Traffic simulation credibility assessment method, device, equipment and medium

Abstract

The embodiments of the present application disclose a traffic simulation credibility assessment method, apparatus, equipment and medium. The traffic network simulation environment includes a micro-traffic layer, a network topology layer and a network communication layer. A first network indicator is determined based on the first network information of the network communication layer obtained during the traffic simulation process; a second network indicator is determined based on the second network information corresponding to the network topology layer obtained during the traffic simulation process; a traffic indicator is determined based on the traffic information corresponding to the micro-traffic layer obtained during the traffic simulation process; a score between each two indicators among the first network indicator, the second network indicator and the traffic indicator is obtained, and a hierarchical analysis is performed based on the score to determine a subjective weight value of each indicator; the information entropy of each indicator is determined, and an objective weight value of each indicator is determined based on the information entropy; a normalized index value corresponding to each indicator is obtained, and a traffic simulation credibility assessment value is determined through the subjective weight value, the objective weight value and the normalized index value.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a traffic simulation credibility assessment method, device, equipment and medium. Background Art

[0002] Microscopic traffic simulation is a practical technology that simulates the operating status of road traffic systems based on similarity principles, information technology, system engineering and traffic engineering knowledge, using computers and system simulation models. It is an important research method in the field of traffic engineering.

[0003] Microscopic traffic simulations typically focus solely on traffic flow parameters and individual vehicle behavior, lacking in-depth analysis of traffic information system security factors. While these tools can effectively describe vehicle motion characteristics and traffic flow variations, they inadequately consider factors such as information security risks and cyberattacks within the traffic network. Consequently, they fail to capture network anomalies in real-world traffic environments. Consequently, related technologies face the technical challenge of ensuring the credibility of microscopic traffic simulation results. Summary of the Invention

[0004] The embodiments of the present application provide a traffic simulation credibility assessment method, apparatus, device and medium, which can accurately assess the credibility of the simulation results of traffic simulation.

[0005] To achieve the above objectives, an embodiment of the present application provides a traffic simulation credibility assessment method, which is applied to a traffic network simulation environment, wherein the traffic network simulation environment includes a microscopic traffic layer, a network topology layer, and a network communication layer. The method includes:

[0006] Acquire first network information corresponding to the network communication layer during the traffic simulation process, and determine at least one first network indicator based on the first network information;

[0007] Acquire second network information corresponding to the network topology layer during the traffic simulation process, and determine at least one second network indicator based on the second network information;

[0008] Acquiring traffic information corresponding to the microscopic traffic layer during traffic simulation, and determining at least one traffic indicator based on the traffic information;

[0009] Obtaining scores corresponding to each two of the at least one first network indicator, the at least one second network indicator, and the at least one traffic indicator, and performing hierarchical analysis based on the scores to determine a subjective weight value corresponding to each indicator;

[0010] Determining information entropy corresponding to each of the at least one first network indicator, the at least one second network indicator, and the at least one traffic indicator, and determining an objective weight value corresponding to each indicator based on the information entropy;

[0011] A normalized index value corresponding to each of the indicators is obtained, and a traffic simulation credibility evaluation value is determined according to the subjective weight value, the objective weight value, and the normalized index value corresponding to each of the indicators.

[0012] To achieve the above objectives, an embodiment of the present application provides, on one hand, a traffic simulation credibility assessment device, which is applied to a traffic network simulation environment, wherein the traffic network simulation environment includes a microscopic traffic layer, a network topology layer, and a network communication layer. The device includes:

[0013] A first acquisition module is configured to acquire first network information corresponding to the network communication layer during a traffic simulation process, and determine at least one first network indicator based on the first network information;

[0014] A second acquisition module is used to obtain second network information corresponding to the network topology layer during the traffic simulation process, and determine at least one second network indicator based on the second network information;

[0015] A third acquisition module is used to obtain traffic information corresponding to the microscopic traffic layer during the traffic simulation process, and determine at least one traffic indicator based on the traffic information;

[0016] A first determination module is configured to obtain a score corresponding to each two of the at least one first network indicator, the at least one second network indicator, and the at least one traffic indicator, and to perform a hierarchical analysis based on the score to determine a subjective weight value corresponding to each indicator;

[0017] A second determination module is configured to determine the information entropy corresponding to each of the at least one first network indicator, the at least one second network indicator, and the at least one traffic indicator, and determine the objective weight value corresponding to each indicator based on the information entropy;

[0018] The evaluation module is used to obtain the normalized index value corresponding to each indicator, and determine the traffic simulation credibility evaluation value according to the subjective weight value, the objective weight value and the normalized index value corresponding to each indicator.

[0019] In some embodiments, the first network information includes the number of nodes, a timestamp of node receiving data, and a timestamp of node forwarding data, and the first network indicator includes a node processing delay; and the first acquisition module is configured to:

[0020] Subtract the node forwarding data timestamp from the node receiving data timestamp of each node to obtain the receiving delay of each node;

[0021] The node processing delay is obtained by adding the receiving delay of each node and dividing it by the number of nodes.

[0022] In some embodiments, the first network information includes data transmission delay, average data transmission delay, and number of data packets, and the first network indicator includes delay jitter; and the first acquisition module is configured to:

[0023] Subtract the average data transmission delay from the data transmission delay corresponding to each data packet to obtain the transmission delay difference corresponding to each data packet;

[0024] The transmission delay difference corresponding to each data packet is added and divided by the number of data packets to obtain the delay jitter.

[0025] In some embodiments, the traffic information includes a signal light cycle duration, a lane saturation flow rate, and a green light signal duration, and the traffic indicator includes a lane capacity; the third acquisition module is configured to:

[0026] Divide the green light signal duration by the signal light cycle duration to obtain the green light signal ratio;

[0027] The lane capacity is obtained by multiplying the green light signal ratio by the lane saturation flow rate.

[0028] In some embodiments, the traffic information includes a vehicle arrival rate, a vehicle departure rate, and a queue duration, and the traffic indicator includes a vehicle queue length; the third acquisition module is configured to:

[0029] The vehicle retention rate is obtained by subtracting the vehicle departure rate from the vehicle arrival rate;

[0030] The vehicle queue length is obtained by multiplying the vehicle retention rate by the queue duration.

[0031] In some embodiments, the first determining module is configured to:

[0032] constructing a plurality of elements based on each of the at least one first network indicator, the at least one second network indicator, and the at least one traffic indicator;

[0033] A judgment matrix is ​​generated according to each element and the score value corresponding to each element, and a subjective weight value corresponding to each indicator is determined according to the judgment matrix.

[0034] In some embodiments, the first determining module is configured to:

[0035] Normalizing the elements in the judgment matrix to obtain the target element value corresponding to each element;

[0036] Generate a target matrix according to the element and the target element value;

[0037] Adding the element values ​​of the row elements corresponding to each indicator in the target matrix to obtain the initial weight corresponding to each indicator;

[0038] The initial weights are normalized to obtain the subjective weight values ​​corresponding to each indicator.

[0039] In some embodiments, the first determining module is configured to:

[0040] After adding the element values ​​of the row elements corresponding to each indicator in the target matrix to obtain the initial weight corresponding to each indicator, determining the maximum eigenvalue corresponding to the target matrix according to the initial weight;

[0041] Performing a consistency check on the target matrix according to the maximum eigenvalue to obtain a consistency check result;

[0042] When the consistency check result is less than a preset consistency check value, the initial weight is normalized to obtain a subjective weight value corresponding to each indicator.

[0043] In some embodiments, the second determining module is configured to:

[0044] Generate a first matrix according to the index value corresponding to each of the at least one first network index, the at least one second network index, and the at least one traffic index and each of the indicators;

[0045] Determine a normalized positive index value and a normalized negative index value of each element in the first matrix according to the maximum element value and the minimum element value in the first matrix;

[0046] Perform negative index normalization on each element in the first matrix according to the minimum element value in the first matrix;

[0047] The information entropy corresponding to each indicator is determined according to the normalized value of the positive indicator and the normalized value of the negative indicator.

