A traffic intelligent guidance method and system based on a smart city

Abstract

The present invention relates to the technical field of traffic management, and discloses a traffic intelligent guidance method and system based on a smart city, including: determining the operation and maintenance regulations corresponding to traffic facilities, and formulating congestion evaluation elements for urban traffic areas; analyzing the traffic flow state of urban traffic areas, calculating the congestion disturbance coefficient corresponding to the congestion evaluation elements, and evaluating the congestion state intensity of urban traffic areas; analyzing the vehicle behavior intention characteristics of vehicles in urban traffic areas, analyzing the traffic condition matrix of roads in urban traffic areas, and calculating the traffic efficiency of roads in urban traffic areas; extracting attraction factor elements from land use information and real-time event information, and analyzing the traffic pressure conduction degree of the surrounding area on the urban traffic area; formulating traffic intelligent guidance strategies for urban traffic areas, implementing traffic guidance work in urban traffic areas, and obtaining traffic guidance results. The present invention can improve the traffic intelligent guidance efficiency of a smart city.

Description

Technical Field

[0001] The present invention relates to a traffic intelligent guidance method and system based on a smart city, and belongs to the technical field of traffic management. Background Art

[0002] At present, with the accelerating urbanization process, the construction of smart cities has become an important direction for urban development. As the key lifeline of urban operation, the efficient operation of the traffic system is crucial for the development of smart cities. With the continuous growth of urban population and the sharp rise in the number of motor vehicles, the problem of traffic congestion has become increasingly severe, bringing great inconvenience to the travel of urban residents, and at the same time increasing energy consumption and environmental pollution.

[0003] Currently, urban traffic guidance mainly relies on traditional traffic signal timing control and on-site traffic police command. Traffic signal timing control switches signals according to a pre-set time plan. For example, during the morning rush hour, the green light duration of the main road is appropriately extended to ensure vehicle passage. However, this method lacks accurate perception and flexible response to real-time traffic flow changes, and often there is a situation where the traffic flow in one direction is scarce but the green light is on for a long time, while the traffic in the other direction is congested but has to wait for the green light for a long time. On-site traffic police command is restricted by the distribution of police forces and time, and it is difficult to achieve effective coverage of the entire time period and the entire area. Moreover, in special situations such as bad weather, the work efficiency of traffic police will be greatly affected, resulting in low traffic intelligent guidance efficiency in smart cities. Summary of the Invention

[0004] The present invention provides a traffic intelligent guidance method and system based on a smart city, and its main purpose is to improve the traffic intelligent guidance efficiency in smart cities.

[0005] To achieve the above object, a traffic intelligent guidance method based on a smart city provided by the present invention includes:

[0006] Obtain the urban traffic area that needs traffic guidance, query the traffic facilities in the urban traffic area, determine the traffic facility operation and maintenance regulations corresponding to the traffic facilities, and based on the traffic facility operation and maintenance regulations, formulate the congestion evaluation elements of the urban traffic area;

[0007] Collect the real-time traffic data in the urban traffic area, based on the real-time traffic data, analyze the traffic flow state of the urban traffic area, combine the traffic flow state and the real-time traffic data, calculate the congestion disturbance coefficient corresponding to the congestion evaluation elements, and based on the congestion disturbance coefficient, evaluate the congestion state intensity of the urban traffic area;

[0008] Collect the regional driving trajectory images of vehicles and the holographic images of road scenes in the urban traffic area by using the monitoring devices in the urban traffic area. Analyze the vehicle behavior intention characteristics of the vehicles in the urban traffic area based on the regional driving trajectory images. Analyze the traffic condition matrix of the roads in the urban traffic area based on the holographic images of road scenes. Calculate the traffic efficiency of the roads in the urban traffic area by combining the vehicle behavior intention characteristics and the traffic condition matrix.

[0009] Collect the land use information and real-time event information of the surrounding areas of the urban traffic area. Extract the attraction factor elements from the land use information and the real-time event information. Analyze the traffic pressure conduction degree of the surrounding areas on the urban traffic area based on the attraction factor elements.

[0010] Formulate the traffic intelligent guidance strategy for the urban traffic area based on the congestion situation intensity, the traffic efficiency, and the traffic pressure conduction degree. Execute the traffic guidance work for the urban traffic area based on the traffic intelligent guidance strategy to obtain the traffic guidance result.

[0011] Optionally, the determination of the operation and maintenance regulations corresponding to the traffic facilities includes:

[0012] Collect the infrastructure information of the traffic facilities and calculate the information entropy corresponding to the infrastructure information.

[0013] Extract the key facility information from the infrastructure information based on the information entropy.

[0014] Query the facility operation and maintenance criteria corresponding to the traffic facilities and calculate the matching index between the key facility information and the facility operation and maintenance criteria.

[0015] Determine the operation and maintenance regulations corresponding to the traffic facilities based on the matching index and the key facility information.

[0016] Optionally, the calculation of the matching index between the key facility information and the facility operation and maintenance criteria includes:

[0017] Extract the characteristic characters from the key facility information and the facility operation and maintenance criteria to obtain the facility information characteristic characters and the operation and maintenance criteria characteristic characters.

[0018] Perform semantic parsing processing on the facility information characteristic characters and the operation and maintenance criteria characteristic characters respectively to obtain the first characteristic character semantics and the second characteristic character semantics.

[0019] Perform vectorization processing on the first characteristic character semantics and the second characteristic character semantics to obtain the first semantic vector and the second semantic vector.

[0020] Combining the first semantic vector and the second semantic vector, calculate the fitness index between the critical facility information and the facility operation and maintenance criteria through the following formula:

[0021] ;

[0022] where T represents the fitness index between the critical facility information and the facility operation and maintenance criteria, represents the i-th vector in the first semantic vector, and i represents the serial number of the first semantic vector, represents the j-th vector in the second semantic vector, and j represents the serial number of the second semantic vector, represents taking the maximum vector of the i-th vector and the j-th vector, and q and r respectively represent the quantities corresponding to the first semantic vector and the second semantic vector.

[0023] Optionally, formulating the congestion evaluation elements of the urban traffic area based on the traffic facility operation and maintenance regulations includes:

[0024] Performing element mining processing on the traffic facility operation and maintenance regulations to obtain the key elements of the regulations;

[0025] Constructing an element judgment matrix for the key elements of the regulations, and calculating the element weights corresponding to the key elements of the regulations based on the element judgment matrix;

[0026] Querying the traffic industry standards of the urban traffic area, and formulating the congestion evaluation elements of the urban traffic area in combination with the element weights, the traffic industry standards and the key elements of the regulations.

[0027] Optionally, analyzing the traffic flow state of the urban traffic area based on the real-time traffic data includes:

[0028] Performing data cleaning on the real-time traffic data to obtain target traffic data;

[0029] Extracting multi-dimensional traffic features of the target traffic data, and performing standard processing on the multi-dimensional traffic features to obtain standard traffic features;

[0030] Performing feature dimensionality reduction processing on the standard traffic features to obtain dimensionality-reduced traffic features;

[0031] Performing feature clustering processing on the dimensionality-reduced traffic features to obtain clustered traffic features;

[0032] Performing spatio-temporal pattern analysis on the clustered traffic features to obtain traffic spatio-temporal pattern features;

[0033] Analyzing the traffic flow state of the urban traffic area based on the traffic spatio-temporal pattern features.

[0034] Optionally, calculating a congestion disturbance coefficient corresponding to the congestion evaluation factor by combining the traffic flow state and the real-time traffic data includes:

[0035] Screen out a congestion-related state corresponding to the congestion evaluation factor from the traffic flow state, and calculate a state congestion disturbance coefficient corresponding to the congestion-related state based on the real-time traffic data;

[0036] Based on the real-time traffic data, determine whether there is a traffic accident in the urban traffic area. If there is no traffic accident, calculate a congestion disturbance coefficient corresponding to the congestion evaluation factor by combining the state congestion disturbance coefficient;

[0037] If there is a traffic accident, extract accident feature data related to the traffic accident from the real-time traffic data, and calculate an accident interference degree corresponding to the traffic accident based on the accident feature data;

[0038] Calculate a congestion disturbance coefficient corresponding to the congestion evaluation factor by combining the accident interference degree and the state congestion disturbance coefficient.

[0039] Optionally, calculating the accident interference degree corresponding to the traffic accident based on the accident feature data includes:

[0040] Extract an accident damage record, an accident handling period, and an accident occurrence point from the accident feature data, and evaluate an accident level corresponding to the traffic accident based on the accident damage record;

[0041] Determine an accident duration period corresponding to the traffic accident based on the accident handling period;

[0042] Calculate a distance value between the traffic accident and an adjacent passing road in combination with the accident occurrence point;

[0043] Combining the distance value, the accident duration period, and the accident level, the accident interference degree corresponding to the traffic accident can be calculated by the following formula:

[0044] ;

[0045] where D represents the accident interference degree corresponding to the traffic accident, E represents the accident level, d represents the distance value, t represents the accident duration period, represents a distance influence factor.

