A method for analyzing and controlling road congestion

Abstract

The present invention relates to the technical field of traffic flow control, and discloses a method for analyzing and controlling road congestion, comprising the following steps: S01, obtaining historical road congestion data through an expert platform to obtain multiple driving roads; S02, combing the multiple driving roads at the same time to obtain the main roads in each of the driving roads and the collection data of multiple entrance node roads along the main roads; S03, generating a corresponding data acquisition chain according to the number of the collection data, and continuously acquiring the road condition data of the current driving road based on the data acquisition chain. The invention obtains historical road congestion data, combs the main roads and entrance node roads of the driving roads, continuously obtains the road condition data of the current and next moment, predicts the expected time of vehicles entering the main roads in the future, and generates a waiting time extension mechanism for traffic lights based on the reasonable capacity parameters of the main road traffic flow, the time of congestion occurrence and elimination.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic flow control, and in particular to a method for analyzing and controlling road congestion. Background Art

[0002] Road congestion is a relatively common situation. On the one hand, it is because the road surface converges into the main road and a large number of vehicles pour in, causing congestion on the main road. The traffic system that was originally designed to maintain traffic order has become one of the factors that aggravate road congestion during traffic jams.

[0003] Traffic systems can be used to provide data support in road congestion to help alleviate the embarrassment of road congestion, such as China Patent Publication No.: CN117116065A, which discloses a smart road traffic control method and system, which involves the field of traffic management technology, including: collecting road images, obtaining a first vehicle model set and a second vehicle model set, collecting image sequences, performing headway recognition, obtaining a first headway and a second headway, performing road congestion analysis, obtaining a first road congestion level and a second road congestion level, and taking the larger one as the road congestion level, comparing with the vehicle model congestion comparison table, obtaining a traffic flow diversion obstacle coefficient, making corrections, obtaining a corrected road congestion level, collecting multiple associated corrected road congestion levels, combining the corrected road congestion levels, calculating and obtaining a comprehensive road congestion level, making a control plan decision, obtaining a control plan, and controlling traffic lights. The present invention solves the technical problem that traditional traffic management cannot make timely adjustments according to real-time road congestion conditions, resulting in a mismatch between traffic lights and actual traffic needs.

[0004] In the prior art including the above-mentioned patent, the traffic system can display the congestion conditions on each road section, but it is unable to generate the propagation and spread process of road congestion based on road traffic data, let alone predict the spread trend of congestion. Summary of the invention

[0005] The purpose of the present invention is to provide a method for analyzing and controlling road congestion to solve the above-mentioned problems.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] A method for analyzing and controlling road congestion, comprising the following steps:

[0008] S01. Obtain historical road congestion data through an expert platform to obtain multiple driving roads;

[0009] S02, combing through the plurality of driving roads at the same time, obtaining the main road in each driving road and the aggregate data of the plurality of entrance node roads along the main road;

[0010] S03, generating a corresponding data acquisition chain according to the number of the set data, and continuously acquiring the current driving road condition data and the next driving road condition data based on the data acquisition chain;

[0011] The driving road condition data includes the main road condition and the entrance node road condition along the main road congestion starting point to the end point;

[0012] S04, taking the position of each vehicle on the entry node road as a secondary starting point, and the intersection where the entry node road enters the trunk road as a secondary ending point, and then obtaining the expected future entry time of each vehicle traveling on the entry node road into the secondary ending point based on the expert platform;

[0013] S05. Based on the acquired reasonable capacity parameters of the main road traffic flow, the time of occurrence of congestion and the time of elimination of congestion, a waiting time extension mechanism for traffic lights along the entrance node road is generated for feedback execution.

[0014] Preferably, the road congestion data collected in step S01 includes the number of road congestion items in rainy and snowy weather and the number of road congestion items in non-rainy and snowy weather, and two groups of sequence sets about multiple driving roads are obtained based on the number of road congestion items in rainy and snowy weather and the number of road congestion items in non-rainy and snowy weather, respectively, and the sequence sets include multiple driving roads.