[0048] In some embodiments, the second determining module is configured to:

[0049] Subtract the objective weight value corresponding to each indicator from the preset value to obtain a first result corresponding to each indicator;

[0050] Adding the first results corresponding to each indicator to obtain a second result;

[0051] The first result corresponding to each indicator is divided by the second result to obtain the objective weight value corresponding to each indicator.

[0052] In order to achieve the above-mentioned objectives, an embodiment of the present application provides a computer-readable storage medium on the one hand, which stores multiple instructions, and the instructions are suitable for a processor to load to execute the traffic simulation credibility assessment method provided by the embodiment of the present application.

[0053] In order to achieve the above-mentioned objectives, an embodiment of the present application provides a computer device on the one hand, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. When the processor executes the computer program, the traffic simulation credibility assessment method provided in the embodiment of the present application is implemented.

[0054] In the present application, the traffic network simulation environment includes a micro-traffic layer, a network topology layer and a network communication layer. The first network information corresponding to the network communication layer in the traffic simulation process is obtained, and at least one first network indicator is determined based on the first network information; the second network information corresponding to the network topology layer in the traffic simulation process is obtained, and at least one second network indicator is determined based on the second network information; the traffic information corresponding to the micro-traffic layer in the traffic simulation process is obtained, and at least one traffic indicator is determined based on the traffic information; the scores corresponding to each two indicators among at least one first network indicator, at least one second network indicator and at least one traffic indicator are obtained, and a hierarchical analysis is performed based on the scores to determine the subjective weight value corresponding to each indicator; the information entropy corresponding to each indicator among at least one first network indicator, at least one second network indicator and at least one traffic indicator is determined, and the objective weight value corresponding to each indicator is determined based on the information entropy; the normalized index value corresponding to each indicator is obtained, and the traffic simulation credibility evaluation value is determined based on the subjective weight value, objective weight value and normalized index value corresponding to each indicator.

[0055] In this way, the present application first constructs a network-related traffic network simulation environment by combining the micro-traffic layer with the network topology layer and the network communication layer, thereby taking into account network security and traffic flow parameters to obtain more accurate traffic simulation results. Secondly, by obtaining the first network information of the network communication layer and determining at least one first network indicator through the first network information, by obtaining the second network information of the network topology layer and determining at least one second network indicator through the second network information, then by obtaining the traffic information of the micro-traffic layer and determining at least one traffic indicator through the traffic information, finally, combining the first network indicator, the second network indicator and the traffic indicator, determining the score corresponding to each indicator, and performing hierarchical analysis based on the score to determine the subjective weight value corresponding to each indicator. Then, determine the information entropy corresponding to each indicator, and determine the objective weight value corresponding to each indicator based on the information entropy. Finally, obtain the normalized index value corresponding to each indicator, and determine the traffic simulation credibility evaluation value based on the subjective weight value, objective weight value and normalized index value corresponding to each indicator. In this way, by combining the first network indicator, the second network indicator, and the traffic indicator, the credibility of the traffic simulation can be accurately assessed. By introducing both subjective and objective weights for each indicator, the credibility assessment value of the traffic simulation can be more accurately determined. Compared to related art solutions that focus solely on traffic flow parameters and individual vehicle behavior characteristics, this application combines various indicators for a comprehensive assessment, thereby determining the credibility assessment value corresponding to the traffic simulation.

[0056] Other features and advantages of the present application will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present application. The purposes and other advantages of the present application can be achieved and obtained through the structures particularly pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0058] Figure 1 Schematic diagram of the system framework corresponding to the traffic simulation credibility assessment method provided in the embodiment of the present application;

[0059] Figure 2 Schematic diagram of the composition of the traffic network simulation environment provided in an embodiment of the present application;

[0060] Figure 3Schematic diagram of the flow of the traffic simulation credibility assessment method provided in the embodiment of the present application;

[0061] Figure 4 Schematic diagram of the matrix constructed by each indicator provided in the embodiment of the present application;

[0062] Figure 5 This is another flow chart of the traffic simulation credibility assessment method provided by an embodiment of the present application;

[0063] Figure 6 Schematic diagram of the structure of the traffic simulation credibility assessment device provided in an embodiment of the present application;

[0064] Figure 7 It is a structural diagram of the computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0065] In order to enable those skilled in the art to better understand the solutions of this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of this application.

[0066] It should be noted that in each specific embodiment of this application, when it comes to the need to perform relevant processing based on network information or traffic information, the user's permission or consent will be obtained first, and the collection, use and processing of such data will comply with relevant laws, regulations and standards. In addition, when the embodiment of this application needs to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or by jumping to a confirmation page. After clearly obtaining the user's separate permission or consent, the necessary user-related data for the normal operation of the embodiment of this application will be obtained.

[0067] Some processes described in the specification, claims, and figures include multiple steps that appear in a specific order. However, it should be understood that these steps may be performed in a different order or in parallel. Step numbers are used solely to distinguish between different steps and do not inherently indicate any order of execution. Furthermore, terms such as "first," "second," or "target" are used to distinguish similar objects and are not necessarily intended to describe a specific order or precedence.

[0068] The embodiments of the present application provide a traffic simulation credibility assessment method, apparatus, device and medium. Specifically, the embodiments of the present application will be described from the dimension of a traffic simulation credibility assessment apparatus, and the traffic simulation credibility assessment apparatus can be specifically integrated in a computer device, which can be a server or a terminal or other device. The server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The terminal can be a smart phone, tablet computer, laptop computer, desktop computer, smart speaker, smart watch, smart home appliance, vehicle-mounted terminal, intelligent voice interaction device, aircraft, etc., but is not limited to this.

[0069] Before further explaining the embodiments of the present application in detail, the nouns and terms involved in the embodiments of the present application are explained. The nouns and terms involved in the embodiments of the present application are subject to the following interpretations:

[0070] The network communication layer consists of a cyber range, a virtual network environment built through virtualization and simulation technologies. It simulates real-world network architectures, devices, and application scenarios, providing a controllable, reproducible, and scalable experimental platform for network security research, attack and defense drills, and personnel training. It can simulate various network attack and defense scenarios, helping users improve their network security protection capabilities and emergency response capabilities without impacting the actual network.

[0071] The network topology layer, comprised of multiple network topology devices, provides an abstract representation of the entire network. Network topology refers to the physical or logical layout of network devices (such as computers, routers, and switches) and connecting media (such as network cables and optical fibers). It describes the connections between devices, data flow paths, and the overall structure of the network. Network topology not only influences network performance, reliability, and maintainability but also serves as a core foundation for network design and planning.

[0072] The micro-traffic layer consists of a micro-simulated traffic environment. This environment recreates real-world traffic dynamics by modeling and simulating the behavior of individual vehicles, pedestrians, and other traffic entities. It accurately measures the position, velocity, acceleration, and other state changes of each traffic entity at every time step, providing highly detailed and accurate analysis for traffic planning, management, and research.

[0073] If other related terms are designed later in the text, they will be described in detail later.

[0074] First, let’s explain the technical problems existing in related technologies:

[0075] Microscopic traffic simulation is a practical technology that simulates the operating status of road traffic systems based on similarity principles, information technology, system engineering and traffic engineering knowledge, using computers and system simulation models. It is an important research method in the field of traffic engineering.

[0076] Microscopic traffic simulations typically focus solely on traffic flow parameters and individual vehicle behavior, lacking in-depth analysis of traffic information system security factors. While these tools can effectively describe vehicle motion characteristics and traffic flow variations, they inadequately consider factors such as information security risks and cyberattacks within the traffic network. Consequently, they fail to capture network anomalies in real-world traffic environments. Consequently, related technologies face the technical challenge of ensuring the credibility of microscopic traffic simulation results.