[0046] Optionally, analyzing the vehicle behavior intention characteristics of vehicles in the urban traffic area based on the regional driving trajectory image includes:

[0047] Perform image preprocessing on the regional driving trajectory image to obtain an optimized driving trajectory image;

[0048] Perform feature extraction processing on the optimized driving trajectory image to obtain a feature driving trajectory image;

[0049] Identify the vehicle movement trajectory in the feature driving trajectory image, and calculate the trajectory change rate of the vehicle in the urban traffic area based on the vehicle movement trajectory;

[0050] Extract the trajectory point information corresponding to the feature driving trajectory image, and calculate the behavior intention parameters of the vehicle in the urban traffic area based on the trajectory point information;

[0051] Generate the vehicle behavior intention feature of the vehicle in the urban traffic area based on the trajectory change rate and the behavior intention parameters;

[0052] Optionally, the calculating the trajectory change rate of the vehicle in the urban traffic area based on the vehicle movement trajectory includes:

[0053] Obtain the standard movement trajectory of the vehicle in the urban traffic area, and perform curve fitting processing on the vehicle movement trajectory and the standard movement trajectory respectively to obtain a first trajectory curve and a second trajectory curve;

[0054] Based on the first trajectory curve, calculate the trajectory curvature sequence corresponding to the vehicle movement trajectory to obtain a first trajectory curvature sequence;

[0055] Based on the second trajectory curve, calculate the trajectory curvature sequence corresponding to the standard movement trajectory to obtain a second trajectory curvature sequence;

[0056] And calculate the trajectory lengths corresponding to the vehicle movement trajectory and the standard movement trajectory to obtain a first trajectory length and a second trajectory length;

[0057] Combine the first trajectory curvature sequence, the second trajectory curvature sequence, the first trajectory length and the second trajectory length, and calculate the trajectory change rate of the vehicle in the urban traffic area through the following formula:

[0058] ;

[0059] where F represents the trajectory change rate of the vehicle in the urban traffic area, represents the f-th curvature value in the first trajectory curvature sequence, represents the f-th curvature value in the second trajectory curvature sequence, represents the first trajectory length, represents the second trajectory length, f represents the serial number in the trajectory curvature sequence, and u represents the length of the trajectory curvature sequence.

[0060] To solve the above problems, the present invention also provides a traffic intelligent guidance system based on a smart city, and the system includes:

[0061] A congestion evaluation factor formulation module, configured to obtain an urban traffic area that needs traffic guidance, query traffic facilities within the urban traffic area, determine traffic facility operation and maintenance regulations corresponding to the traffic facilities, and formulate congestion evaluation factors for the urban traffic area based on the traffic facility operation and maintenance regulations;

[0062] A congestion situation intensity evaluation module, configured to collect real-time traffic data within the urban traffic area, analyze the traffic flow state of the urban traffic area based on the real-time traffic data, combine the traffic flow state and the real-time traffic data, calculate a congestion disturbance coefficient corresponding to the congestion evaluation factor, and evaluate the congestion situation intensity of the urban traffic area based on the congestion disturbance coefficient;

[0063] A traffic efficiency calculation module, configured to use monitoring devices in the urban traffic area to collect regional driving trajectory images of vehicles and holographic road scene images, analyze vehicle behavior intention characteristics of vehicles within the urban traffic area based on the regional driving trajectory images, analyze a traffic condition matrix of roads within the urban traffic area based on the holographic road scene images, and calculate the traffic efficiency of roads within the urban traffic area by combining the vehicle behavior intention characteristics and the traffic condition matrix;

[0064] A traffic pressure conduction degree analysis module, configured to collect land use information and real-time event information of the surrounding area of the urban traffic area, extract attraction factor elements from the land use information and the real-time event information, and analyze the traffic pressure conduction degree of the surrounding area on the urban traffic area based on the attraction factor elements;

[0065] A traffic guidance module, configured to formulate a traffic intelligent guidance strategy for the urban traffic area based on the congestion situation intensity, the traffic efficiency, and the traffic pressure conduction degree, and execute traffic guidance work for the urban traffic area based on the traffic intelligent guidance strategy to obtain a traffic guidance result.

[0066] Compared with the problems described in the background art, by determining the operation and maintenance regulations corresponding to the traffic facilities, the present invention can obtain scientific and highly targeted maintenance and management guidelines for the traffic facilities under different conditions, thereby providing strong support for the formulation of congestion evaluation factors in the subsequent urban traffic area. Further, by analyzing the traffic flow state of the urban traffic area based on the real-time traffic data, the present invention can obtain the operation mode and change law of the traffic in the urban traffic area in different time and space dimensions, providing an important basis for the subsequent scientific calculation of the congestion perturbation coefficient. Further, by analyzing the vehicle behavior intention characteristics of the vehicles in the urban traffic area based on the regional driving trajectory images, the present invention can better grasp the driving purpose and trend of the vehicles in the traffic scenario, providing a basis for the subsequent calculation of the road traffic efficiency. Further, by collecting the land use information and real-time event information of the surrounding area, extracting the attraction factor therein, and analyzing the traffic pressure conduction degree based on this, the present invention can comprehensively and accurately grasp the traffic impact of the surrounding area on the urban traffic area, providing a strong basis for urban traffic planning and management. By comprehensively considering the congestion situation intensity, the traffic efficiency, and the traffic pressure conduction degree to formulate a traffic intelligent guidance strategy, the present invention can grasp the urban traffic situation from multiple dimensions, greatly improving the scientificity and pertinence of the strategy formulation. Furthermore, based on this strategy, traffic guidance work is carried out to improve the traffic intelligent guidance efficiency of the smart city. Therefore, the traffic intelligent guidance method and system based on the smart city provided by the embodiments of the present invention can improve the traffic intelligent guidance efficiency of the smart city. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 It is a schematic flow chart of a traffic intelligent guidance method based on a smart city provided by an embodiment of the present invention;

[0068] Figure 2 It is a schematic diagram of a module for implementing the traffic intelligent guidance method based on a smart city provided by an embodiment of the present invention.

[0069] The realization, functional features, and advantages of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0070] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0071] An embodiment of the present application provides a traffic intelligent guidance method based on a smart city. The execution subject of the traffic intelligent guidance method based on a smart city includes, but is not limited to, at least one of electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided in the embodiment of the present application. In other words, the traffic intelligent guidance method based on a smart city can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to: a single server, a server cluster, a cloud server, or a cloud server cluster, etc.

[0072] Embodiment 1:

[0073] Referring to Figure 1 As shown, it is a schematic flowchart of a traffic intelligent guidance method based on a smart city provided by an embodiment of the present invention. In this embodiment, the traffic intelligent guidance method based on a smart city includes:

[0074] S1. Obtain the urban traffic area that needs traffic guidance, query the traffic facilities in the urban traffic area, determine the traffic facility operation and maintenance regulations corresponding to the traffic facilities, and based on the traffic facility operation and maintenance regulations, formulate the congestion evaluation elements of the urban traffic area.

[0075] By determining the traffic facility operation and maintenance regulations corresponding to the traffic facilities, the present invention can obtain scientific and highly targeted maintenance and management guidelines for the traffic facilities in different situations, thereby providing strong support for the subsequent formulation of the congestion evaluation elements of the urban traffic area.

[0076] It should be explained that the urban traffic area that needs traffic guidance refers to an area where traffic congestion frequently occurs due to reasons such as excessive traffic flow, road construction, emergencies, etc.; the traffic facilities include, but are not limited to, roads, bridges, traffic lights, bus stops, etc.; the traffic facility operation and maintenance regulations refer to the maintenance, management rules and standards formulated to ensure the normal operation of traffic facilities.

[0077] Specifically, the determination of the traffic facility operation and maintenance regulations corresponding to the traffic facilities includes:

[0078] Collect the infrastructure information of the traffic facilities, and calculate the information entropy corresponding to the infrastructure information;

[0079] Based on the information entropy, extract the key facility information from the infrastructure information;

[0080] Query the facility operation and maintenance guidelines corresponding to the traffic facilities, and calculate the fit index between the key facility information and the facility operation and maintenance guidelines;

[0081] Based on the fit index and the key facility information, determine the traffic facility operation and maintenance regulations corresponding to the traffic facilities.

[0082] Among them, the infrastructure information is the multi-dimensional basic situation and operation status information of the transportation facilities, such as the construction time, service life, geographical location, daily maintenance records, failure frequency, and surrounding traffic flow data of the transportation facilities. The information entropy is a quantitative index used to measure the uncertainty or degree of chaos of the infrastructure information. The key facility information is the information that has a greater impact on the normal operation of the transportation facilities and contains more key content from the infrastructure information. The facility operation and maintenance criteria are the maintenance and management rules formulated by transportation experts for different types of transportation facilities by referring to advanced domestic and foreign standards and combining local actual situations. The fitness index represents the matching degree of each feature in the key facility information and the facility operation and maintenance criteria through comprehensive consideration.