[0015] Preferably, the number of items of road congestion in rainy and snowy weather also includes:

[0016] S11, obtaining matched historical road congestion data, determining mutation points in the historical road congestion data, and obtaining main road location information corresponding to the mutation points and meteorological characteristics of corresponding timestamps;

[0017] S12, generating a meteorological label according to the meteorological feature, clustering the location information corresponding to the mutation point according to the meteorological label, obtaining a set of mutation points under different meteorological labels, and determining the occurrence frequency of the main road location information corresponding to the different meteorological labels;

[0018] S13, obtaining time period data in the historical road congestion data corresponding to the frequency, and setting a feature label for the frequency based on the time period data;

[0019] S14, selecting the corresponding feature label according to the time period data of the current driving road, and including the main road location information of the frequency corresponding to the feature label into the sequence set corresponding to the number of road congestion items in the rainy and snowy weather.

[0020] Preferably, the main road in each of the driving roads and the collection data of multiple entrance node roads along the main road in step S02 are used to determine the location of the congested section of the main road, and the collection data of the multiple entrance node roads upstream and downstream of the congested section in the congested driving direction are determined based on the determined location of the congested section.

[0021] The aggregate data includes historical vehicle tidal data corresponding to the congestion occurrence time and the congestion elimination time at the location of the congested road section, and tidal average vehicle data is obtained based on the historical vehicle tidal data.

[0022] Preferably, in step S03, generating a corresponding data acquisition chain according to the number of the set data includes:

[0023] S31, based on the determined multiple driving roads, obtaining from the data at least one trunk road and the number of items of the multiple upstream entrance node roads and the number of items of the multiple downstream entrance node roads corresponding to the congested road section position of the trunk road;

[0024] S32: establishing a relationship network with the congested road section of the trunk road as the origin based on the determined number of items of the multiple entrance node roads on the upper route and the number of items of the multiple entrance node roads on the lower route;

[0025] S33, acquiring real-time vehicle data of the trunk road and the entry node road based on the determined relationship network;

[0026] S34, based on the execution of step S33, synchronously obtain the historical current driving road condition data and the historical next driving road condition data at the corresponding moment under the historical road congestion data, and make a difference with the current driving road condition data and the next driving road condition data, wherein:

[0027] The historical current driving road condition data and the historical next moment driving road condition data are maximum vehicle flow capacity parameters for non-congestion occurrence;

[0028] S35. Based on the result of the difference, a judgment is performed. If it is a positive value, there is no road congestion; if it is a negative value, congestion occurs.

[0029] Preferably, the waiting time extension mechanism for the traffic lights along the entrance node road generated in step S05 includes:

[0030] S51, obtaining the number of vehicles that can pass through the current congested road section per unit time based on the expert platform;

[0031] S52, obtaining the vehicle volume at the current location of the congested road section based on the expert platform;

[0032] S53, determining the total time required to restore the traffic flow at the current congested road section under reasonable capacity parameters according to the determined vehicle volume at the congested road section and the number of vehicles allowed to pass;

[0033] S54, determining the expected time of entry of each vehicle in the downstream multiple entry node roads in the future, and summarizing multiple vehicles with the same expected time of entry to obtain first reference data; wherein the expected time of entry of multiple vehicles with the same expected time of entry is the expected time of entry of vehicles within 1-2 minutes of each other;

[0034] S55, subtracting the number of vehicles entering the congested road section per unit time on the trunk road obtained based on the expert platform from the number of vehicles allowed to pass through to obtain the volume allowed to enter;

[0035] S56, finding the number of vehicles allowed to pass through each of the entry node roads per unit time, which is: ;

[0036] S57: based on the obtained entry node road allowing vehicles to pass, delaying the red light of the first traffic light on the entry node road to the secondary termination point on the downlink and uplink sides of the entry node road close to the congested road section by a threshold value.

[0037] Preferably, the expert platform includes a public navigation platform and navigation software background data.

[0038] In the above technical solution, the present invention provides a method for analyzing and controlling road congestion, which has the following beneficial effects:

[0039] According to the historical data provided by the expert platform, the number of road congestion items in rainy and snowy weather and the number of road congestion items in non-rainy and snowy weather are obtained, and multiple driving roads are obtained according to the actual time period and weather. Then, the multiple driving roads are sorted out to obtain the main road and multiple entry node roads along the road, and the location of the multiple entry node roads along the road entering the congested section of the main road is determined through the expected location of the congested section of the main road. Through data processing, a waiting time extension mechanism for traffic lights along the entry node roads is determined, so as to dynamically coordinate the traffic lights along the entry node roads, thereby coordinating the number of vehicles entering the main road, thereby eliminating road congestion in the process of alleviating the spread of road congestion.