[0077] In order to solve the above problems, the embodiments of the present application propose a traffic simulation credibility assessment method, device, equipment and medium. First, a traffic network simulation environment including network-related information is constructed by combining the micro-traffic layer with the network topology layer and the network communication layer, thereby taking into account network security and traffic flow parameters to obtain more accurate traffic simulation results. Secondly, by obtaining the first network information of the network communication layer and determining at least one first network indicator based on the first network information, by obtaining the second network information of the network topology layer and determining at least one second network indicator based on the second network information, then by obtaining the traffic information of the micro-traffic layer and determining at least one traffic indicator based on the traffic information, finally, combining the first network indicator, the second network indicator and the traffic indicator, determining the score corresponding to each indicator, and performing hierarchical analysis based on the score to determine the subjective weight value corresponding to each indicator. Then, the information entropy corresponding to each indicator is determined, and the objective weight value corresponding to each indicator is determined based on the information entropy. Finally, the normalized index value corresponding to each indicator is obtained, and the traffic simulation credibility assessment value is determined based on the subjective weight value, objective weight value and normalized index value corresponding to each indicator. In this way, by combining the first network indicator, the second network indicator, and the traffic indicator, the credibility of the traffic simulation can be accurately assessed. By introducing both subjective and objective weights for each indicator, the credibility assessment value of the traffic simulation can be more accurately determined. Compared to related art solutions that focus solely on traffic flow parameters and individual vehicle behavior characteristics, this application combines various indicators for a comprehensive assessment, thereby determining the credibility assessment value corresponding to the traffic simulation.

[0078] See also Figure 1 , Figure 1Schematic diagram of the system framework corresponding to the traffic simulation credibility evaluation method provided in the embodiment of the present application. The traffic simulation credibility evaluation method provided in the embodiment of the present application can be applied in this system framework.

[0079] Please refer to the following for details: Figure 1 , Figure 1 1 is a system architecture diagram of the traffic simulation credibility evaluation method provided in the embodiment of the present application, which includes a terminal 140, the Internet 130, a gateway 120, a server 110, etc.

[0080] The terminal 140 or the server 110 may be a device for executing the traffic simulation credibility assessment method.

[0081] Terminal 140 includes, but is not limited to, mobile phones, computers, intelligent voice interaction devices, smart home appliances, in-vehicle terminals, aircraft, and the like. Embodiments of the present application can be applied to various scenarios, including, but not limited to, traffic simulation and network security. Furthermore, it can be a single device or a combination of multiple devices. For example, multiple desktop computers connected via a local area network, sharing a common display, and working collaboratively, collectively constitute terminal 140. Terminal 140 can communicate with Internet 130 via wired or wireless means to exchange data.

[0082] Server 110 refers to a computer system that provides certain services to terminal 140. Compared to ordinary terminal 140, server 110 has higher requirements in terms of stability, security, and performance. Server 110 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms.

[0083] Gateway 120, also known as a gateway or protocol converter, implements network interconnection at the transport layer and is a computer system or device that performs a conversion function. It acts as a translator between two systems using different communication protocols, data formats, languages, or even completely different architectures. Gateways can also provide filtering and security functions. Messages sent from terminal 140 to server 110 are sent through gateway 120 to the corresponding server 110. Messages sent from server 110 to terminal 140 are also sent through gateway 120 to the corresponding terminal 140.

[0084] The traffic simulation credibility evaluation method in the embodiment of the present application can be applied in a variety of scenarios, such as traffic simulation, network security, etc. The scenarios to which the traffic simulation credibility evaluation method in the present application is applied are not limited here.

[0085] See also Figure 2 , Figure 2 It is a schematic diagram of the composition of the traffic network simulation environment provided in an embodiment of the present application.

[0086] Among them, in this application, the entire traffic network simulation environment is mainly composed of a virtual environment and a real environment, wherein the virtual environment can be composed of a network target range and a micro traffic simulation environment, while the real environment includes a communication module and a simulator.

[0087] The micro-simulation traffic environment recreates real-world traffic dynamics by modeling and simulating the behavior of individual vehicles, pedestrians, and other traffic entities. It accurately measures the position, velocity, acceleration, and other state changes of each traffic entity at every time step, providing highly detailed and accurate analysis for traffic planning, management, and research.

[0088] A cyber range is a virtual network environment constructed through virtualization and simulation technologies. It simulates real-world network architectures, devices, and application scenarios, providing a controllable, reproducible, and scalable experimental platform for cybersecurity research, attack and defense drills, and personnel training. It can simulate various network attack and defense scenarios, helping users improve their cybersecurity protection capabilities and emergency response capabilities without impacting the real network. The cyber range and micro-traffic simulation environment can be combined to form a traffic simulation environment that includes cybersecurity scenarios. The cyber range and micro-traffic simulation environment can communicate through middleware interfaces, for example, using multiple middleware programs.

[0089] The communication module can communicate with the cyber range through a communication interface. This module can be a T-box communication module. The T-box (Telematics Box) is a core component of the in-vehicle intelligent terminal, primarily used to connect the vehicle to the outside world (such as cloud servers, mobile apps, other vehicles, or infrastructure), while integrating in-vehicle data collection, processing, and remote control functions. It is the key entry point to the Internet of Vehicles (IoV) and a fundamental module for implementing technologies such as intelligent connected vehicles and autonomous driving. The simulator can connect to the communication module via the CAN bus (Controller Area Network). The simulator can be an OBD-2 simulator, a device or software that simulates the behavior of a vehicle's On-Board Diagnostics-2 (OBD-2) system. The communication module, simulator, and cyber range can form a virtual reality interconnected environment.

[0090] Ultimately, the communication module, simulator, network target range and microscopic traffic simulation environment can constitute the traffic network simulation environment provided by the embodiment of the present application, in which traffic simulation can be implemented and corresponding simulation data can be generated during the traffic simulation process.

[0091] It should be noted that the traffic network simulation environment can be divided into three parts, namely the micro-traffic layer, the network topology layer and the network communication layer. The micro-traffic layer can be understood as being composed of the micro-traffic simulation environment, the network communication layer can be understood as being composed of the network target range, and the network topology layer can be understood as an abstract network representation composed of various nodes and devices in the entire traffic network simulation environment.

[0092] Among them, the micro-traffic layer corresponds to objects such as road network structure, traffic signals, traffic flow characteristics, vehicle operating status, vehicle queuing behavior, and traffic control equipment. The road network structure includes roads, intersections, etc., traffic signals include signal timing, phase switching, etc., traffic flow characteristics include traffic density, flow, speed, etc., vehicle operating status includes position, speed, acceleration, etc., vehicle queuing behavior includes queue length, parking, etc., and traffic control equipment includes signal lights, sensors, etc.

[0093] The network topology layer includes objects such as network nodes, inter-node links, network topology structures, and network paths. Network nodes include routers, switches, edge devices, and other devices. Inter-node links include connection relationships, link capacity, etc. Network topology structures include star, tree, mesh, and random complex network structures. Network paths include the length of the data transmission path and routing path selection.

[0094] The network communication layer includes objects such as communication links, network equipment, data packets, communication protocols, and network security events. Network equipment includes switches, routers, servers, etc. Data packets include data transmission, reception, and loss processes, etc. Communication protocols include TCP / IP, UDP and other protocol behavior simulations, etc. Network security events include attack events, abnormal traffic, etc.

[0095] Traffic simulation is performed in a traffic network simulation environment, by obtaining first network information corresponding to the network communication layer during the traffic simulation process, and determining at least one first network indicator based on the first network information; obtaining second network information corresponding to the network topology layer during the traffic simulation process, and determining at least one second network indicator based on the second network information; obtaining traffic information corresponding to the micro-traffic layer during the traffic simulation process, and determining at least one traffic indicator based on the traffic information; obtaining scores corresponding to each two indicators among at least one first network indicator, at least one second network indicator, and at least one traffic indicator, and determining a subjective weight value corresponding to each indicator through hierarchical analysis based on the scores; determining information entropy corresponding to each indicator among at least one first network indicator, at least one second network indicator, and at least one traffic indicator, and determining an objective weight value corresponding to each indicator based on the information entropy; obtaining a normalized index value corresponding to each indicator, and determining a traffic simulation credibility evaluation value based on the subjective weight value, objective weight value, and normalized index value corresponding to each indicator.