[0083] Furthermore, the infrastructure information of the transportation facilities can be collected through Internet of Things devices, and the information entropy corresponding to the infrastructure information can be calculated through the Shannon entropy algorithm; the information entropy is compared with a preset threshold, and when the information entropy is greater than the preset threshold, the key facility information in the infrastructure information is extracted; the facility operation and maintenance criteria corresponding to the transportation facilities can be queried through the standard document library formulated by the local transportation department; based on the fitness index and the key facility information, by cross-comparing the key facility information with a high fitness index with the facility operation and maintenance criteria, combined with expert experience, targeted analysis is carried out on the unique or abnormal data in the key facility information, and referring to the successful operation and maintenance cases of similar transportation facilities in the past, the transportation facility operation and maintenance regulations corresponding to the transportation facilities are finally determined.

[0084] Furthermore, as an optional embodiment of the present invention, calculating the fitness index between the key facility information and the facility operation and maintenance criteria includes:

[0085] Extract the characteristic characters in the key facility information and the facility operation and maintenance criteria to obtain the facility information characteristic characters and the operation and maintenance criterion characteristic characters;

[0086] Semantic parsing processing is respectively performed on the facility information characteristic characters and the operation and maintenance criterion characteristic characters to obtain the first characteristic character semantics and the second characteristic character semantics;

[0087] Vectorization processing is performed on the first characteristic character semantics and the second characteristic character semantics to obtain the first semantic vector and the second semantic vector;

[0088] Combining the first semantic vector and the second semantic vector, calculate the fitness index between the key facility information and the facility operation and maintenance criteria through the following formula:

[0089] ;

[0090] Among them, T represents the matching index between the critical facility information and the facility operation and maintenance guidelines. represents the i-th vector in the first semantic vector, where i represents the serial number of the first semantic vector. represents the j-th vector in the second semantic vector, where j represents the serial number of the second semantic vector. represents taking the maximum vector between the i-th vector and the j-th vector, and q and r respectively represent the quantities corresponding to the first semantic vector and the second semantic vector.

[0091] It should be explained that the facility information feature characters and the operation and maintenance guideline feature characters are respectively the literal symbols or codes used to describe and characterize their features in the critical facility information and the facility operation and maintenance guidelines. These characters are the literal expression forms of the key features extracted from the critical facility information and the facility operation and maintenance guidelines; the first feature character semantics and the second feature character semantics are respectively the meaning explanations corresponding to the facility information feature characters and the operation and maintenance guideline feature characters, that is, the description of the actual meanings expressed by these characters; the first semantic vector and the second semantic vector are respectively the numerical vector representations corresponding to the first feature character semantics and the second feature character semantics. By mathematically quantifying the character semantics and presenting them in the form of vectors, it is convenient for the computer to perform calculations and analyses, so as to be used for subsequent operations such as calculating the matching index.

[0092] Furthermore, the feature characters in the critical facility information and the facility operation and maintenance guidelines can be extracted through the TextRank algorithm to obtain the facility information feature characters and the operation and maintenance guideline feature characters; the facility information feature characters and the operation and maintenance guideline feature characters can be semantically parsed respectively through semantic analysis to obtain the first feature character semantics and the second feature character semantics; the first feature character semantics and the second feature character semantics can be vectorized through the Word2Vec model to obtain the first semantic vector and the second semantic vector.

[0093] Through the present invention, by formulating the congestion evaluation elements of the urban traffic area based on the traffic facility operation and maintenance regulations, the internal relationship between the operation of the traffic facilities and traffic congestion can be accurately grasped, providing a quantitative basis for scientifically evaluating the traffic congestion situation. It should be noted that the congestion evaluation elements are the key indicators used to measure the congestion degree of the urban traffic area.

[0094] Specifically, formulating the congestion evaluation elements of the urban traffic area based on the traffic facility operation and maintenance regulations includes:

[0095] Performing element mining processing on the traffic facility operation and maintenance regulations to obtain the key elements of the regulations.

[0096] Construct an element judgment matrix for the key elements of the specification. Based on the element judgment matrix, calculate the element weights corresponding to the key elements of the specification.

[0097] Query the traffic industry standards for the urban traffic area. Combine the element weights, the traffic industry standards, and the key elements of the specification to formulate the congestion evaluation elements for the urban traffic area.

[0098] It should be explained that the key elements of the specification are the important parts in the traffic facility operation and maintenance specification that have a great impact on traffic congestion; the element judgment matrix is a square matrix used to compare which key elements of the specification are more important; the element weight represents the numerical value indicating how important each key element of the specification is; the traffic industry standards are the normative guidelines followed by the urban traffic area.

[0099] Furthermore, the key elements of the specification can be obtained by performing element mining on the traffic facility operation and maintenance specification through natural language processing technology combined with a domain knowledge graph; the element judgment matrix of the key elements of the specification can be constructed through matrix functions, such as the zero matrix function. Based on the element judgment matrix, use the analytic hierarchy process (AHP) and professional matrix operation methods to calculate the element weights corresponding to the key elements of the specification.

[0100] The traffic industry standards for the urban traffic area can be queried through the transportation standardization information platform. Combine the element weights, the traffic industry standards, and the key elements of the specification to formulate the congestion evaluation elements for the urban traffic area. For example, taking the key element of road maintenance frequency as an example, judge its importance in congestion evaluation based on its element weight; refer to the specifications for road maintenance frequency in the traffic industry standards and set a reasonable numerical range; comprehensively consider these and take road maintenance frequency as one of the congestion evaluation elements for the urban traffic area to scientifically formulate an evaluation system.

[0101] S2. Collect the real-time traffic data in the urban traffic area. Based on the real-time traffic data, analyze the traffic flow state of the urban traffic area. Combine the traffic flow state and the real-time traffic data to calculate the congestion disturbance coefficient corresponding to the congestion evaluation elements. Based on the congestion disturbance coefficient, evaluate the congestion state intensity of the urban traffic area.

[0102] Based on the real-time traffic data, by analyzing the traffic flow state of the urban traffic area, the operation mode and change law of the traffic in the urban traffic area in different time and space dimensions can be obtained, which provides an important basis for subsequent scientific calculation of the congestion perturbation coefficient. It should be explained that the real-time traffic data covers multi-dimensional information such as traffic flow, vehicle speed, vehicle driving trajectory, and traffic signal status on the road. The traffic flow state is a comprehensive description of different traffic flow patterns in the urban traffic area in the time and space dimensions, reflecting the changes of traffic parameters such as traffic flow, speed, and density in different regions and time periods, and is an important basis for measuring the operation state of urban traffic. Further, the real-time traffic data in the urban traffic area can be obtained by real-time collection through devices such as geomagnetic sensors, cameras, floating cars, and intelligent traffic signal systems distributed in the urban traffic area.

[0103] Specifically, the analysis of the traffic flow state of the urban traffic area based on the real-time traffic data includes:

[0104] Performing data cleaning on the real-time traffic data to obtain target traffic data;

[0105] Extracting multi-dimensional traffic features of the target traffic data, and performing standard processing on the multi-dimensional traffic features to obtain standard traffic features;

[0106] Performing feature dimensionality reduction processing on the standard traffic features to obtain reduced-dimensional traffic features;

[0107] Performing feature clustering processing on the reduced-dimensional traffic features to obtain clustered traffic features;

[0108] Performing spatio-temporal pattern analysis on the clustered traffic features to obtain traffic spatio-temporal pattern features;

[0109] Based on the traffic spatio-temporal pattern features, analyzing the traffic flow state of the urban traffic area.

[0110] It should be explained that the target traffic data is the available data obtained after data cleaning operations such as removing outliers and filling missing values from the real-time traffic data; the multi-dimensional traffic features are multiple-dimensional information reflecting traffic operation conditions extracted from the target traffic data, such as traffic flow, vehicle speed, traffic density, etc.; the standard traffic features are the data representation forms after eliminating the dimension differences of the multi-dimensional traffic features; the reduced-dimensional traffic features are the standard traffic features with the dimension reduced while retaining the main information; the clustered traffic features are the reduced-dimensional traffic features divided into different categories according to the similarity of data; the traffic spatio-temporal pattern features are the relevant features about the distribution and change law of traffic flow in the time and space dimensions in the clustered traffic features.

[0111] Furthermore, the real-time traffic data can be cleaned by the box plot method to obtain target traffic data; the multi-dimensional traffic features of the target traffic data can be extracted by a trained convolutional neural network, and the multi-dimensional traffic features can be standardized by the maximum-minimum normalization method to obtain standardized traffic features; the dimensionality of the standardized traffic features can be reduced by the principal component analysis (PCA) method to obtain reduced-dimensional traffic features; the reduced-dimensional traffic features can be clustered by the K-Means clustering algorithm to obtain clustered traffic features; the spatio-temporal pattern analysis of the clustered traffic features can be performed by calculating the autocorrelation coefficient of the time series and the Moran index of the space, etc., to obtain traffic spatio-temporal pattern features. For example, in the time dimension, calculating the autocorrelation coefficient of the time series of the clustered traffic features can reveal the similarity and variation law of traffic patterns at different times. For instance, it is found that the autocorrelation coefficient of the evening peak congestion pattern is relatively high within several consecutive hours, indicating the temporal continuity of congestion. In the space dimension, the correlation of the clustered traffic features in the spatial distribution is analyzed by the Moran index. If the Moran index is positive, it means that similar traffic patterns are spatially aggregated. For example, congestion clusters frequently appear in certain areas, revealing the spatial aggregation characteristics of congestion areas. By synthesizing these analyses, traffic spatio-temporal pattern features can be obtained. Based on the traffic spatio-temporal pattern features, the traffic flow state of the urban traffic area is analyzed. First, based on the traffic spatio-temporal pattern features, from the time perspective, the variation law of traffic flow at different times is analyzed, such as the start, peak, and duration of morning peak congestion. Then, from the space dimension, the distribution, aggregation, and diffusion direction of congestion areas are examined. By synthesizing spatio-temporal information, the dynamic change state of traffic flow in the urban traffic area is comprehensively analyzed, thereby obtaining the traffic flow state.