[0040] 2. Identifying mutation points from historical road congestion data helps to discover sudden situations and potential causes of congestion, associate mutation points with meteorological characteristics, analyze the patterns and characteristics of congestion under different meteorological conditions, and provide a basis for formulating preventive measures. Based on time period data and frequency analysis, feature labels are set for congestion under different meteorological conditions to facilitate subsequent analysis and identification, so as to predict the location and number of future congested sections with faster matching, so as to provide early warnings and notify relevant departments for manual coordination.

[0041] 3. Clarify the location of congested sections of main roads, as well as the upstream and downstream entry node roads in the direction of congestion, provide accurate target areas for congestion prediction and control, collect historical vehicle tidal data on congested sections during the time of congestion occurrence and elimination, analyze vehicle flow patterns, and provide a basis for formulating traffic management measures.

[0042] 4. Based on the locations of congested sections of multiple driving roads and trunk roads, a data acquisition chain is constructed to ensure the accuracy and completeness of real-time data. A relationship network is established with the locations of congested sections of trunk roads as the origin to clarify the number of items of upstream and downstream entrance node roads and their correlation with trunk roads.

[0043] 5. Based on the traffic flow and vehicle capacity at the current congested road section, a mechanism to extend the waiting time of traffic lights will be established to alleviate congestion. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0045] Figure 1 A flowchart provided for an embodiment of the present invention;

[0046] Figure 2 A flow chart showing the relationship between multiple uplink and downlink entry node roads and the locations of congested sections provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] Embodiment 1:

[0049] like Figure 1 As shown, a method for analyzing and controlling road congestion includes the following steps:

[0050] S01. Obtain historical road congestion data through an expert platform to obtain multiple driving roads;

[0051] The collected road congestion data includes the number of road congestion items in rainy and snowy weather and the number of road congestion items in non-rainy and snowy weather, and two sets of sequence sets about multiple driving roads are obtained based on the number of road congestion items in rainy and snowy weather and the number of road congestion items in non-rainy and snowy weather, respectively, and the sequence sets include multiple driving roads;

[0052] S02. Sorting out a plurality of driving roads at the same time to obtain the aggregate data of the main road in each driving road and a plurality of entrance node roads along the main road;

[0053] S03, generating a corresponding data acquisition chain according to the number of set data, and continuously acquiring the current driving road condition data and the next driving road condition data based on the data acquisition chain;

[0054] S04, taking the position of each vehicle on the entry node road as the secondary starting point, and the intersection where the entry node road enters the main road as the secondary ending point, and then obtaining the future expected entry time of each vehicle traveling on the entry node road into the secondary ending point based on the expert platform;

[0055] S05. Based on the acquired reasonable capacity parameters of the main road traffic flow, the time of occurrence of congestion and the time of elimination of congestion, a waiting time extension mechanism for traffic lights along the entrance node road is generated for feedback execution.

[0056] Specifically, the driving road condition data in the above embodiment includes the main road condition and the entrance node road condition along the main road congestion starting point to the end point.

[0057] The expert platform includes a public navigation platform and navigation software background data, such as real-time background data of well-known navigation software such as Amap and Baidu Map.

[0058] Furthermore, in the above embodiment, based on the number of data acquisition chain projects obtained, a blockchain data processing channel of the data acquisition chain corresponding to the number of projects is generated, thereby ensuring that multiple data acquisition chains can simultaneously acquire, analyze and process data.

[0059] In the above technology, based on the historical data provided by the expert platform, the number of road congestion items in rainy and snowy weather and the number of road congestion items in non-rainy and snowy weather are obtained, and multiple driving roads are obtained according to the actual time period and weather. Then, the multiple driving roads are sorted out to obtain the main road and multiple entry node roads along the road, and the location of the multiple entry node roads along the road entering the congested section of the main road is determined through the expected location of the congested section of the main road. Through data processing, a waiting time extension mechanism for traffic lights along the entry node roads is determined, so as to dynamically coordinate the traffic lights along the entry node roads, thereby coordinating the number of vehicles entering the main road, thereby eliminating road congestion in the process of alleviating the spread of road congestion.