[0096] To understand the traffic simulation credibility evaluation method provided by the embodiment of this application in more detail, please refer to Figure 3 , Figure 3 : This is a flow chart of a traffic simulation credibility assessment method provided by an embodiment of the present application. The traffic simulation credibility assessment method can be applied to the above-mentioned traffic network simulation environment, and the traffic simulation credibility assessment method can include the following steps:

[0097] Step 210: Obtain first network information corresponding to the network communication layer during the traffic simulation process, and determine at least one first network indicator based on the first network information;

[0098] Step 220: Obtain second network information corresponding to the network topology layer during the traffic simulation process, and determine at least one second network indicator based on the second network information;

[0099] Step 230: Obtain traffic information corresponding to the microscopic traffic layer during the traffic simulation process, and determine at least one traffic indicator based on the traffic information;

[0100] Step 240: Obtain scores corresponding to each two of the at least one first network indicator, the at least one second network indicator, and the at least one traffic indicator, and perform hierarchical analysis based on the scores to determine a subjective weight value corresponding to each indicator;

[0101] Step 250: Determine the information entropy corresponding to each of the at least one first network indicator, the at least one second network indicator, and the at least one traffic indicator, and determine the objective weight value corresponding to each indicator based on the information entropy;

[0102] Step 260: Obtain the normalized index value corresponding to each index, and determine the traffic simulation credibility evaluation value according to the subjective weight value, objective weight value and normalized index value corresponding to each index.

[0103] Steps 210 to 260 will be described in detail below.

[0104] In step 210, first network information corresponding to the network communication layer in the traffic simulation process is obtained, and at least one first network indicator is determined based on the first network information.

[0105] The first network information corresponding to the network communication layer during the traffic simulation process can be obtained. The first network information may include the number of nodes, node ID, timestamp of each node receiving data, timestamp of each node forwarding data, total number of packet losses, total number of packets sent, network throughput, amount of data successfully received, total time spent on successfully receiving data, data transmission delay, average data transmission delay and number of data packets, etc.

[0106] By analyzing one, two or more types of data included in the first network information, one, two or more first network indicators can be obtained.

[0107] In some embodiments, the first network information includes the number of nodes, a timestamp of the node receiving data, and a timestamp of the node forwarding data, and the first network indicator includes a node processing delay; and determining at least one first network indicator based on the first network information includes:

[0108] (1.1) Subtract the timestamp of the node forwarding data from the timestamp of the node receiving data to obtain the receiving delay of each node;

[0109] (1.2) Add the receiving delay of each node and divide it by the number of nodes to get the node processing delay.

[0110] The specific calculation method is:

[0111] .in, is the node processing delay, which is the average time it takes for a node to receive a data packet, process it, and forward it. i is the node ID of each node. The timestamp of the data forwarded by node i, is the timestamp when node i receives the data, and N is the number of nodes. It can be expressed as the receiving delay of each node.

[0112] Through the above method, the receiving delay of each node can be calculated, and then the receiving delay corresponding to all nodes can be determined, that is, the average receiving delay, which can accurately reflect the corresponding network delay in the entire traffic network simulation environment.

[0113] In some embodiments, the first network information includes data transmission delay, average data transmission delay, and number of data packets, and the first network indicator includes delay jitter; and determining at least one first network indicator based on the first network information includes:

[0114] (2.1) Subtract the average data transmission delay from the data transmission delay of each data packet to obtain the transmission delay difference of each data packet;

[0115] (2.2) Add the transmission delay differences corresponding to each data packet and divide by the number of data packets to obtain the delay jitter.

[0116] The specific calculation method is as follows:

[0117] .in, Indicates delay jitter, which is the standard deviation of the delay time during data packet transmission. represents the data transmission delay of data packet i, is the average transmission delay corresponding to all data packets, n is the number of data packets. The unit of delay is milliseconds (ms).

[0118] This method can accurately determine the delay jitter corresponding to all data packets during transmission.

[0119] In some implementations, the packet loss rate, that is, the proportion of lost packets per unit time, may also be determined. The specific calculation method is as follows:

[0120] .in, is the packet loss rate, is the total number of packet losses, The total number of packets sent.

[0121] In some implementations, network throughput may also be determined, that is, the total number of data packets or bits successfully received per unit time. The specific calculation method is as follows:

[0122] .in, represents the network throughput, Indicates the amount of data successfully received (bits), and T indicates the time taken to receive this amount of data.

[0123] It should be noted that node processing delay, data packet loss rate, network throughput, delay jitter, etc. are all first network indicators. These are only part of the first network indicators. Other first network indicators can also be calculated through the first network information.

[0124] From the above, it can be seen that by calculating the first network indicator, the network status corresponding to the network communication layer can be accurately reflected.

[0125] In step 220, second network information corresponding to the network topology layer in the traffic simulation process is obtained, and at least one second network indicator is determined based on the second network information.

[0126] Among them, the second network information corresponding to the network topology layer in the traffic simulation process can be obtained. The second network information includes the number of nodes, node sets, edge sets, the number of nodes with degree k, the number of reachable node pairs, the shortest path length from node i to node j, etc.

[0127] By analyzing one, two or more types of data included in the second network information, one, two or more second network indicators can be obtained.

[0128] In some embodiments, the second network indicator includes the node degree distribution, that is, the probability distribution of the connectivity of each node, which reflects the degree of match between the heterogeneity of the network structure and the actual topology. The specific calculation method is as follows:

[0129] .in, is the proportion of nodes with degree k, is the number of nodes with degree k, and N is the number of nodes.

[0130] In some embodiments, the second network indicator includes a network connectivity rate, which is the ratio of the number of nodes to the total number of nodes, and is specifically calculated as follows:

[0131] .in, is the network connectivity rate, is the number of reachable node pairs, and N is the number of nodes.

[0132] In some embodiments, the second network indicator includes an average path length, that is, the average value of the shortest paths between nodes, which is used to evaluate the transmission efficiency of the network topology. The specific calculation method is as follows:

[0133] .in, represents the average path length, The shortest path length from node 𝑖 to node 𝑗, is the number of reachable node pairs.

[0134] In some embodiments, the second network indicator includes invulnerability, that is, the ability of the remaining network to maintain connectivity after a node is deleted from the network, represented by the average connected subgraph size or the connectivity loss ratio, and is specifically calculated as follows:

[0135] .in, Indicates invulnerability. express Remove Node The remaining network, , f represents the relevant indicator function.

[0136] It should be noted that the above-mentioned second network indicators are only part of the second network indicators, and other second network indicators can also be calculated through the second network information.

[0137] From the above, it can be seen that by calculating the second network indicator, the network status corresponding to the network topology layer can be accurately reflected.

[0138] In step 230, traffic information corresponding to the microscopic traffic layer in the traffic simulation process is obtained, and at least one traffic index is determined based on the traffic information.

[0139] Among them, the traffic information corresponding to the micro traffic layer in the traffic simulation process can be obtained, including the yellow light signal duration, the full red light signal duration, the signal cycle duration, lane capacity, vehicle speed, vehicle driving time, the number of vehicles in the road section, the green light signal duration, etc. Then you can

[0140] By analyzing one, two or more types of data contained in the traffic information, one, two or more traffic indicators can be obtained.

[0141] In some embodiments, traffic information includes vehicle arrival rates, vehicle departure rates, and queue durations; traffic indicators include vehicle queue lengths, which are the number of vehicles or the length of a vehicle queue waiting to pass due to traffic control (e.g., a signal light, a stop sign) or traffic congestion (e.g., a traffic accident); and determining at least one traffic indicator based on the traffic information includes:

[0142] (1.1) Subtract the vehicle departure rate from the vehicle arrival rate to obtain the vehicle retention rate;

[0143] (1.2) Multiply the vehicle retention rate by the queue duration to obtain the vehicle queue length.

[0144] The specific calculation method is as follows:

[0145] Q represents the length of the vehicle queue, represents the vehicle arrival rate (veh / s), Represents the vehicle departure rate (veh / s).

[0146] This allows the accurate determination of the queue length of vehicles.