[0112] By combining the traffic flow state and the real-time traffic data, the present invention calculates the congestion perturbation coefficient corresponding to the congestion evaluation factor, which can quantitatively evaluate the influence degree of the congestion evaluation factor on traffic congestion, and provides a key index for accurately evaluating the congestion state intensity of the urban traffic area. It should be noted that the congestion perturbation coefficient is a quantitative index used to represent the influence degree of each congestion evaluation factor on the traffic congestion condition. The larger its value, the more significant the influence of the factor on traffic congestion.

[0113] Specifically, the combination of the traffic flow state and the real-time traffic data to calculate the congestion perturbation coefficient corresponding to the congestion evaluation factor includes:

[0114] Screen out the congestion-related state corresponding to the congestion evaluation factor from the traffic flow state, and calculate the state congestion perturbation coefficient corresponding to the congestion-related state based on the real-time traffic data;

[0115] Based on the real-time traffic data, determine whether there is a traffic accident in the urban traffic area. If there is no traffic accident, then combine the dynamic congestion disturbance coefficient to calculate the congestion disturbance coefficient corresponding to the congestion evaluation factor;

[0116] If there is a traffic accident, extract the accident characteristic data related to the traffic accident from the real-time traffic data, and based on the accident characteristic data, calculate the accident interference degree corresponding to the traffic accident;

[0117] Combine the accident interference degree and the dynamic congestion disturbance coefficient to calculate the congestion disturbance coefficient corresponding to the congestion evaluation factor.

[0118] It should be explained that the congestion-related dynamic state is the specific state manifestation in the traffic flow dynamic state that has a direct association and influence on the congestion evaluation factor. The dynamic congestion disturbance coefficient is a numerical index corresponding to the congestion-related dynamic state and used to quantify the degree of disturbance of this state to traffic congestion. The accident characteristic data is the specific information in the real-time traffic data related to traffic accidents that can reflect aspects such as the basic situation, scale, severity, and handling situation of the accident. The accident interference degree represents the comprehensive degree measure of the interference caused by the traffic accident to the normal traffic order.

[0119] Furthermore, by setting a threshold range closely related to the congestion evaluation factors and combining the method of data trend analysis, the congestion-related states corresponding to the congestion evaluation factors can be screened out from the traffic flow states. Taking traffic flow as an example, first determine the normal flow range based on historical data. For example, during the morning rush hour on weekdays, 500 - 800 vehicles per hour on a certain section of the road is normal. If the current flow exceeds this range, then combined with the rising or falling trend of the flow over a period of time, determine whether it is related to congestion. If the flow continuously rises and exceeds the upper limit in multiple consecutive monitoring periods, it will be included in the congestion-related states; based on the real-time traffic data, calculate the state congestion disturbance coefficient corresponding to the congestion-related state. For example, for the traffic flow state, the coefficient can be calculated according to the deviation degree of the current flow from the historical mean and the impact degree of this deviation on congestion. According to past experience, when this deviation has a greater impact on congestion, for example, every time it exceeds the historical mean by 10%, the degree of congestion aggravation is 0.1. Then the state congestion disturbance coefficient of the traffic flow state is calculated as: 0.1×(current flow - historical mean) / historical mean; based on the real-time traffic data, determine whether there is a traffic accident in the urban traffic area. If there is no traffic accident, sum up the state congestion disturbance coefficients to obtain the congestion disturbance coefficient corresponding to the congestion evaluation factor; if there is a traffic accident, specific data extraction rules can be set, and based on key identifiers such as accident timestamps, geographical location information, and accident type markings, accurately query and screen relevant accident characteristic data in the real-time traffic database table from the real-time traffic data; add the accident interference degree and the state congestion disturbance coefficient to obtain the congestion disturbance coefficient corresponding to the congestion evaluation factor.

[0120] Furthermore, as an optional embodiment of the present invention, calculating the accident interference degree corresponding to the traffic accident based on the accident characteristic data includes:

[0121] Extract the accident damage record, accident handling period, and accident occurrence point in the accident characteristic data, and evaluate the accident level corresponding to the traffic accident based on the accident damage record;

[0122] Determine the accident duration period corresponding to the traffic accident based on the accident handling period;

[0123] Combine the accident occurrence point to calculate the interval distance value between the traffic accident and the adjacent passing road;

[0124] Combining the interval distance value, the accident duration period, and the accident level, the accident interference degree corresponding to the traffic accident can be calculated through the following formula:

[0125] ;

[0126] Among them, D represents the accident interference degree corresponding to the traffic accident, E represents the accident level, d represents the interval distance value, and t represents the accident duration period. represents the distance influence factor.

[0127] It should be explained that the accident damage record is a specific record of the damage-related information such as the vehicle damage degree, casualty situation, and property loss in the accident characteristic data. The accident handling period is the time length experienced from the accident occurrence to the completion of the accident handling (including on-site cleaning, liability determination, claims settlement, etc.) in the accident characteristic data. The accident level represents the level divided according to the accident severity (such as casualties, property loss, social impact, etc.) corresponding to the traffic accident. The interval distance value represents the spatial straight-line distance or the actual passable distance between the traffic accident and the adjacent passing road. The distance influence factor is a quantitative parameter that comprehensively considers the distance between the accident occurrence point and the adjacent passing road and affects aspects such as the interference degree of the accident on the adjacent road traffic and the rescue difficulty.

[0128] Furthermore, the accident damage record, accident handling period, and accident occurrence point in the accident characteristic data can be extracted through an extraction function, and the extraction function is compiled by a programming language. Based on the accident damage record, the accident level corresponding to the traffic accident is evaluated. For example, according to the vehicle damage condition, the number and severity of casualties, and the amount of property loss in the accident damage record, and comparing with the pre-set accident level division standard, such as when the number of seriously injured people reaches 3 or more or the property loss exceeds 500,000, it is determined as a major accident level, so as to evaluate the accident level corresponding to the traffic accident, from 1 to 5 levels, 1 being minor and 5 being serious. The pre-set accident level division standard usually divides traffic accidents into different levels, such as minor accidents, general accidents, major accidents, and extremely serious accidents, etc., based on multiple factors such as the casualty situation (such as the number of deaths, seriously injured, and slightly injured), the amount of property loss (the amount of direct economic loss), and the social impact degree of the accident (such as whether it causes long-term traffic congestion, whether it affects the operation of important areas, etc.).

[0129] Based on the accident handling cycle, considering the accuracy of the accident occurrence time record and the integrity of the time connection in the intermediate links, using the time difference calculation method and combining with the time definition rules of the traffic management department for the accident handling process, determine the accident duration corresponding to the traffic accident. For example, obtain the time when the accident was first recorded, and then clarify the time when the last handling link is completed from the accident handling cycle, and calculate the difference between the two to obtain the preliminary duration. Subsequently, according to the regulations of the traffic management department, adjust the reasonable delay time caused by objective factors such as waiting for rescue and weather during the intermediate period, and finally determine the accident duration; combine the accident occurrence point, use a high-precision geographic information system (GIS) to obtain the precise coordinates of the accident occurrence point, and at the same time determine the center line or boundary coordinates of the adjacent passing roads, and calculate the interval distance value between the traffic accident and the adjacent passing roads through a spatial distance calculation algorithm (such as the Euclidean distance formula or the shortest path algorithm considering the actual road network).

[0130] Through the congestion perturbation coefficient, the present invention evaluates the congestion state intensity of the urban traffic area, and can comprehensively and accurately evaluate the severity of urban traffic congestion. It should be explained that the congestion state intensity is a comprehensive evaluation of the overall situation of urban traffic congestion, reflecting the traffic congestion degree under the joint action of multiple congestion evaluation factors. Further, based on the congestion perturbation coefficient, evaluate the congestion state intensity of the urban traffic area, and judge the congestion state intensity according to the size of the congestion perturbation coefficient. The larger the coefficient, the greater the impact of the evaluation factor on congestion, and the higher the congestion state intensity of the urban traffic area.

[0131] S3. Use the monitoring devices in the urban traffic area to collect the regional driving trajectory images of vehicles and the holographic images of road scenes. Based on the regional driving trajectory images, analyze the vehicle behavior intention characteristics of the vehicles in the urban traffic area. Based on the holographic images of road scenes, analyze the traffic condition matrix of the roads in the urban traffic area. Combine the vehicle behavior intention characteristics and the traffic condition matrix to calculate the traffic efficiency of the roads in the urban traffic area.

