffic data and lane density data;

[0059] Store the vehicle traffic data in each road section information obtained, the corresponding lane traffic data therein, the vehicle density data in each road section information, and the lane density data in the corresponding lanes, and mark their collection times according to the traffic unit time and the density unit time respectively;

[0060] It should be further noted that in the specific implementation process, the monitoring areas corresponding to the traffic monitoring areas and the density monitoring areas are different, and the corresponding acquisition methods of the video data are also different.

[0061] Obtain historical data information, analyze and process the historical data information, judge the congestion level to which the historical data information corresponding to each road section information belongs, and generate a volatility period corresponding to each road section information according to the congestion level to which the historical data information belongs; its specific implementation process includes:

[0062] Obtain the historical data information of each road section. The historical data information includes the historical vehicle flow data, historical vehicle density data of the road section information and their corresponding collection times; set corresponding weight factors according to the importance degree of historical vehicle flow data and historical vehicle density data for road congestion, and obtain vehicle comprehensive data based on the relationship between historical vehicle flow data information and the corresponding weight factors;

[0063] In the road traffic image set in the vehicle flow guidance and management platform, there are preset vehicle flow intervals, vehicle density intervals and vehicle comprehensive intervals corresponding to the congestion levels of the corresponding road information. The congestion levels include uncongested, slightly congested, moderately congested and severely congested;

[0064] Compare and analyze the obtained vehicle comprehensive data with the vehicle comprehensive interval; judge the vehicle comprehensive interval to which the corresponding vehicle comprehensive data in the corresponding road section information belongs, and judge its congestion level according to the vehicle comprehensive interval to which the corresponding vehicle comprehensive data in the corresponding road section information belongs;

[0065] Analyze and process the corresponding historical data information in each road section information, compare and analyze the vehicle flow data and vehicle density data obtained within the corresponding unit time with the vehicle flow intervals and vehicle density intervals corresponding to different congestion levels respectively to obtain its congestion level; when the congestion levels corresponding to the intervals to which the vehicle flow data and vehicle density data belong are not equal, mark the historical data information as abnormal; mark and process the road sections with abnormalities in the vehicle flow guidance and management platform to avoid unexpected situations;

[0066] Set the congestion level and its corresponding vehicle comprehensive data that the historical data information in each road section information belongs to within the corresponding unit time; set the frequency spectrum curve of the corresponding unit time with respect to the corresponding vehicle comprehensive data, extract the frequency spectrum characteristics of the frequency spectrum curve according to the frequency spectrum analysis algorithm, and judge whether there is a periodic fluctuation according to the obtained frequency spectrum characteristics. If there is a periodic fluctuation, set the corresponding fluctuation period according to its periodic fluctuation; if there is no periodic fluctuation, extend the corresponding frequency spectrum curve and perform repeated analysis until the fluctuation period is obtained;

[0067] It should be further noted that in the specific implementation process, the vehicle flow guidance and management platform stores and marks the fluctuation periods corresponding to each road section information in the road traffic image; in addition, the vehicle flow guidance and management platform updates and corrects the fluctuation periods corresponding to each road section information according to the update and processing of historical data information, and correlates the corrected fluctuation periods with each road section information in the road traffic image.

[0068] The obtained vehicle flow data and vehicle density data are analyzed and processed to obtain the congestion level of the current corresponding road section information, and the congestion level of each road section information in the obtained road traffic image is visualized to obtain a traffic event image. The specific implementation process includes:

[0069] Obtaining weight factors corresponding to vehicle flow data and vehicle density data set in the vehicle flow management platform, and analyzing and processing the corresponding vehicle flow data and vehicle density data according to the obtained weight factors to obtain comprehensive vehicle data;

[0070] Obtain the vehicle comprehensive interval of the corresponding congestion level of the corresponding road section information stored in the platform; compare and analyze the vehicle comprehensive data obtained in each road section information with its corresponding vehicle comprehensive interval, obtain the congestion level of each road section information, and mark it according to the collection time of vehicle flow data and vehicle density data;

[0071] Obtain a road traffic image, and perform visualization processing according to the congestion level corresponding to each road section information within the current acquisition time, wherein the congestion level includes no congestion, light congestion, moderate congestion and severe congestion; when the congestion level is no congestion, the corresponding road section information is marked in green; when the congestion level is light congestion, the corresponding road section information is marked in blue; when the congestion level is moderate congestion, the corresponding road section information is marked in orange; when the congestion level is severe congestion, the corresponding road section information is marked in red; and generate a traffic event image according to the marking result of the corresponding road section information in the road traffic image;

[0072] The generated traffic situation image is updated in real time according to the obtained vehicle flow data and vehicle density data.