[0147] In some embodiments, the traffic indicator includes phase switching delay, which is the additional time loss caused by yellow lights, full red lights, vehicle start-up, and clearing of the intersection when the traffic signal switches between different phases, resulting in a reduction in the effective travel time of the intersection. The specific calculation method is as follows:

[0148] . Indicates the phase switching delay, L indicates the duration of the yellow light signal, Indicates the duration of the full red light signal.

[0149] In some embodiments, the traffic indicator includes lane capacity, which is the maximum number of vehicles that can pass through a lane per unit time under certain traffic and road conditions. The specific calculation method is as follows:

[0150] Where C represents the signal cycle length, g represents the green light signal duration, and s represents the lane saturation flow rate (veh / h / ln, vehicles / hour / lane). Indicates lane capacity.

[0151] In some embodiments, the traffic indicator includes road capacity, which is the maximum number of vehicles that can pass through the road facility per unit time under specific road, traffic and control conditions. The specific calculation method is as follows:

[0152] .in, Indicates the road communication capacity (veh / h), represents the lane capacity of road i (veh / h / ln).

[0153] In some embodiments, the traffic indicator includes vehicle speed, that is, the average speed of all vehicles traveling on a certain road section or area during a certain period of time or on a certain road section, which is specifically calculated as follows:

[0154] Where v represents the speed, D represents the distance traveled by the vehicle, and t represents the time the vehicle travels.

[0155] In some embodiments, the traffic indicator includes vehicle density, that is, the number of vehicles that exist simultaneously per unit length of road within a certain road section, which is specifically calculated as follows:

[0156] Where k represents the vehicle density, N represents the number of vehicles in the road section, and L represents the duration of the yellow light signal.

[0157] It should be noted that the above traffic indicators are only part of the traffic indicators, and other traffic indicators can also be calculated through traffic information.

[0158] From the above, it can be seen that by calculating the traffic indicators, the road conditions corresponding to the microscopic road layer can be accurately reflected.

[0159] In step 240, the scores corresponding to each two indicators among at least one first network indicator, at least one second network indicator, and at least one traffic indicator are obtained, and a hierarchical analysis is performed based on the scores to determine the subjective weight value corresponding to each indicator.

[0160] Each of the at least one first network indicator, the at least one second network indicator, and the at least one traffic indicator can be used as a factor in the subsequent credibility assessment of the traffic simulation. Scores are then obtained for each indicator. For example, a team of experts in traffic simulation, network security, and complex networks can be assembled to conduct pairwise comparisons of each indicator, assigning subjective scores using a 1-9 scale, and constructing a pairwise comparison judgment matrix. During the scoring process, the experts quantitatively describe the indicator's importance to the assessment objective.

[0161] For example, the scoring criteria are as follows: 1: indicates that the two indicators are of equal importance; 3: indicates that one indicator is slightly more important than the other indicator; 5: indicates that one indicator is obviously more important than the other indicator; 7: indicates that one indicator is strongly more important than the other indicator; 9: indicates that one indicator is extremely important than the other indicator; 2, 4, 6, 8: indicate the intermediate values ​​between adjacent importance judgments.

[0162] Scores are set between each two indicators using the scoring criteria set by experts, and then hierarchical analysis is performed based on the scores to determine the subjective weight value corresponding to each indicator.

[0163] In some embodiments, performing hierarchical analysis based on the scores to determine the subjective weight value corresponding to each indicator includes:

[0164] (1.1) constructing a plurality of elements based on each of at least one first network indicator, at least one second network indicator, and at least one traffic indicator;

[0165] (1.2) Generate a judgment matrix based on each element and the score value corresponding to each element, and determine the subjective weight value corresponding to each indicator based on the judgment matrix.

[0166] Among them, multiple elements are constructed according to each indicator of at least one first network indicator, at least one second network indicator and at least one traffic indicator. Figure 4 , Figure 4 Schematic diagram of the matrix constructed by each indicator provided in the embodiment of the present application. It is assumed that the indicators include indicators such as a1, a2, a3, and a4, and multiple elements such as a11, a12, and a13 can be constructed through these indicators.

[0167] As can be seen from the above, each element is actually the score corresponding to each two indicators. Therefore, a judgment matrix can be generated based on each element and the score value corresponding to each element. In the judgment matrix, each element has a corresponding value. The judgment matrix can be expressed as:

[0168] , where A represents a judgment matrix, which contains multiple elements. Each element has a corresponding score value.

[0169] In some embodiments, determining the subjective weight value corresponding to each indicator according to the judgment matrix includes:

[0170] (1.2.1) Normalize the elements in the judgment matrix to obtain the target element value corresponding to each element;

[0171] (1.2.2) Generate the target matrix based on the element and target element values;

[0172] (1.2.3) Add the values ​​of the row elements corresponding to each indicator in the target matrix to obtain the initial weight corresponding to each indicator;

[0173] (1.2.4) Normalize the initial weights to obtain the subjective weight value corresponding to each indicator.

[0174] Among them, the elements in the judgment matrix can be normalized to obtain the target element value corresponding to each element. For example, each column element of the judgment matrix can be normalized to achieve normalization of the score value of each element and obtain the target element value corresponding to each element.

[0175] Then, the target matrix is ​​generated based on the elements and the target element values. For example, the value of each element is determined to be the corresponding target element value. Thus, the target matrix is ​​obtained.

[0176] Then, add the values ​​of the row elements corresponding to each indicator in the target matrix to obtain the initial weight corresponding to each indicator. For example, if there are n elements in a row corresponding to indicator a1, the target element values ​​of these n elements can be added to obtain the initial weight corresponding to indicator a1. Similarly, the initial weight corresponding to each indicator can be determined in this way.

[0177] Finally, the initial weight corresponding to each indicator is normalized to obtain the subjective weight value corresponding to each indicator. For example, the initial weight corresponding to each indicator is normalized to obtain the subjective weight value corresponding to each indicator. The subjective weight value corresponding to each indicator can be expressed as , Indicates the subjective weight value of each indicator.

[0178] The advantage of this is that experts can use their domain knowledge to make subjective scores, thereby determining the subjective weight value corresponding to each indicator.

[0179] In some embodiments, after adding the element values ​​of the row elements corresponding to each indicator in the target matrix to obtain the initial weight corresponding to each indicator, the method further includes:

[0180] (2.1) Determine the maximum eigenvalue corresponding to the target matrix based on the initial weights;

[0181] (2.2) Perform consistency check on the target matrix based on the maximum eigenvalue to obtain the consistency check result;

[0182] (2.3) When the consistency check result is less than the preset consistency check value, the initial weight is normalized to obtain the subjective weight value corresponding to each indicator.

[0183] Among them, the maximum eigenvalue corresponding to the target matrix is ​​determined based on the initial weight. The maximum eigenvalue is actually the maximum characteristic root corresponding to the target matrix. Then, the target matrix is ​​checked for consistency based on the maximum eigenvalue to obtain the consistency check result. For example, it can be calculated as follows:

[0184] .in, Represents the consistency index (Consistency Index), is the maximum eigenvalue, and n is the order of the matrix.

[0185] Among them, CR represents the consistency ratio (Consistency Ratio), RI is the random consistency index, and its value is determined by the matrix order.

[0186] When the consistency check result is less than the preset consistency check value, the initial weights are normalized to obtain the subjective weight values ​​corresponding to each indicator. The preset consistency check value is 0.1. When CR < 0.1, the consistency of the judgment matrix is ​​considered acceptable. The initial weights are normalized to obtain the subjective weight values ​​corresponding to each indicator. If CR ≥ 0.1, it is necessary to revise the expert scores between each two indicators and adjust the judgment matrix until the consistency requirements are met.

[0187] The advantage of this is that the consistency of the judgment matrix can be judged, thereby determining whether the expert's scoring is standard, and ensuring the accuracy of the subjective weight value of each indicator.

[0188] Step 250: Determine the information entropy corresponding to each of the at least one first network indicator, the at least one second network indicator, and the at least one traffic indicator, and determine the objective weight value corresponding to each indicator based on the information entropy.