[0132] By analyzing the vehicle behavior intention characteristics of the vehicles in the urban traffic area based on the regional driving trajectory images, the present invention can better grasp the driving purpose and trend of vehicles in the traffic scene, and provide a basis for calculating the traffic efficiency of the roads subsequently.

[0133] It should be noted that the regional driving trajectory image is the vehicle driving path image collected by surveillance cameras set at various key positions in the urban traffic area. The road scene holographic image is the omnidirectional image information of the road and its surrounding environment, including information such as road conditions, signal light status, number of lanes, traffic signs, etc. The vehicle behavior intention feature reflects the driving intention of the vehicle, such as behavior patterns of accelerating, decelerating, turning, changing lanes, stopping, etc. The traffic condition matrix describes the traffic conditions of the road, including information such as road width, lane availability, road congestion level, signal light cycle, etc. Further, the monitoring devices in the urban traffic area can adopt high-definition cameras and the sensor network of the intelligent transportation system to ensure the quality and accuracy of the collected image and video information.

[0134] Specifically, analyzing the vehicle behavior intention feature of the vehicles in the urban traffic area based on the regional driving trajectory image includes:

[0135] Performing image preprocessing on the regional driving trajectory image to obtain an optimized driving trajectory image;

[0136] Performing feature extraction processing on the optimized driving trajectory image to obtain a feature driving trajectory image;

[0137] Identifying the vehicle movement trajectory in the feature driving trajectory image, and calculating the trajectory change rate of the vehicles in the urban traffic area based on the vehicle movement trajectory;

[0138] Extracting the trajectory point information corresponding to the feature driving trajectory image, and calculating the behavior intention parameters of the vehicles in the urban traffic area based on the trajectory point information;

[0139] Generating the vehicle behavior intention feature of the vehicles in the urban traffic area based on the trajectory change rate and the behavior intention parameters.

[0140] It should be noted that the optimized driving trajectory image is the image obtained after the regional driving trajectory image undergoes image preprocessing (such as removing noise, adjusting brightness and contrast, etc.). The feature driving trajectory image is the image from which key movement information is extracted from the optimized driving trajectory image. The vehicle movement trajectory is the route information of the vehicle driving on the road. The trajectory change rate represents the degree of change of the vehicle movement trajectory, such as the frequency of turning, the frequency of accelerating and decelerating, etc. The trajectory point information is the information such as the position and speed of the vehicle at different times included in the feature driving trajectory image. The behavior intention parameter is a quantitative index describing the vehicle behavior intention, such as the number of acceleration times, deceleration times, lane change times of the vehicle within a certain period of time, etc.

[0141] Further, the image preprocessing of the regional driving trajectory image can be achieved through image filtering algorithms (such as Gaussian filtering, median filtering) and histogram equalization methods; the feature extraction process of the optimized driving trajectory image can be used to track the vehicle's motion trajectory by the optical flow method; the recognition of the vehicle's motion trajectory in the feature driving trajectory image can be achieved through the Hough transform or object tracking algorithms based on deep learning (such as YOLO, DeepSORT); the extraction of the trajectory point information corresponding to the feature driving trajectory image can be obtained by analyzing the discrete points of the motion trajectory and calculating parameters such as the speed and acceleration of adjacent trajectory points; based on the trajectory point information, the behavior intention parameters of the vehicle in the urban traffic area are calculated, such as the number of times the vehicle accelerates beyond a certain threshold within one minute as the acceleration behavior intention parameter, and the same for deceleration. The lane change behavior can be judged by the change angle of the trajectory line direction, so as to obtain the behavior intention parameters; the trajectory change rate and the behavior intention parameters are used as the vehicle behavior intention features of the vehicle in the urban traffic area.

[0142] Further, as an optional embodiment of the present invention, calculating the trajectory change rate of the vehicle in the urban traffic area based on the vehicle's motion trajectory includes:

[0143] Obtain the standard motion trajectory of the vehicle in the urban traffic area, and perform curve fitting processing on the vehicle's motion trajectory and the standard motion trajectory respectively to obtain the first trajectory curve and the second trajectory curve;

[0144] Based on the first trajectory curve, calculate the trajectory curvature sequence corresponding to the vehicle's motion trajectory to obtain the first trajectory curvature sequence;

[0145] Based on the second trajectory curve, calculate the trajectory curvature sequence corresponding to the standard motion trajectory to obtain the second trajectory curvature sequence;

[0146] And calculate the trajectory lengths corresponding to the vehicle's motion trajectory and the standard motion trajectory to obtain the first trajectory length and the second trajectory length;

[0147] Combining the first trajectory curvature sequence, the second trajectory curvature sequence, the first trajectory length and the second trajectory length, calculate the trajectory change rate of the vehicle in the urban traffic area through the following formula:

[0148] ;

[0149] where F represents the trajectory change rate of the vehicle in the urban traffic area, represents the f - th curvature value in the first trajectory curvature sequence, represents the f - th curvature value in the second trajectory curvature sequence, represents the first trajectory length, represents the second trajectory length, f represents the serial number in the trajectory curvature sequence, and u represents the length of the trajectory curvature sequence.

[0150] It should be explained that the standard motion trajectory is the expected driving trajectory of the vehicle under ideal traffic conditions. The first trajectory curve and the second trajectory curve are the mathematical representations after curve fitting of the actual and standard trajectories. The first trajectory curvature sequence and the second trajectory curvature sequence are respectively the curvature change sequences of the vehicle motion trajectory and the standard motion trajectory. The first trajectory length and the second trajectory length are respectively the lengths of the vehicle motion trajectory and the standard motion trajectory.

[0151] Furthermore, the acquisition of the standard motion trajectory of the vehicle in the urban traffic area can be simulated through the historical data and traffic rules of the traffic planning department. The curve fitting process of the vehicle motion trajectory and the standard motion trajectory can use polynomial fitting or spline curve fitting methods. Calculate the curvature of the first trajectory curve at different positions to obtain the first trajectory curvature sequence. The calculation of the second trajectory curvature sequence is the same. The calculation of the trajectory lengths corresponding to the vehicle motion trajectory and the standard motion trajectory can be obtained by the integral method, applying the curve length integral formula to the corresponding curves.

[0152] Through the present invention, by analyzing the traffic condition matrix of roads in the urban traffic area based on the holographic image of the road scene, the traffic conditions of the roads can be comprehensively evaluated. And by combining the vehicle behavior intention characteristics and the traffic condition matrix, the traffic efficiency of roads in the urban traffic area can be calculated, so as to evaluate the traffic efficiency of roads and provide a reference for traffic management and planning. It should be explained that the traffic condition matrix is a multi-dimensional index set reflecting the road traffic conditions, including the physical conditions of the road (such as the slope of the road, the road surface condition) and the traffic facility conditions (such as the control strategy of traffic lights, the setting of traffic signs), etc. The traffic efficiency represents the passing ability and smoothness of vehicles on the road within a certain period of time. Further, based on the holographic image of the road scene, the traffic condition matrix of roads in the urban traffic area is analyzed. For example, the flatness of the road surface is evaluated by analyzing the texture of the road surface in the image, the control effect of traffic lights is judged according to the flashing frequency and duration of traffic lights, and the availability of lanes is judged by the clarity and integrity of lane lines, etc., so as to obtain the traffic condition matrix; the steps of calculating the traffic efficiency of roads in the urban traffic area by combining the vehicle behavior intention characteristics and the traffic condition matrix are as follows: first, calculate the condition difference degree between the standard traffic condition matrix and the current traffic condition matrix, and the intention difference degree between the standard vehicle behavior intention characteristics and the current vehicle behavior intention characteristics, respectively assign weights to the condition difference degree and the intention difference degree, multiply the condition difference degree and the intention difference degree by the corresponding weights and sum them up to calculate the traffic efficiency of roads in the urban traffic area. The weight assignment is based on traffic theory and actual experience to determine the influence weights of the condition difference degree and the intention difference degree on the traffic efficiency, such as through the expert scoring method or historical data statistical analysis.

[0153] S4. Collect the land use information and real-time event information of the surrounding area of the urban traffic area, extract the attraction element factors in the land use information and the real-time event information, and analyze the traffic pressure conduction degree of the surrounding area on the urban traffic area based on the attraction element factors.