[0060] Embodiment 2:

[0061] Based on the above-mentioned first embodiment, this embodiment aims to more accurately predict the propagation process of congestion by considering the road congestion under different weather conditions, especially in bad weather. This helps to formulate more appropriate traffic management strategies for different weather conditions. The specific steps include:

[0062] S11, obtaining matched historical road congestion data, determining mutation points in the historical road congestion data, and obtaining main road location information corresponding to the mutation points and meteorological characteristics of corresponding timestamps;

[0063] S12, generating meteorological labels according to meteorological characteristics, clustering the location information corresponding to the mutation points according to the meteorological labels, obtaining sets of mutation points under different meteorological labels, and determining the occurrence frequency of the corresponding main road location information under different meteorological labels;

[0064] S13, obtaining time period data in historical road congestion data corresponding to the frequency, and setting a feature label for the frequency based on the time period data;

[0065] S14. Determine a corresponding feature label according to the time period data of the current driving road, and include the main road location information of the frequency corresponding to the feature label into a sequence set corresponding to the number of road congestion items in rainy and snowy weather.

[0066] Secondly, the main road in each driving road and the collection data of multiple entrance node roads along the main road are used to determine the location of the congested section of the main road, and based on the determined congested section location, the collection data of multiple entrance node roads in the upper direction and the collection data of multiple entrance node roads in the lower direction of the congested section location are determined (such as Figure 2 shown);

[0067] The aggregate data includes historical vehicle tidal data corresponding to the congestion occurrence time and elimination time at the location of the congested road section, and tidal average vehicle data is obtained based on the historical vehicle tidal data.

[0068] Specifically, the above embodiment refines the collection of historical road congestion data, including data in rainy and snowy weather and non-rainy and snowy weather, and obtains two sets of sequence sets about multiple driving roads. Identifying mutation points from historical road congestion data helps to discover sudden situations and potential causes of congestion, associate mutation points with meteorological characteristics, analyze the laws and characteristics of congestion under different meteorological conditions, and provide a basis for formulating preventive measures. Based on time period data and frequency analysis, feature labels are set for congestion under different meteorological conditions to facilitate subsequent analysis and identification, so as to foresee the location and number of future congested sections with faster matching, so as to provide prompts in advance and notify relevant departments for manual coordination.

[0069] In addition, by clarifying the location of congested sections of main roads and the upward and downward entry node roads in the direction of congestion, accurate target areas are provided for congestion prediction and control, and historical vehicle tidal data on congested sections during the time of congestion occurrence and elimination are collected, and vehicle flow patterns are analyzed to provide a basis for formulating traffic management measures.

[0070] Embodiment three:

[0071] Based on the above-mentioned first embodiment, this embodiment aims to generate a corresponding data acquisition chain according to the number of set data, and to determine whether a road is congested based on the difference between real-time and historical data. By comparing real-time and historical data, road congestion can be discovered in a timely manner, and real-time data on the propagation and spread of congestion can be generated. This provides timely and accurate information support for traffic management. The specific steps include:

[0072] S31, based on the determined multiple driving roads, obtaining from the data at least one trunk road and the number of items of multiple upstream entrance node roads and the number of items of multiple downstream entrance node roads corresponding to the congested road section position of the trunk road;

[0073] S32: establishing a relationship network with the congested road section of the trunk road as the origin based on the determined number of items of the upstream multiple entry node roads and the number of items of the downstream multiple entry node roads;

[0074] S33, acquiring real-time vehicle data of trunk roads and entrance node roads based on the determined relationship network;

[0075] S34, based on the execution of step S33, synchronously obtain the historical current driving road condition data and the historical next driving road condition data at the corresponding moment under the historical road congestion data, and make a difference with the current driving road condition data and the next driving road condition data, wherein:

[0076] The historical current driving road condition data and the historical next moment driving road condition data are the maximum traffic flow capacity parameters for non-congestion;

[0077] S35. Based on the difference result, a judgment is made. If it is a positive value, there is no road congestion; if it is a negative value, congestion occurs.