[0189] Each of the at least one first network indicator, the at least one second network indicator, and the at least one traffic indicator has an indicator value. A first matrix can be generated based on the indicator value, and then the first matrix can be processed to determine the information entropy corresponding to each indicator. Finally, the objective weight value corresponding to each indicator can be determined based on the information entropy.

[0190] The objective weight value can reflect the impact of each indicator on this traffic simulation from the indicator value corresponding to each indicator itself.

[0191] In some embodiments, determining the information entropy corresponding to each of the at least one first network indicator, the at least one second network indicator, and the at least one traffic indicator includes:

[0192] (1.1) generating a first matrix based on an indicator value corresponding to each of the at least one first network indicator, the at least one second network indicator, and the at least one traffic indicator and each indicator;

[0193] (1.2) determining a normalized positive index value and a normalized negative index value of each element in the first matrix according to the maximum element value and the minimum element value in the first matrix;

[0194] (1.3) Determine the information entropy corresponding to each indicator based on the normalized values ​​of positive indicators and negative indicators.

[0195] A first matrix can be generated based on the indicator value corresponding to each of the at least one first network indicator, the at least one second network indicator, and the at least one traffic indicator, and each indicator. A matrix can be constructed based on each indicator, wherein the matrix includes multiple elements, and the element value of each element can be determined based on the indicator value corresponding to each indicator. Then, the first matrix is ​​generated based on the indicator value corresponding to each indicator and each indicator.

[0196] Then, the normalized positive index value and the normalized negative index value of each element in the first matrix are determined based on the maximum element value and the minimum element value in the first matrix. The specific calculation method is as follows:

[0197] .in represents the normalized value of the positive indicator, represents the maximum element value in the first matrix, represents the minimum element value in the first matrix, For the The indicator in The value in the scheme.

[0198] .in, Indicates the normalized value of negative indicators, represents the maximum element value in the first matrix, represents the minimum element value in the first matrix, For the The indicator in The value in the scheme.

[0199] Then, the information entropy corresponding to each indicator is determined based on the normalized values ​​of the positive and negative indicators. For example, after obtaining the normalized values ​​of the positive and negative indicators for each element, a second matrix can be constructed, and then the numerical proportion of the jth indicator to the ith solution can be determined in the second matrix. The specific calculation method is as follows:

[0200] .in, It represents the numerical ratio of the jth indicator to the ith solution. Then the information entropy corresponding to each indicator is calculated based on the numerical ratio. The specific calculation method is as follows:

[0201] ,in represents the information entropy of each indicator, n is the number of evaluation objects, and m is the total number of indicators.

[0202] The advantage of doing this is that the information entropy of each indicator can be determined more objectively.

[0203] In some implementations, determining the objective weight value corresponding to each indicator based on information entropy includes:

[0204] (2.1) Subtract the objective weight value corresponding to each indicator from the preset value to obtain the first result corresponding to each indicator;

[0205] (2.2) Add the first results corresponding to each indicator to obtain the second result;

[0206] (2.3) Divide the first result corresponding to each indicator by the second result to obtain the objective weight value corresponding to each indicator.

[0207] The specific calculation method is as follows:

[0208] Among them, the preset value is 1, and the first result is , the second result is , m is the number of indicators, The objective weight value corresponding to each indicator.

[0209] The advantage of doing this is that the objective weight value of each indicator can be accurately determined through the information entropy of each indicator.

[0210] Step 260: Obtain the normalized index value corresponding to each index, and determine the traffic simulation credibility evaluation value according to the subjective weight value, objective weight value and normalized index value corresponding to each index.

[0211] Each indicator corresponds to an indicator value, and the indicator value corresponding to each indicator can be normalized to obtain a normalized indicator value corresponding to each indicator.

[0212] Then, the credibility evaluation value of the traffic simulation is determined based on the subjective weight value, objective weight value and normalized index value corresponding to each indicator. For example, the fusion weight value of each indicator is first determined, and the calculation method is as follows:

[0213] .in, is the credibility evaluation value of indicator j, is the subjective weight value of indicator j, is the objective weight value of indicator j, is the weighting coefficient between the subjective weight value and the objective weight value, which usually ranges from 0.4 to 0.6. In the present invention, it is preferably 0.5, that is, the subjective and objective weights are equally important.

[0214] Then, the fusion weight value of each indicator is multiplied by the normalized indicator value corresponding to each indicator to obtain the evaluation result corresponding to each indicator. Finally, the evaluation results of each indicator are added together to obtain the traffic simulation credibility evaluation value corresponding to this traffic simulation. The specific calculation method is as follows:

[0215] Where D is the credibility evaluation value of traffic simulation, m is the number of indicators, is the normalized index value of index j.

[0216] After determining the traffic simulation credibility assessment value corresponding to this traffic simulation, it can help researchers understand the traffic situation that integrates network security and traffic parameters.

[0217] In the present application, the traffic network simulation environment includes a micro-traffic layer, a network topology layer and a network communication layer. The first network information corresponding to the network communication layer in the traffic simulation process is obtained, and at least one first network indicator is determined based on the first network information; the second network information corresponding to the network topology layer in the traffic simulation process is obtained, and at least one second network indicator is determined based on the second network information; the traffic information corresponding to the micro-traffic layer in the traffic simulation process is obtained, and at least one traffic indicator is determined based on the traffic information; the scores corresponding to each two indicators among at least one first network indicator, at least one second network indicator and at least one traffic indicator are obtained, and a hierarchical analysis is performed based on the scores to determine the subjective weight value corresponding to each indicator; the information entropy corresponding to each indicator among at least one first network indicator, at least one second network indicator and at least one traffic indicator is determined, and the objective weight value corresponding to each indicator is determined based on the information entropy; the normalized index value corresponding to each indicator is obtained, and the traffic simulation credibility evaluation value is determined based on the subjective weight value, objective weight value and normalized index value corresponding to each indicator.

[0218] In this way, the present application first constructs a network-related traffic network simulation environment by combining the micro-traffic layer with the network topology layer and the network communication layer, thereby taking into account network security and traffic flow parameters to obtain more accurate traffic simulation results. Secondly, by obtaining the first network information of the network communication layer and determining at least one first network indicator through the first network information, by obtaining the second network information of the network topology layer and determining at least one second network indicator through the second network information, then by obtaining the traffic information of the micro-traffic layer and determining at least one traffic indicator through the traffic information, finally, combining the first network indicator, the second network indicator and the traffic indicator, determining the score corresponding to each indicator, and performing hierarchical analysis based on the score to determine the subjective weight value corresponding to each indicator. Then, determine the information entropy corresponding to each indicator, and determine the objective weight value corresponding to each indicator based on the information entropy. Finally, obtain the normalized index value corresponding to each indicator, and determine the traffic simulation credibility evaluation value based on the subjective weight value, objective weight value and normalized index value corresponding to each indicator. In this way, by combining the first network indicator, the second network indicator, and the traffic indicator, the credibility of the traffic simulation can be accurately assessed. By introducing both subjective and objective weights for each indicator, the credibility assessment value of the traffic simulation can be more accurately determined. Compared to related art solutions that focus solely on traffic flow parameters and individual vehicle behavior characteristics, this application combines various indicators for a comprehensive assessment, thereby determining the credibility assessment value corresponding to the traffic simulation.