[0154] The present invention collects land use information and real-time event information in the surrounding area, extracts the attraction factor elements therefrom, and analyzes the traffic pressure conduction degree based on this, so as to comprehensively and accurately grasp the traffic impact of the surrounding area on the urban traffic area, providing a strong basis for urban traffic planning and management. It should be explained that the land use information includes the land use types in the surrounding area, such as commercial land, residential land, industrial land, etc.; the real-time event information covers emergencies, such as traffic accidents, large-scale event holding, etc.; the attraction factor elements refer to those key factors that can attract the flow of people or vehicles, such as the business hours of commercial centers, the holding time and scale of events, etc. The traffic pressure conduction degree refers to the quantitative degree index of the traffic pressure exerted on different time periods and different sections of the urban traffic area due to the attraction factor elements generated by land use, real-time events, etc. in the surrounding area, resulting in the flow of people and vehicles in the urban traffic system. Further, the collection of land use information can be achieved through the urban planning database and the Geographic Information System (GIS), and the real-time event information is collected by means of the traffic monitoring system, social media, and news reports. The steps for extracting the attraction factor elements from the land use information and the real-time event information are as follows: classify and process the land use information, divide it according to categories such as commerce, residence, industry, public service, etc., and for different types of land use, extract the corresponding attraction factor elements. For example, for commercial land, extract its business hours, peak business activity periods, etc.; for residential land, extract information such as the morning and evening peak periods of residents' travel. Screen and sort out the real-time event information, identify events with traffic impact, such as traffic accidents, and extract the accident occurrence time, location, estimated handling duration, etc.; for large-scale events, extract information such as the start and end times of the event, the number of participants, and the traffic conditions around the event venue. Integrate the various types of attraction factor elements extracted from the land use information and the real-time event information to obtain the attraction factor elements.

[0155] Further, the steps for analyzing the traffic pressure conduction degree of the surrounding area on the urban traffic area based on the attraction factor elements are as follows: construct a traffic pressure conduction model, which comprehensively considers factors such as the type, intensity of the attraction factor elements, and the distance from the urban traffic area. For example, a large commercial center that is relatively close to the urban traffic area and has concentrated business hours has a large intensity of attraction factor elements and has a significant impact on traffic pressure conduction; while an event that is far away and has a small activity scale has a relatively small impact.

[0156] Use historical traffic data and geographical information to calibrate and verify the parameters of the constructed model. By analyzing data such as traffic flow changes and congestion conditions under the action of similar attraction factor elements in history, adjust the model parameters to ensure that the model can accurately reflect the traffic pressure conduction law.

[0157] Input the extracted attraction factor into the calibrated model to calculate the traffic pressure conduction degree values of the surrounding area on the urban traffic area at different time periods and different road sections. For example, through model calculation, it is obtained that during the business hours of the commercial center, the traffic pressure conduction degree of a certain connecting road in the surrounding area is 0.8 (the value range is 0 - 1, and the larger the value, the greater the traffic pressure conduction), indicating that this road bears a relatively large traffic pressure due to the attraction factor of the commercial center during this period.

[0158] It should be explained that the traffic pressure conduction model is the core tool for analyzing the traffic pressure conduction degree. Considering multiple factors comprehensively can make the model more in line with the actual situation; calibrating and validating the model can ensure its accuracy and reliability; the traffic pressure conduction degree values calculated through the model intuitively reflect the degree of traffic pressure impact of the surrounding area on the urban traffic area, providing a quantitative basis for the traffic management department to formulate traffic guidance, traffic restrictions and other measures.

[0159] S5. Based on the congestion situation intensity, the traffic efficiency and the traffic pressure conduction degree, formulate the traffic intelligent guidance strategy for the urban traffic area, and based on the traffic intelligent guidance strategy, execute the traffic guidance work for the urban traffic area to obtain the traffic guidance result.

[0160] The present invention formulates the traffic intelligent guidance strategy by comprehensively considering the congestion situation intensity, the traffic efficiency and the traffic pressure conduction degree, can grasp the urban traffic situation from multiple dimensions, greatly improves the scientificity and pertinence of strategy formulation, and then carries out the traffic guidance work based on this strategy to improve the traffic intelligent guidance efficiency of the smart city. It should be explained that the traffic intelligent guidance strategy is a series of specific traffic guidance methods and measures formulated for the urban traffic area, and its purpose is to relieve traffic congestion and improve traffic fluency.

[0161] Further, the steps for formulating the traffic intelligent guidance strategy for the urban traffic area are as follows:

[0162] Divide the traffic situation levels: According to the value ranges of the congestion situation intensity, the traffic efficiency and the traffic pressure conduction degree, divide the traffic situation of the urban traffic area into four levels: unobstructed, slightly congested, moderately congested, and severely congested. For example, when the congestion situation intensity is lower than 0.3, the traffic efficiency is higher than 0.8, and the traffic pressure conduction degree is lower than 0.2, it is determined as the unobstructed level; when the congestion situation intensity is between 0.3 - 0.5, the traffic efficiency is between 0.6 - 0.8, and the traffic pressure conduction degree is between 0.2 - 0.4, it is determined as the slightly congested level, and so on, and clear division criteria are formulated.

[0163] Formulate strategies for different levels:

[0164] Smoothness level: Maintain the existing traffic signal timing plan, keep the normal intensity of traffic patrol, and ensure the stability of traffic order. At the same time, utilize the traffic big data platform to collect and analyze traffic flow data in real time, provide data support for subsequent traffic planning, strengthen the daily maintenance of traffic facilities, and ensure that road signs and markings are clearly visible and traffic lights are operating normally.

[0165] Mild congestion level: Appropriately adjust the timing of traffic signals, extend the green light duration in the congested direction, and shorten the green light duration in the non-congested direction to balance traffic flow. Deploy more traffic assistants at key sections to guide vehicles to pass orderly, prevent vehicles from cutting in line or rushing through. Through channels such as traffic radio and mobile APPs, promptly release real-time traffic information to the public and guide them to choose reasonable travel routes.

[0166] Moderate congestion level: Activate the regional traffic coordination control strategy. According to the congestion conditions of different sections, conduct linkage control of traffic signals at adjacent intersections to achieve green wave traffic for vehicles. Implement tidal lane measures on some severely congested sections, flexibly adjust the lane direction according to the change of traffic flow, strengthen the traffic management of vehicles such as trucks and motorcycles, and restrict their entry into the congested area during congested periods.

[0167] Severe congestion level: Implement traffic control in the core congested area, such as one-way traffic and restricting the passage of specific vehicles. Dispatch emergency rescue vehicles to promptly handle traffic accidents and breakdown vehicles and restore the road traffic capacity. Collaborate with the public transportation department to increase the departure frequency of buses and subways, encourage citizens to choose public transportation for travel, and reduce the use of private cars.

[0168] Dynamic adjustment strategy: Establish a real-time traffic monitoring system to continuously track the changes in the intensity of congestion trend, traffic efficiency, and traffic pressure conduction. According to the real-time monitoring data, dynamically adjust the intelligent traffic guidance strategy to ensure that the strategy always adapts to the actual traffic conditions. For example, when the congestion in a certain area is alleviated, promptly adjust the traffic signal timing and traffic control measures to restore normal traffic order.

[0169] Based on the above-mentioned formulated intelligent traffic guidance strategy, relevant traffic management departments organize police forces and staff to strictly implement traffic guidance work in the urban traffic area. By reasonably allocating human and material resources, ensure the effective implementation of various guidance measures, and finally obtain the traffic guidance results, including feedback data on aspects such as the improvement degree of traffic congestion, the increase in the average driving speed of vehicles, and the satisfaction of citizens' travel, providing a basis for subsequent optimization of the traffic guidance strategy.

[0170] Compared with the problems described in the background art, by determining the operation and maintenance regulations corresponding to the traffic facilities, the present invention can obtain scientific and highly targeted maintenance and management guidelines for the traffic facilities under different conditions, thereby providing strong support for the formulation of congestion evaluation factors in the subsequent urban traffic area. Further, by analyzing the traffic flow state of the urban traffic area based on the real-time traffic data, the present invention can obtain the operation mode and change law of the traffic in the urban traffic area in different time and space dimensions, providing an important basis for the subsequent scientific calculation of the congestion disturbance coefficient. Further, by analyzing the vehicle behavior intention characteristics of the vehicles in the urban traffic area based on the regional driving trajectory images, the present invention can better grasp the driving purpose and trend of the vehicles in the traffic scenario, providing a basis for the subsequent calculation of the road traffic efficiency. Further, by collecting the land use information and real-time event information of the surrounding area, extracting the attraction factor therein, and analyzing the traffic pressure conduction degree based on this, the present invention can comprehensively and accurately grasp the traffic impact of the surrounding area on the urban traffic area, providing a strong basis for urban traffic planning and management. By comprehensively considering the congestion situation intensity, the traffic efficiency, and the traffic pressure conduction degree to formulate a traffic intelligent diversion strategy, the present invention can grasp the urban traffic situation from multiple dimensions, greatly improving the scientificity and pertinence of the strategy formulation. Furthermore, based on this strategy, traffic diversion work is carried out to improve the traffic intelligent diversion efficiency of the smart city. Therefore, the traffic intelligent diversion method and system based on the smart city provided by the embodiments of the present invention can improve the traffic intelligent diversion efficiency of the smart city.

[0171] Embodiment 2:

[0172] As Figure 2 shown, it is a functional module diagram of a traffic intelligent diversion system based on the smart city of the present invention.

[0173] The traffic intelligent diversion system 200 based on the smart city of the present invention can be installed in an electronic device. According to the functions realized, the traffic intelligent diversion system based on the smart city can include a congestion evaluation factor formulation module 201, a congestion situation intensity evaluation module 202, a traffic efficiency calculation module 203, a traffic pressure conduction degree analysis module 204, and a traffic diversion module 205. The modules of the present invention can also be referred to as units, which refer to a series of computer program segments that can be executed by the processor of the electronic device and can complete fixed functions, and are stored in the memory of the electronic device.