[0078] Specifically, based on the locations of congested sections of multiple driving roads and trunk roads, a data acquisition chain is constructed to ensure the accuracy and completeness of real-time data. A relationship network is established with the locations of congested sections of trunk roads as the origin to clarify the number of items on the upstream and downstream entrance node roads and their correlation with the trunk roads.

[0079] Embodiment 4:

[0080] Based on the above embodiment 1, this embodiment aims to control the number of vehicles entering the congested road section by adjusting the waiting time of the traffic light, thereby slowing down the spread of congestion. This method can flexibly adjust traffic signals according to real-time congestion conditions and improve road traffic efficiency.

[0081] The mechanism for generating a waiting time extension for traffic lights along the entry node road includes:

[0082] S51, obtaining the number of vehicles that can pass through the current congested road section per unit time based on the expert platform;

[0083] S52, obtaining the vehicle volume at the current congested road section based on the expert platform;

[0084] S53, determining the total time required to restore the current congested road section location under reasonable capacity parameters of the traffic flow according to the determined vehicle volume at the congested road section location and the number of vehicles allowed to pass;

[0085] S54, determining the expected time of entry of each vehicle in the downstream multiple entry node roads in the future, and summarizing multiple vehicles with the same expected time of entry to obtain first reference data; wherein the expected time of entry of multiple vehicles with the same expected time of entry is the expected time of entry of vehicles within 1-2 minutes of each other;

[0086] S55, the number of vehicles entering the congested road section per unit time on the main road obtained based on the expert platform is subtracted from the number of vehicles allowed to pass, to obtain the volume allowed to enter;

[0087] S56. Calculate the number of vehicles allowed to pass through each entrance node road per unit time: ;

[0088] S57: Based on the obtained entry node roads allowing vehicles to pass, the red light of the first traffic light of the downlink and uplink entry node roads to the secondary termination point close to the congested road section is delayed by a threshold value.

[0089] Specifically, we rely on the powerful analytical capabilities of the expert platform to accurately grasp the number of vehicles that can be allowed to pass through the current congested road section within a unit of time. This data is not obtained out of thin air, but is based on a comprehensive consideration of historical traffic data, road design capacity, traffic rules and real-time traffic conditions. Through the intelligent analysis of the expert platform, we can obtain data that is both in line with the actual situation and can effectively guide traffic management.

[0090] Next, the total number of vehicles in the current congested section, i.e., the vehicle volume, is obtained, which directly reflects the congestion level of the current section and is also an important basis for the subsequent formulation of control strategies. Only by accurately understanding the vehicle volume of the current section can targeted measures be taken to prevent further deterioration of congestion.

[0091] With the above two key data, we can start to calculate the total time required for the current congested road section to return to a reasonable traffic flow state. This process involves a deep understanding of the road capacity and a precise grasp of the current traffic conditions. Through scientific calculations, we can derive a rough time range, which provides strong support for subsequent traffic management.

[0092] However, it is not enough to just know the time required for recovery. We also need to consider how to slow down the speed of vehicles entering the congested road section by adjusting traffic lights within this time frame. To this end, we first need to determine the expected time for each vehicle to enter the main road in the downstream multiple entry node roads. In this process, we will pay special attention to those entry node roads with similar expected time of entry in the future (with a difference of no more than 1-2 minutes), and use the total number of vehicles on these roads as the first reference data.

[0093] Next, we use the data from the expert platform again to obtain the number of vehicles that enter the congested section of the main road per unit time, and compare it with the number of vehicles that can pass through to obtain the volume that can be allowed to enter. This data will help us understand how many vehicles the main road can accommodate to enter the congested section under the current traffic conditions.

[0094] With the above data, we can start to calculate the number of vehicles allowed to pass through each entry node road per unit time. This process involves comprehensive consideration of the first reference data, the volume allowed to enter, and the number of entry node roads that provide the same first reference data per unit time. Through accurate calculation, we can get the number of vehicles that can be allowed to pass through each entry node road without affecting the traffic capacity of the main road.

[0095] Finally, based on the above calculation results, a red light delay strategy will be implemented for the first traffic light to the secondary end point of the downlink and uplink entry node roads near the congested section. The delay time will be flexibly adjusted according to the number of vehicles allowed to pass through each entry node road and the actual traffic conditions. Through the implementation of this strategy, the speed of vehicles entering the congested section can be effectively slowed down, providing a valuable time window for traffic recovery on the main road.