[0219] See also Figure 5 , Figure 5FIG. 5 is another flow chart of a traffic simulation credibility assessment method provided in an embodiment of the present application. The traffic simulation credibility assessment method may include the following steps:

[0220] Step 301: Acquire first network information corresponding to a network communication layer during a traffic simulation process, and determine at least one first network indicator based on the first network information;

[0221] Step 302: Obtain second network information corresponding to the network topology layer during the traffic simulation process, and determine at least one second network indicator based on the second network information;

[0222] Step 303: Obtain traffic information corresponding to the microscopic traffic layer during the traffic simulation process, and determine at least one traffic indicator based on the traffic information;

[0223] Step 304: construct multiple elements based on each of the at least one first network indicator, the at least one second network indicator, and the at least one traffic indicator;

[0224] Step 305: Generate a judgment matrix based on each element and the score value corresponding to each element, normalize the elements in the judgment matrix, and obtain the target element value corresponding to each element;

[0225] Step 306: Generate a target matrix based on the element and the target element value, and add the element values ​​of the row elements corresponding to each indicator in the target matrix to obtain the initial weight corresponding to each indicator;

[0226] Step 307: Normalize the initial weights to obtain the subjective weight value corresponding to each indicator;

[0227] Step 308: Generate a first matrix based on the indicator value corresponding to each indicator of at least one first network indicator, at least one second network indicator, and at least one traffic indicator;

[0228] Step 309: Determine the normalized positive index value and the normalized negative index value of each element in the first matrix according to the maximum element value and the minimum element value in the first matrix;

[0229] Step 310: Determine the information entropy corresponding to each indicator based on the normalized value of the positive indicator and the normalized value of the negative indicator;

[0230] Step 311: Subtract the objective weight value corresponding to each indicator from the preset value to obtain a first result corresponding to each indicator;

[0231] Step 312: Add the first results corresponding to each indicator to obtain a second result, and divide the first result corresponding to each indicator by the second result to obtain an objective weight value corresponding to each indicator;

[0232] Step 313: Obtain the normalized index value corresponding to each index, and determine the traffic simulation credibility evaluation value according to the subjective weight value, objective weight value and normalized index value corresponding to each index.

[0233] In the above embodiments, the description of each embodiment has its own focus. For the part not described in detail in a certain embodiment, please refer to the detailed description of the traffic simulation credibility assessment method, which will not be repeated here.

[0234] See also Figure 6 , Figure 6 It is a structural diagram of the traffic simulation credibility assessment device provided in an embodiment of the present application. The traffic simulation credibility assessment device can execute the above-mentioned traffic simulation credibility assessment method. In the embodiment of the present application, the term "module" or "unit" refers to a computer program or a part of a computer program with a predetermined function, and works together with other related parts to achieve a predetermined goal, and can be fully or partially implemented by using software, hardware (such as processing circuits or memories) or a combination thereof. Similarly, a processor (or multiple processors or memories) can be used to implement one or more modules or units. In addition, each module or unit can be part of an overall module or unit that includes the function of the module or unit.

[0235] In one aspect, an embodiment of the present application provides a traffic simulation credibility assessment device 400, which is applied to a traffic network simulation environment. The traffic network simulation environment includes a microscopic traffic layer, a network topology layer, and a network communication layer. The device includes:

[0236] A first acquisition module 410 is configured to acquire first network information corresponding to a network communication layer during a traffic simulation process, and determine at least one first network indicator based on the first network information;

[0237] A second acquisition module 420 is configured to acquire second network information corresponding to the network topology layer during the traffic simulation process, and determine at least one second network indicator based on the second network information;

[0238] The third acquisition module 430 is used to obtain traffic information corresponding to the microscopic traffic layer during the traffic simulation process and determine at least one traffic indicator based on the traffic information;

[0239] A first determination module 440 is configured to obtain scores corresponding to each two of the at least one first network indicator, the at least one second network indicator, and the at least one traffic indicator, and to perform a hierarchical analysis based on the scores to determine a subjective weight value corresponding to each indicator;

[0240] A second determination module 450 is configured to determine information entropy corresponding to each of the at least one first network indicator, the at least one second network indicator, and the at least one traffic indicator, and determine an objective weight value corresponding to each indicator based on the information entropy;

[0241] The evaluation module 460 is used to obtain the normalized index value corresponding to each index, and determine the traffic simulation credibility evaluation value according to the subjective weight value, objective weight value and normalized index value corresponding to each index.

[0242] In some embodiments, the first network information includes the number of nodes, the timestamp of the node receiving data, and the timestamp of the node forwarding data, and the first network indicator includes the node processing delay; the first acquisition module 410 is configured to:

[0243] Subtract the node forwarding data timestamp from the node receiving data timestamp of each node to obtain the receiving delay of each node;

[0244] The node processing delay is obtained by adding up the receiving delay of each node and dividing it by the number of nodes.

[0245] In some embodiments, the first network information includes data transmission delay, average data transmission delay, and number of data packets, and the first network indicator includes delay jitter; the first acquisition module 410 is configured to:

[0246] Subtract the average data transmission delay from the data transmission delay of each data packet to obtain the transmission delay difference corresponding to each data packet;

[0247] The delay jitter is calculated by adding the transmission delay differences corresponding to each data packet and dividing by the number of data packets.

[0248] In some embodiments, traffic information includes signal light cycle duration, lane saturation flow rate, and green light signal duration, and traffic indicators include lane capacity. The third acquisition module 430 is configured to:

[0249] Divide the green light signal duration by the signal light cycle duration to obtain the green light signal ratio;

[0250] The lane capacity is obtained by multiplying the green light signal ratio by the lane saturation flow rate.

[0251] In some embodiments, traffic information includes vehicle arrival rate, vehicle departure rate, and queue duration, and traffic indicators include vehicle queue length; the third acquisition module 430 is configured to:

[0252] The vehicle retention rate is obtained by subtracting the vehicle departure rate from the vehicle arrival rate;

[0253] The vehicle queue length is obtained by multiplying the vehicle retention rate by the queue duration.

[0254] In some implementations, the first determining module 440 is configured to:

[0255] constructing a plurality of elements based on each of the at least one first network indicator, the at least one second network indicator, and the at least one traffic indicator;

[0256] A judgment matrix is ​​generated based on each element and the score value corresponding to each element, and the subjective weight value corresponding to each indicator is determined based on the judgment matrix.

[0257] In some implementations, the first determining module 440 is configured to:

[0258] Normalize the elements in the judgment matrix to obtain the target element value corresponding to each element;

[0259] Generate target matrix based on element and target element value;

[0260] Add the element values ​​of the row elements corresponding to each indicator in the target matrix to obtain the initial weight corresponding to each indicator;

[0261] The initial weights are normalized to obtain the subjective weight value corresponding to each indicator.

[0262] In some implementations, the first determining module 440 is configured to:

[0263] After adding the element values ​​of the row elements corresponding to each indicator in the target matrix to obtain the initial weight corresponding to each indicator, the maximum eigenvalue corresponding to the target matrix is ​​determined according to the initial weight;

[0264] Perform consistency check on the target matrix according to the maximum eigenvalue to obtain the consistency check result;

[0265] When the consistency check result is less than the preset consistency check value, the initial weight is normalized to obtain the subjective weight value corresponding to each indicator.

[0266] In some implementations, the second determining module 450 is configured to:

[0267] generating a first matrix according to an indicator value corresponding to each indicator of at least one first network indicator, at least one second network indicator, and at least one traffic indicator and each indicator;

[0268] Determine the positive index normalized value and the negative index normalized value of each element in the first matrix according to the maximum element value and the minimum element value in the first matrix;

[0269] Perform negative index normalization on each element in the first matrix according to the minimum element value in the first matrix;

[0270] The information entropy corresponding to each indicator is determined based on the normalized values ​​of positive indicators and negative indicators.

[0271] In some implementations, the second determining module 450 is configured to:

[0272] Subtract the objective weight value corresponding to each indicator from the preset value to obtain the first result corresponding to each indicator;

[0273] Add the first results corresponding to each indicator to obtain the second result;

[0274] Divide the first result corresponding to each indicator by the second result to obtain the objective weight value corresponding to each indicator.

[0275] In the present application, a first acquisition module 410 is used to obtain first network information corresponding to the network communication layer during the traffic simulation process, and determine at least one first network indicator based on the first network information; a second acquisition module 420 is used to obtain second network information corresponding to the network topology layer during the traffic simulation process, and determine at least one second network indicator based on the second network information; a third acquisition module 430 is used to obtain traffic information corresponding to the micro-traffic layer during the traffic simulation process, and determine at least one traffic indicator based on the traffic information; a first determination module 440 is used to obtain the corresponding scores between each two indicators among at least one first network indicator, at least one second network indicator, and at least one traffic indicator, and determine the subjective weight value corresponding to each indicator through hierarchical analysis based on the scores; a second determination module 450 is used to determine the information entropy corresponding to each indicator among at least one first network indicator, at least one second network indicator, and at least one traffic indicator, and determine the objective weight value corresponding to each indicator based on the information entropy; an evaluation module 460 is used to obtain the normalized index value corresponding to each indicator, and determine the traffic simulation credibility evaluation value based on the subjective weight value, objective weight value, and normalized index value corresponding to each indicator.