[0174] In the embodiments of the present invention, the functions of each module / unit are as follows:

[0175] The congestion evaluation factor formulation module 201 is configured to obtain the urban traffic area that needs traffic guidance, query the traffic facilities in the urban traffic area, determine the traffic facility operation and maintenance regulations corresponding to the traffic facilities, and formulate the congestion evaluation factors for the urban traffic area based on the traffic facility operation and maintenance regulations;

[0176] The congestion situation intensity evaluation module 202 is configured to collect the real-time traffic data in the urban traffic area, analyze the traffic flow state of the urban traffic area based on the real-time traffic data, calculate the congestion disturbance coefficient corresponding to the congestion evaluation factor by combining the traffic flow state and the real-time traffic data, and evaluate the congestion situation intensity of the urban traffic area based on the congestion disturbance coefficient;

[0177] The traffic efficiency calculation module 203 is configured to use the monitoring devices in the urban traffic area to collect the regional driving trajectory images of vehicles and the holographic road scene images, analyze the vehicle behavior intention characteristics of the vehicles in the urban traffic area based on the regional driving trajectory images, analyze the traffic condition matrix of the roads in the urban traffic area based on the holographic road scene images, and calculate the traffic efficiency of the roads in the urban traffic area by combining the vehicle behavior intention characteristics and the traffic condition matrix;

[0178] The traffic pressure conduction degree analysis module 204 is configured to collect the land use information and real-time event information of the surrounding areas of the urban traffic area, extract the attraction element factors from the land use information and the real-time event information, and analyze the traffic pressure conduction degree of the surrounding areas on the urban traffic area based on the attraction element factors;

[0179] The traffic guidance module 205 is configured to formulate the intelligent traffic guidance strategy for the urban traffic area based on the congestion situation intensity, the traffic efficiency, and the traffic pressure conduction degree, and execute the traffic guidance work for the urban traffic area based on the intelligent traffic guidance strategy to obtain the traffic guidance result.

[0180] Specifically, each module in the intelligent traffic guidance system 200 based on the smart city in the embodiments of the present invention adopts the same technical means as those in the Figure 1 intelligent traffic guidance method based on the smart city described above, and can produce the same technical effects, which will not be elaborated here.

[0181] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention.

[0182] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. 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 solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A traffic intelligent guidance method based on smart city, characterized in that: The method comprises: Acquire an urban traffic area that needs traffic diversion, query traffic facilities in the urban traffic area, determine traffic facility operation and maintenance regulations corresponding to the traffic facilities, and formulate congestion evaluation factors for the urban traffic area based on the traffic facility operation and maintenance regulations, wherein determining the traffic facility operation and maintenance regulations corresponding to the traffic facilities includes: Collecting infrastructure information of the transportation facilities, and calculating information entropy corresponding to the infrastructure information; Based on the information entropy, extracting key facility information from the infrastructure information; Querying the facility operation and maintenance criteria corresponding to the transportation facility, and calculating the fit index between the key facility information and the facility operation and maintenance criteria; Determining, based on the fit index and the key facility information, a transportation facility operation and maintenance specification corresponding to the transportation facility; The step of calculating the compatibility index between the key facility information and the facility operation and maintenance criteria includes: Extracting characteristic characters from the key facility information and the facility operation and maintenance criteria to obtain facility information characteristic characters and operation and maintenance criteria characteristic characters; Performing semantic analysis on the facility information characteristic characters and the operation and maintenance criteria characteristic characters respectively to obtain the first characteristic character semantics and the second characteristic character semantics; Performing vectorization processing on the first characteristic character semantics and the second characteristic character semantics to obtain a first semantic vector and a second semantic vector; Combining the first semantic vector and the second semantic vector, the matching index between the key facility information and the facility operation and maintenance criteria is calculated by the following formula: Among them, T represents the fit index between key facility information and facility operation and maintenance criteria, A i represents the i-th vector in the first semantic vector, i represents the sequence number of the first semantic vector, B j represents the jth vector in the second semantic vector, j represents the sequence number of the second semantic vector, max(A i ,B j ) represents taking the maximum vector between the i-th vector and the j-th vector, q and r represent the number of the first semantic vector and the second semantic vector respectively; Collecting real-time traffic data in the urban traffic area, analyzing the traffic flow dimension state of the urban traffic area based on the real-time traffic data, calculating the congestion disturbance coefficient corresponding to the congestion evaluation factor in combination with the traffic flow dimension state and the real-time traffic data, and evaluating the congestion state intensity of the urban traffic area based on the congestion disturbance coefficient, wherein the analyzing the traffic flow dimension state of the urban traffic area based on the real-time traffic data includes: Performing data cleaning on the real-time traffic data to obtain target traffic data; Extracting multi-dimensional traffic features of the target traffic data, and performing standard processing on the multi-dimensional traffic features to obtain standard traffic features; Performing feature dimensionality reduction processing on the standard traffic features to obtain reduced-dimensionality traffic features; Performing feature clustering processing on the dimension-reduced traffic features to obtain clustered traffic features; Performing spatiotemporal pattern analysis on the clustered traffic characteristics to obtain traffic spatiotemporal pattern characteristics; Analyzing the traffic flow dimensional state of the urban traffic area based on the traffic spatiotemporal pattern characteristics; The step of combining the traffic flow dimension state and the real-time traffic data to calculate the congestion disturbance coefficient corresponding to the congestion evaluation factor includes: Selecting a congestion-related dimension corresponding to the congestion evaluation element from the traffic flow dimension, and calculating a dimensional congestion disturbance coefficient corresponding to the congestion-related dimension based on the real-time traffic data; Based on the real-time traffic data, determine whether there is a traffic accident in the urban traffic area; if there is no traffic accident, calculate the congestion disturbance coefficient corresponding to the congestion evaluation factor in combination with the dimensional congestion disturbance coefficient; If a traffic accident occurs, extracting accident feature data related to the traffic accident from the real-time traffic data, and calculating the accident interference degree corresponding to the traffic accident based on the accident feature data; Calculate the congestion disturbance coefficient corresponding to the congestion evaluation factor by combining the accident interference degree and the dimensional congestion disturbance coefficient; The step of calculating the accident interference degree corresponding to the traffic accident based on the accident characteristic data includes: Extracting accident damage records, accident handling cycles and accident occurrence points from the accident feature data, and evaluating the accident level corresponding to the traffic accident based on the accident damage records; Based on the accident handling cycle, determining the accident duration cycle corresponding to the traffic accident; Calculate the distance between the traffic accident and the adjacent road based on the accident occurrence point; In combination with the interval distance value, the accident duration period and the accident level, the accident interference degree corresponding to the traffic accident can be calculated by the following formula: Among them, D represents the accident interference degree corresponding to the traffic accident, E represents the accident level, d represents the interval distance value, t represents the accident duration period, and δd represents the distance impact factor; The monitoring equipment in the urban traffic area is used to collect regional driving trajectory images and road scene holographic images of vehicles, and based on the regional driving trajectory images, the vehicle behavior intention characteristics of the vehicles in the urban traffic area are analyzed, and based on the road scene holographic images, the traffic condition matrix of the roads in the urban traffic area is analyzed, and the traffic efficiency of the roads in the urban traffic area is calculated by combining the vehicle behavior intention characteristics and the traffic condition matrix, wherein the vehicle behavior intention characteristics of the vehicles in the urban traffic area are analyzed based on the regional driving trajectory images, including: Performing image preprocessing on the driving trajectory image of the region to obtain an optimized driving trajectory image; Performing feature extraction processing on the optimized driving trajectory image to obtain a feature driving trajectory image; Identifying a vehicle motion trajectory in the characteristic driving trajectory image, and calculating a trajectory change rate of the vehicle in the urban traffic area based on the vehicle motion trajectory; Extracting the track point information corresponding to the characteristic driving track image, and calculating the behavior intention parameters of the vehicles in the urban traffic area based on the track point information; generating a vehicle behavior intention feature of vehicles in the urban traffic area based on the trajectory change rate and the behavior intention parameter; Wherein, the calculating the trajectory change rate of the vehicle in the urban traffic area based on the vehicle motion trajectory includes: Acquire a standard motion trajectory of vehicles in the urban traffic area, and perform curve fitting processing on the vehicle motion trajectory and the standard motion trajectory respectively to obtain a first trajectory curve and a second trajectory curve; Based on the first trajectory curve, calculating a trajectory curvature sequence corresponding to the vehicle motion trajectory to obtain a first trajectory curvature sequence; Based on the second trajectory curve, calculating the trajectory curvature sequence corresponding to the standard motion trajectory to obtain a second trajectory curvature sequence; and calculating the trajectory lengths corresponding to the vehicle motion trajectory and the standard motion trajectory to obtain a first trajectory length and a second trajectory length; The trajectory change rate of the vehicle in the urban traffic area is calculated by combining the first trajectory curvature sequence, the second trajectory curvature sequence, the first trajectory length, and the second trajectory length using the following formula: Among them, F represents the trajectory change rate of vehicles in the urban traffic area, H f represents the fth curvature value in the first trajectory curvature sequence, G f represents the fth curvature value in the second trajectory curvature sequence, L1 represents the length of the first trajectory, L2 represents the length of the second trajectory, f represents the sequence number in the trajectory curvature sequence, and u represents the length of the trajectory curvature sequence; Collecting land use information and real-time event information of the surrounding areas of the urban traffic area, extracting attraction factors from the land use information and the real-time event information, and analyzing the degree of traffic pressure transmission from the surrounding areas to the urban traffic area based on the attraction factors; Based on the intensity of the congestion situation, the traffic efficiency and the traffic pressure conductivity, an intelligent traffic diversion strategy for the urban traffic area is formulated, and based on the intelligent traffic diversion strategy, traffic diversion work for the urban traffic area is performed to obtain traffic diversion results.