[0073] The traffic diversion management platform sets traffic diversion priorities according to the congestion level and severity from light to heavy, obtains a traffic event image, obtains a road section with a high priority according to the traffic diversion priority, obtains the congestion level of the road section, presets a basic diversion radius according to the congestion level, and obtains the congestion level and volatility period corresponding to other road sections within the basic diversion radius in the traffic event image with the road section as the center;

[0074] Mark the congestion levels corresponding to other road sections, obtain the unit time of the volatility cycle to which each other road section belongs according to the volatility cycle of each other road section and the current collection time, and obtain the congestion level corresponding to the next unit time of the volatility cycle according to the current unit time, and record it as the predicted congestion level; mark the predicted congestion level corresponding to other road sections; obtain the location of the corresponding road section and other road sections within the basic diversion radius in the traffic event image and the number of forks between them, and mark the number of forks corresponding to other road sections;

[0075] Process the congestion level, predicted congestion level, and the number of intersections corresponding to other road segments within the basic diversion radius respectively. Based on big data algorithms, obtain the correlation data corresponding to different data information between each road segment. Multiply the obtained correlation data by the congestion level, predicted congestion level, and the number of intersections respectively and obtain their sum to obtain comprehensive correlation data. Preset a correlation threshold. When the comprehensive correlation data is greater than or equal to the correlation threshold, it indicates that there is a correlation between the two road segments. When the comprehensive correlation data is less than the correlation threshold, there is no correlation between the two road segments.

[0076] Temporarily store the other road segments with correlations according to their comprehensive correlation data.

[0077] Obtain the vehicle flow data and vehicle density data collected for the corresponding road segment, obtain the lane types corresponding to the lane flow data and lane density data among them, process the lane flow data and lane density data according to the lane types, obtain the corresponding average lane flow and average density data according to the lane types, obtain the average vehicle comprehensive data corresponding to the lane types, conduct a comparative analysis of the average vehicle comprehensive data corresponding to different lane types, obtain the difference data, set a difference threshold according to the smallest average vehicle comprehensive data, and conduct a comparative analysis of the difference data and the difference threshold. When the difference data is greater than the difference threshold, set lane flexible diversion for this road segment. When the difference data is less than or equal to the difference threshold, do not set lane flexible diversion for this road segment.

[0078] There is a preset traffic light control adjustment curve for the average vehicle comprehensive data with respect to the passing time in the vehicle flow diversion management platform. Map the average vehicle comprehensive data corresponding to the lane types within this road segment into the traffic light control adjustment curve to obtain the passing time and set traffic light flexible diversion.

[0079] Generate a diversion plan for the lane flexible diversion and traffic light flexible diversion obtained for the corresponding road segment.

[0080] It should be further noted that in the specific implementation process, there are flexible lanes set within the corresponding road segment. The flexible lanes are used to set the corresponding lane types according to requirements. The lane types include left-turn lanes, straight-through lanes, and right-turn lanes.

[0081] Obtain other road segments that are related to it. Obtain the current traffic flow data information and their positional relationships from the other road segments that are related. The positional relationships include traffic flow import relationships and traffic flow export relationships.

[0082] When the corresponding position relationship between other road sections is a traffic flow import relationship, obtain the average vehicle synthesis of the road section, map it to the traffic light control adjustment curve, and obtain its passing time; set a slow passing time coefficient according to the current congestion level of the road section, and analyze and process the passing time of the road section according to the obtained slow passing time coefficient to obtain the auxiliary passing time of the road section.

[0083] When the corresponding position relationship between other road sections is a traffic flow export relationship, obtain its passing time, and set an accelerated passing time coefficient according to the current congestion level of the road section; analyze and process the passing time of the road section according to the obtained accelerated passing time coefficient to obtain the auxiliary passing time of the road section.