[0096] In summary, the mechanism of extending the waiting time of traffic lights along the entrance node roads is a complex and sophisticated traffic management strategy that relies on the powerful analysis capabilities of the expert platform. By scientifically regulating the red light delay time of traffic lights, it can effectively alleviate the traffic pressure on the main roads and ensure unobstructed roads.

[0097] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0098] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable traffic control device to produce a machine, so that instructions executed by the processor of the computer or other programmable traffic control device generate instructions for implementing the process in the flowchart. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0099] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable traffic control device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0100] These computer program instructions may also be loaded onto a computer or other programmable traffic control device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0101] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

[0102] The embodiments of the present application also provide a specific implementation of an electronic device capable of implementing all steps of the method in the above embodiments, and the electronic device specifically includes the following contents:

[0103] Processor, memory, communications interface and bus;

[0104] Wherein, the processor, memory, and communication interface communicate with each other via the bus;

[0105] The processor is used to call the computer program in the memory, and when the processor executes the computer program, all the steps in the method in the above embodiment are implemented.

[0106] The embodiments of the present application also provide a computer-readable storage medium capable of implementing all the steps of the method in the above embodiments, wherein a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, all the steps of the method in the above embodiments are implemented.

[0107] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the hardware + program embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. Although the embodiment of this specification provides the method operation steps as described in the embodiment or flowchart, more or fewer operation steps may be included based on conventional or non-creative means. The order of steps listed in the embodiment is only one way of executing the order of many steps, and does not represent the only execution order. When the device or terminal product in practice is executed, it can be executed in sequence or in parallel according to the method shown in the embodiment or the accompanying drawings (for example, a parallel processor or a multi-threaded processing environment, or even a distributed traffic control environment). The term "include", "comprise" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, product or device including a series of elements includes not only those elements, but also includes other elements that are not explicitly listed, or also includes elements inherent to such a process, method, product or device. Without further restrictions, it is not excluded that there are other identical or equivalent elements in the process, method, product or device including the elements. For the convenience of description, the above device is described by dividing the functions into various modules and describing them separately. Of course, when implementing the embodiments of this specification, the functions of each module can be implemented in the same or one or more software and / or hardware, or the module implementing the same function can be implemented by a combination of multiple sub-modules or sub-units. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the coupling or direct coupling or communication connection between each other shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The present invention is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable traffic control device to generate a machine, so that the instructions executed by the processor of the computer or other programmable traffic control device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0108] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. In the description of this specification, the description of the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiments of this specification.

[0109] In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradicting each other. The above is only an embodiment of the embodiment of this specification and is not intended to limit the embodiment of this specification. For those skilled in the art, the embodiment of this specification may have various changes and variations. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiment of this specification shall be included in the scope of the claims of the embodiment of this specification.

Claims

1. A method for analyzing and controlling road congestion, characterized in that: The following steps are involved: S01. Obtain historical road congestion data through an expert platform to obtain multiple driving roads; S02, combing through the plurality of driving roads at the same time, obtaining the main road in each driving road and the aggregate data of the plurality of entrance node roads along the main road; S03, generating a corresponding data acquisition chain according to the number of the set data, and continuously acquiring the current driving road condition data and the next driving road condition data based on the data acquisition chain; S04, taking the position of each vehicle on the entry node road as a secondary starting point, and the intersection where the entry node road enters the trunk road as a secondary ending point, and then obtaining the expected future entry time of each vehicle traveling on the entry node road into the secondary ending point based on the expert platform; S05. Based on the acquired reasonable capacity parameters of the main road traffic flow, the time of occurrence of congestion and the time of elimination of congestion, a waiting time extension mechanism for traffic lights along the entrance node road is generated for feedback execution; In step S02, the main road in each of the driving roads and the collection data of the multiple entrance node roads along the main road are used to determine the location of the congested section of the main road, and based on the determined location of the congested section, the collection data of the multiple entrance node roads in the upper direction and the collection data of the multiple entrance node roads in the lower direction of the congested section are determined; The aggregate data includes historical vehicle tidal data corresponding to the congestion occurrence time and the congestion elimination time at the location of the congested road section, and tidal average vehicle data is obtained based on the historical vehicle tidal data; The step S03 generates a corresponding data acquisition chain according to the number of the set data, including: S31, based on the determined multiple driving roads, obtaining from the data at least one trunk road and the number of items of the multiple upstream entrance node roads and the number of items of the multiple downstream entrance node roads corresponding to the congested road section position of the trunk road; S32: establishing a relationship network with the congested road section of the trunk road as the origin based on the determined number of items of the multiple entrance node roads on the upper route and the number of items of the multiple entrance node roads on the lower route; S33, acquiring real-time vehicle data of the trunk road and the entry node road based on the determined relationship network; S34, based on the execution of step S33, synchronously obtain the historical current driving road condition data and the historical next driving road condition data at the corresponding moment under the historical road congestion data, and make a difference with the current driving road condition data and the next driving road condition data, wherein: The historical current driving road condition data and the historical next moment driving road condition data are maximum vehicle flow capacity parameters for non-congestion occurrence; S35. Based on the result of the difference, a judgment is performed. If it is a positive value, there is no road congestion; if it is a negative value, congestion occurs.