2. The intelligent traffic diversion method based on smart city as claimed in claim 1, characterized in that: The congestion evaluation factors of the urban traffic area are formulated based on the traffic facility operation and maintenance regulations, including: Performing element mining on the transportation facility operation and maintenance regulations to obtain key elements of the regulations; Constructing an element judgment matrix of the key elements of the specification, and calculating element weights corresponding to the key elements of the specification based on the element judgment matrix; The traffic industry standards of the urban traffic area are queried, and the congestion evaluation factors of the urban traffic area are formulated in combination with the factor weights, the traffic industry standards and the key elements of the regulations.

3. A smart traffic management system based on smart city, characterized in that: The system comprises: The congestion evaluation factor formulation module is used to obtain an urban traffic area that needs traffic diversion, query the traffic facilities in the urban traffic area, determine the traffic facility operation and maintenance regulations corresponding to the traffic facilities, and formulate the congestion evaluation factors of the urban traffic area based on the traffic facility operation and maintenance regulations, wherein the determination of the traffic facility operation and maintenance regulations corresponding to the traffic facilities includes: Collecting infrastructure information of the transportation facilities, and calculating information entropy corresponding to the infrastructure information; Based on the information entropy, extracting key facility information from the infrastructure information; Querying the facility operation and maintenance criteria corresponding to the transportation facility, and calculating the fit index between the key facility information and the facility operation and maintenance criteria; Determining, based on the fit index and the key facility information, a transportation facility operation and maintenance specification corresponding to the transportation facility; The step of calculating the compatibility index between the key facility information and the facility operation and maintenance criteria includes: Extracting characteristic characters from the key facility information and the facility operation and maintenance criteria to obtain facility information characteristic characters and operation and maintenance criteria characteristic characters; Performing semantic analysis on the facility information characteristic characters and the operation and maintenance criteria characteristic characters respectively to obtain the first characteristic character semantics and the second characteristic character semantics; Performing vectorization processing on the first characteristic character semantics and the second characteristic character semantics to obtain a first semantic vector and a second semantic vector; Combining the first semantic vector and the second semantic vector, the matching index between the key facility information and the facility operation and maintenance criteria is calculated by the following formula: Among them, T represents the fit index between key facility information and facility operation and maintenance criteria, A i represents the i-th vector in the first semantic vector, i represents the sequence number of the first semantic vector, B j represents the jth vector in the second semantic vector, j represents the sequence number of the second semantic vector, max(A i ,B j ) represents taking the maximum vector between the i-th vector and the j-th vector, q and r represent the number of the first semantic vector and the second semantic vector respectively; The congestion situation intensity evaluation module is used to collect real-time traffic data in the urban traffic area, analyze the traffic flow dimension of the urban traffic area based on the real-time traffic data, calculate the congestion disturbance coefficient corresponding to the congestion evaluation factor based on the traffic flow dimension and the real-time traffic data, and evaluate the congestion situation intensity of the urban traffic area based on the congestion disturbance coefficient, wherein the analysis of the traffic flow dimension of the urban traffic area based on the real-time traffic data includes: Performing data cleaning on the real-time traffic data to obtain target traffic data; Extracting multi-dimensional traffic features of the target traffic data, and performing standard processing on the multi-dimensional traffic features to obtain standard traffic features; Performing feature dimensionality reduction processing on the standard traffic features to obtain reduced-dimensionality traffic features; Performing feature clustering processing on the dimension-reduced traffic features to obtain clustered traffic features; Performing spatiotemporal pattern analysis on the clustered traffic characteristics to obtain traffic spatiotemporal pattern characteristics; Analyzing the traffic flow dimensional state of the urban traffic area based on the traffic spatiotemporal pattern characteristics; The step of combining the traffic flow dimension state and the real-time traffic data to calculate the congestion disturbance coefficient corresponding to the congestion evaluation factor includes: Selecting a congestion-related dimension corresponding to the congestion evaluation element from the traffic flow dimension, and calculating a dimensional congestion disturbance coefficient corresponding to the congestion-related dimension based on the real-time traffic data; Based on the real-time traffic data, determine whether there is a traffic accident in the urban traffic area; if there is no traffic accident, calculate the congestion disturbance coefficient corresponding to the congestion evaluation factor in combination with the dimensional congestion disturbance coefficient; If a traffic accident occurs, extracting accident feature data related to the traffic accident from the real-time traffic data, and calculating the accident interference degree corresponding to the traffic accident based on the accident feature data; Calculate the congestion disturbance coefficient corresponding to the congestion evaluation factor by combining the accident interference degree and the dimensional congestion disturbance coefficient; The step of calculating the accident interference degree corresponding to the traffic accident based on the accident characteristic data includes: Extracting accident damage records, accident handling cycles and accident occurrence points from the accident feature data, and evaluating the accident level corresponding to the traffic accident based on the accident damage records; Based on the accident handling cycle, determining the accident duration cycle corresponding to the traffic accident; Calculate the distance between the traffic accident and the adjacent road based on the accident occurrence point; In combination with the interval distance value, the accident duration period and the accident level, the accident interference degree corresponding to the traffic accident can be calculated by the following formula: Among them, D represents the accident interference degree corresponding to the traffic accident, E represents the accident level, d represents the interval distance value, t represents the accident duration period, and δd represents the distance impact factor; The traffic efficiency calculation module is used to collect regional driving trajectory images and road scene holographic images of vehicles using monitoring equipment in the urban traffic area, analyze vehicle behavior intention characteristics of vehicles in the urban traffic area based on the regional driving trajectory images, analyze the traffic condition matrix of roads in the urban traffic area based on the road scene holographic images, and calculate the traffic efficiency of roads in the urban traffic area by combining the vehicle behavior intention characteristics and the traffic condition matrix, wherein the analysis of vehicle behavior intention characteristics of vehicles in the urban traffic area based on the regional driving trajectory images includes: Performing image preprocessing on the driving trajectory image of the region to obtain an optimized driving trajectory image; Performing feature extraction processing on the optimized driving trajectory image to obtain a feature driving trajectory image; Identifying a vehicle motion trajectory in the characteristic driving trajectory image, and calculating a trajectory change rate of the vehicle in the urban traffic area based on the vehicle motion trajectory; Extracting the track point information corresponding to the characteristic driving track image, and calculating the behavior intention parameters of the vehicles in the urban traffic area based on the track point information; generating a vehicle behavior intention feature of vehicles in the urban traffic area based on the trajectory change rate and the behavior intention parameter; Wherein, the calculating the trajectory change rate of the vehicle in the urban traffic area based on the vehicle motion trajectory includes: Acquire a standard motion trajectory of vehicles in the urban traffic area, and perform curve fitting processing on the vehicle motion trajectory and the standard motion trajectory respectively to obtain a first trajectory curve and a second trajectory curve; Based on the first trajectory curve, calculating a trajectory curvature sequence corresponding to the vehicle motion trajectory to obtain a first trajectory curvature sequence; Based on the second trajectory curve, calculating the trajectory curvature sequence corresponding to the standard motion trajectory to obtain a second trajectory curvature sequence; and calculating the trajectory lengths corresponding to the vehicle motion trajectory and the standard motion trajectory to obtain a first trajectory length and a second trajectory length; The trajectory change rate of the vehicle in the urban traffic area is calculated by combining the first trajectory curvature sequence, the second trajectory curvature sequence, the first trajectory length, and the second trajectory length using the following formula: Among them, F represents the trajectory change rate of vehicles in the urban traffic area, H f represents the fth curvature value in the first trajectory curvature sequence, G f represents the fth curvature value in the second trajectory curvature sequence, L1 represents the length of the first trajectory, L2 represents the length of the second trajectory, f represents the sequence number in the trajectory curvature sequence, and u represents the length of the trajectory curvature sequence; A traffic pressure transmission degree analysis module, used to collect land use information and real-time event information of the surrounding areas of the urban traffic area, extract attraction element factors from the land use information and the real-time event information, and analyze the traffic pressure transmission degree of the surrounding areas to the urban traffic area based on the attraction element factors; The traffic diversion module is used to formulate an intelligent traffic diversion strategy for the urban traffic area based on the intensity of the congestion situation, the traffic efficiency and the traffic pressure conductivity, and to perform traffic diversion work in the urban traffic area based on the intelligent traffic diversion strategy to obtain traffic diversion results.

Images