[0084] Generate an auxiliary guidance plan according to the auxiliary passing times of other road sections associated with the corresponding road section.

[0085] It should be further noted that, in the specific implementation process, the coefficient information set in the platform is obtained by analyzing and training according to the historical traffic flow data information of each road section based on digital twin technology.

[0086] Regulate the traffic lights and lane control signals of the corresponding road sections according to the obtained guidance plan and auxiliary guidance plan, and adjust the traffic lights corresponding to each road section according to the generated passing time and auxiliary passing time to complete the traffic flow guidance and control.

[0087] The above embodiments are only used to illustrate the technical method of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A method for traffic diversion and control based on cloud computing, characterized in that: The following steps are involved: Step S1: Set up a traffic flow management platform, obtain basic road data in the area, build a road traffic image based on the basic road data, set up data monitoring nodes, obtain traffic flow data information corresponding to each road section in the road traffic image, and mark its collection time: A traffic diversion management platform is provided, wherein an external window of the platform is provided, the external window of the platform obtains the corresponding road distribution image based on the satellite image data, and is used for the staff to input the basic road data; the basic road data is the name of the corresponding road section, lane type information, fork information, road section position relationship and vehicle comprehensive interval; Constructing a road traffic image based on a road distribution image and basic road data; A data monitoring node is set according to the road traffic image, wherein a corresponding data acquisition terminal is set in the data monitoring node, and the data acquisition terminal is used to obtain traffic data information of the corresponding road section, wherein the traffic data information includes vehicle flow data and vehicle density data, wherein the vehicle flow data includes lane flow data corresponding to each lane in the road section; and the vehicle density data includes lane density data corresponding to each lane in the road section; Step S2: Obtain historical traffic data information, determine the congestion level corresponding to each road section, generate the volatility cycle corresponding to each road section according to the congestion level of the historical traffic data information, and store it: The traffic diversion management platform stores historical traffic data information corresponding to each road section in the road traffic image, obtains historical traffic data information, sets corresponding weight factors according to the importance of historical vehicle flow data and historical vehicle density data to road congestion, and obtains vehicle comprehensive data based on the relationship between historical traffic data information and corresponding weight factors, and obtains its collection time; Obtaining a vehicle comprehensive section preset with a congestion level corresponding to corresponding road information in a road traffic image; the congestion levels include no congestion, light congestion, moderate congestion and severe congestion in order from light to heavy severity; comparing and analyzing the obtained vehicle comprehensive data with the vehicle comprehensive section, and judging the congestion level according to the vehicle comprehensive section to which the corresponding vehicle comprehensive data in the corresponding road section information belongs; The obtained vehicle comprehensive data is set to a corresponding spectrum curve according to the collection time, and the spectrum characteristics are extracted from the spectrum curve according to the spectrum analysis algorithm. The periodic fluctuation is obtained according to the obtained spectrum characteristics, and the congestion level of the vehicle comprehensive data corresponding to each unit time in the periodic fluctuation is marked to generate the fluctuation period of the corresponding road section and store it; Step S3: obtaining the congestion level corresponding to each road section in the road traffic image at the current moment according to the obtained traffic flow data information, and performing visualization processing on the congestion level to generate a traffic event image; Step S4: preset the traffic diversion priority according to the congestion level, obtain the road section with high traffic diversion priority in the traffic event image, preset the basic diversion radius, obtain the congestion level of other road sections within the basic diversion radius and the volatility cycle therein, and judge whether there is a correlation relationship between the other road sections and the congestion level at the current moment and the predicted congestion level at the next moment within the volatility cycle: The traffic diversion management platform sets traffic diversion priorities according to the congestion level and severity from light to heavy, obtains a traffic event image, obtains a road section with a high priority according to the traffic diversion priority, obtains the congestion level of the road section, presets a basic diversion radius according to the congestion level, and obtains the congestion level and volatility period corresponding to other road sections within the basic diversion radius in the traffic event image with the road section as the center; Mark the congestion levels corresponding to other road sections, obtain the unit time of the volatility cycle to which each other road section belongs according to the volatility cycle of each other road section and the current collection time, and obtain the congestion level corresponding to the next unit time of the volatility cycle according to the current unit time, and record it as the predicted congestion level; Mark the predicted congestion levels corresponding to other