2. A method for analyzing and controlling road congestion according to claim 1, characterized in that: The road congestion data collected in step S01 includes the number of road congestion items in rainy and snowy weather and the number of road congestion items in non-rainy and snowy weather, and two groups of sequence sets about multiple driving roads are obtained based on the number of road congestion items in rainy and snowy weather and the number of road congestion items in non-rainy and snowy weather, respectively, and the sequence sets include multiple driving roads.

3. A method for analyzing and controlling road congestion according to claim 2, characterized in that: The number of road congestion items in rainy and snowy weather also includes: S11, obtaining matched historical road congestion data, determining mutation points in the historical road congestion data, and obtaining main road location information corresponding to the mutation points and meteorological characteristics of corresponding timestamps; S12, generating a meteorological label according to the meteorological feature, clustering the location information corresponding to the mutation point according to the meteorological label, obtaining a set of mutation points under different meteorological labels, and determining the occurrence frequency of the main road location information corresponding to the different meteorological labels; S13, obtaining time period data in the historical road congestion data corresponding to the frequency, and setting a feature label for the frequency based on the time period data; S14, selecting the corresponding feature label according to the time period data of the current driving road, and including the main road location information of the frequency corresponding to the feature label into the sequence set corresponding to the number of road congestion items in the rainy and snowy weather.

4. A method for analyzing and controlling road congestion according to claim 1, characterized in that: The waiting time extension mechanism for the traffic lights along the entrance node road generated in step S05 includes: S51, obtaining the number of vehicles that can pass through the current congested road section per unit time based on the expert platform; S52, obtaining the vehicle volume at the current location of the congested road section based on the expert platform; S53, determining the total time required to restore the traffic flow at the current congested road section under reasonable capacity parameters according to the determined vehicle volume at the congested road section and the number of vehicles allowed to pass; S54, determining the expected time of entry of each vehicle in the downstream multiple entry node roads in the future, and summarizing multiple vehicles with the same expected time of entry to obtain first reference data; wherein the expected time of entry of multiple vehicles with the same expected time of entry is the expected time of entry of vehicles within 1-2 minutes of each other; S55, subtracting the number of vehicles entering the congested road section per unit time on the trunk road obtained based on the expert platform from the number of vehicles allowed to pass through to obtain the volume allowed to enter; S56, finding the number of vehicles allowed to pass through each of the entry node roads per unit time, which is: ； S57: based on the obtained entry node road allowing vehicles to pass, delaying the red light of the first traffic light on the entry node road to the secondary termination point on the downlink and uplink sides of the entry node road close to the congested road section by a threshold value.

5. A method for analyzing and controlling road congestion according to claim 1, characterized in that: The expert platform includes a public navigation platform and navigation software background data.

6. A method for analyzing and controlling road congestion according to claim 1, characterized in that: The driving road condition data includes the main road condition and the entrance node road condition along the main road congestion starting point to the end point.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the method for analyzing and controlling road congestion as described in any one of claims 1 to 6 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for analyzing and controlling road congestion as described in any one of claims 1 to 6 are implemented.

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