road sections; obtain the locations of the corresponding road sections and other road sections within the basic diversion radius in the traffic event image and the number of forks between them, and mark the number of forks corresponding to other road sections; The congestion levels, predicted congestion levels, and number of forks corresponding to other road sections within the basic diversion radius are processed respectively, and the correlation data corresponding to different data information between each road section is obtained based on the big data algorithm. The obtained correlation data is multiplied by the congestion level, predicted congestion level, and number of forks respectively, and the sum is obtained to obtain comprehensive correlation data; A correlation threshold is preset. When the comprehensive correlation data is greater than or equal to the correlation threshold, it means that there is correlation between the two road sections; when the comprehensive correlation data is less than the correlation threshold, there is no correlation between the two road sections. Temporarily storing other related road sections according to their comprehensive related data; Step S5: Analyze and process the traffic flow data information currently obtained on the road section, and generate a corresponding traffic diversion plan based on the processing results: Obtain the vehicle flow data and vehicle density data collected on the corresponding road section, obtain the lane type corresponding to the lane flow data and lane density data, process the lane flow data and lane density data according to the lane type, obtain the corresponding average lane flow and average density data according to the lane type, obtain the average vehicle comprehensive data of the corresponding lane type, compare and analyze the average vehicle comprehensive data corresponding to different lane types, obtain the difference data, set the difference threshold according to the minimum average vehicle comprehensive data, compare and analyze the difference data with the difference threshold, and when the difference data is greater than the difference threshold, set lane flexible diversion for the road section; When the difference data is less than or equal to the difference threshold, the lane flexible diversion will not be set for the road section; The traffic flow diversion management platform is preset with a traffic light control adjustment curve of average vehicle comprehensive data on travel time, and the average vehicle comprehensive data of the corresponding lane type in the road section is mapped to the traffic light control adjustment curve to obtain the travel time and set the traffic light for flexible diversion; Generate a traffic flow plan based on the lane flexible traffic flow and traffic light flexible traffic flow obtained on the corresponding road section; Other road sections that are associated with this road section generate auxiliary traffic diversion plans based on their corresponding traffic data information: Acquire other road sections associated with it, and acquire the traffic flow data information of the other road sections at the current moment and the position relationship between them from the other road sections associated with it; the position relationship includes the traffic flow import relationship and the traffic flow export relationship; When the position relationship between other road sections and their corresponding positions is a traffic flow import relationship, the average vehicle integration of the road section is obtained, and it is mapped to the traffic light control adjustment curve to obtain its travel time; A slow travel time coefficient is set according to the current congestion level of the road section, and the travel time of the road section is analyzed and processed according to the obtained slow travel time coefficient to obtain the auxiliary travel time of the road section; When the position relationship between other road sections and the corresponding road sections is a traffic flow derived relationship, the travel time is obtained, and a speed-up travel time coefficient is set according to the current congestion level of the road section; the travel time of the road section is analyzed and processed according to the obtained speed-up travel time coefficient to obtain the auxiliary travel time of the road section; Generate an auxiliary traffic diversion plan according to the auxiliary travel time corresponding to other road sections associated with the corresponding road section; Step S6: Traffic lights and lanes of corresponding road sections are regulated according to the obtained traffic diversion plan and auxiliary traffic diversion plan to complete traffic diversion control.

2. The method for traffic diversion and control based on cloud computing according to claim 1, characterized in that: The process of generating the traffic event image comprises: Analyze and process the obtained traffic flow data information to obtain the corresponding vehicle comprehensive data, compare and analyze the vehicle comprehensive data obtained in each road section information with the corresponding vehicle comprehensive interval, obtain the congestion level in each road section information, and mark its collection time; Obtain road traffic images, visualize the road traffic images according to the congestion levels corresponding to the information of each road section within the current acquisition time, and generate traffic event images; and update the obtained traffic event images in real time according to the acquisition time.

3. The method for traffic diversion and control based on cloud computing according to claim 1, characterized in that: The process of traffic diversion and control includes: The traffic lights and lane control signals of the corresponding road sections are regulated according to the obtained traffic diversion plan and auxiliary traffic diversion plan, and the traffic lights corresponding to each road section are adjusted according to the generated passing time and auxiliary passing time to complete the traffic diversion and control.

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