[0276] In this way, the present application first constructs a network-related traffic network simulation environment by combining the micro-traffic layer with the network topology layer and the network communication layer, thereby taking into account network security and traffic flow parameters to obtain more accurate traffic simulation results. Secondly, by obtaining the first network information of the network communication layer and determining at least one first network indicator through the first network information, by obtaining the second network information of the network topology layer and determining at least one second network indicator through the second network information, then by obtaining the traffic information of the micro-traffic layer and determining at least one traffic indicator through the traffic information, finally, combining the first network indicator, the second network indicator and the traffic indicator, determining the score corresponding to each indicator, and performing hierarchical analysis based on the score to determine the subjective weight value corresponding to each indicator. Then, determine the information entropy corresponding to each indicator, and determine the objective weight value corresponding to each indicator based on the information entropy. Finally, obtain the normalized index value corresponding to each indicator, and determine the traffic simulation credibility evaluation value based on the subjective weight value, objective weight value and normalized index value corresponding to each indicator. In this way, by combining the first network indicator, the second network indicator, and the traffic indicator, the credibility of the traffic simulation can be accurately assessed. By introducing both subjective and objective weights for each indicator, the credibility assessment value of the traffic simulation can be more accurately determined. Compared to related art solutions that focus solely on traffic flow parameters and individual vehicle behavior characteristics, this application combines various indicators for a comprehensive assessment, thereby determining the credibility assessment value corresponding to the traffic simulation.

[0277] In the above embodiments, the description of each embodiment has its own focus. For the part not described in detail in a certain embodiment, please refer to the detailed description of the traffic simulation credibility assessment method, which will not be repeated here.

[0278] The present application also provides a computer device comprising a memory and a processor. The memory stores a computer program, and the processor implements the aforementioned method XXX when executing the computer program. The computer device can be any intelligent terminal, such as a tablet computer or an in-vehicle computer.

[0279] See also Figure 7 , Figure 7 The hardware structure of a computer device according to another embodiment is shown. The computer device includes:

[0280] The processor 501 may be implemented as a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.

[0281] The memory 502 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 502 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 502 and is called by the processor 501 to execute the XXX method of the embodiments of this application.

[0282] Input / output interface 503, used to implement information input and output;

[0283] Communication interface 504, used to implement communication interaction between this device and other devices, which can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WiFi, Bluetooth, etc.);

[0284] Bus 505 , which transmits information between various components of the device (e.g., processor 501 , memory 502 , input / output interface 503 , and communication interface 504 );

[0285] The processor 501 , the memory 502 , the input / output interface 503 and the communication interface 504 are connected to each other in communication within the device via a bus 505 .

[0286] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned traffic simulation credibility assessment method is implemented.

[0287] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0288] The embodiments of the present application provide a traffic simulation credibility assessment method, a traffic simulation credibility assessment device, a computer device and a storage medium. The traffic network simulation environment includes a micro-traffic layer, a network topology layer and a network communication layer. The method obtains first network information corresponding to the network communication layer during the traffic simulation process and determines at least one first network indicator based on the first network information; obtains second network information corresponding to the network topology layer during the traffic simulation process and determines at least one second network indicator based on the second network information; obtains traffic information corresponding to the micro-traffic layer during the traffic simulation process and determines at least one traffic indicator based on the traffic information; obtains scores corresponding to each two indicators among at least one first network indicator, at least one second network indicator and at least one traffic indicator, and determines the subjective weight value corresponding to each indicator through hierarchical analysis based on the scores; determines the information entropy corresponding to each indicator among at least one first network indicator, at least one second network indicator and at least one traffic indicator, and determines the objective weight value corresponding to each indicator based on the information entropy; obtains a normalized index value corresponding to each indicator, and determines the traffic simulation credibility assessment value based on the subjective weight value, objective weight value and normalized index value corresponding to each indicator.

[0289] In this way, the present application first constructs a network-related traffic network simulation environment by combining the micro-traffic layer with the network topology layer and the network communication layer, thereby taking into account network security and traffic flow parameters to obtain more accurate traffic simulation results. Secondly, by obtaining the first network information of the network communication layer and determining at least one first network indicator through the first network information, by obtaining the second network information of the network topology layer and determining at least one second network indicator through the second network information, then by obtaining the traffic information of the micro-traffic layer and determining at least one traffic indicator through the traffic information, finally, combining the first network indicator, the second network indicator and the traffic indicator, determining the score corresponding to each indicator, and performing hierarchical analysis based on the score to determine the subjective weight value corresponding to each indicator. Then, determine the information entropy corresponding to each indicator, and determine the objective weight value corresponding to each indicator based on the information entropy. Finally, obtain the normalized index value corresponding to each indicator, and determine the traffic simulation credibility evaluation value based on the subjective weight value, objective weight value and normalized index value corresponding to each indicator. In this way, by combining the first network indicator, the second network indicator, and the traffic indicator, the credibility of the traffic simulation can be accurately assessed. By introducing both subjective and objective weights for each indicator, the credibility assessment value of the traffic simulation can be more accurately determined. Compared to related art solutions that focus solely on traffic flow parameters and individual vehicle behavior characteristics, this application combines various indicators for a comprehensive assessment, thereby determining the credibility assessment value corresponding to the traffic simulation.

[0290] The embodiments described in the embodiments of this application are intended to more clearly illustrate the technical solutions of the embodiments of this application and do not constitute a limitation on the technical solutions provided by the embodiments of this application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0291] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0292] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0293] Those skilled in the art will appreciate that all or some of the steps in the methods, systems, and functional modules / units in the devices disclosed above may be implemented as software, firmware, hardware, or appropriate combinations thereof.

[0294] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0295] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0296] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the above-mentioned units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0297] The units described above as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0298] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0299] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes multiple instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: various media that can store programs, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0300] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but are not intended to limit the scope of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and essence of the present invention should be within the scope of the present invention.

Claims

1. A traffic simulation credibility assessment method, characterized in that: Applied to a traffic network simulation environment, the traffic network simulation environment includes a microscopic traffic layer, a network topology layer, and a network communication layer, the method includes: Acquire first network information corresponding to the network communication layer during the traffic simulation process, and determine at least one first network indicator based on the first network information; The first network information includes the number of nodes, the timestamp of node data reception, and the timestamp of node data forwarding, and the first network indicator includes the node processing delay; determining at least one first network indicator based on the first network information includes: subtracting the timestamp of node data forwarding from the timestamp of node data reception of each node to obtain the reception delay of each node; adding the reception delays of each node and dividing the sum by the number of nodes to obtain the node processing delay; Acquire second network information corresponding to the network topology layer during the traffic simulation process, and determine at least one second network indicator based on the second network information; Acquiring traffic information corresponding to the microscopic traffic layer during traffic simulation, and determining at least one traffic indicator based on the traffic information; Obtaining a score corresponding to each two of the at least one first network indicator, the at least one second network indicator, and the at least one traffic indicator; constructing a plurality of elements based on each of the at least one first network indicator, the at least one second network indicator, and the at least one traffic indicator; Generating a judgment matrix according to each element and the score value corresponding to each element, and determining the subjective weight value corresponding to each indicator according to the judgment matrix; Determining information entropy corresponding to each of the at least one first network indicator, the at least one second network indicator, and the at least one traffic indicator; Subtract the objective weight value corresponding to each indicator from the preset value to obtain a first result corresponding to each indicator; Adding the first results corresponding to each indicator to obtain a second result; Divide the first result corresponding to each indicator by the second result to obtain an objective weight value corresponding to each indicator; A normalized index value corresponding to each of the indicators is obtained, and a traffic simulation credibility evaluation value is determined according to the subjective weight value, the objective weight value, and the normalized index value corresponding to each of the indicators.

2. The traffic simulation credibility evaluation method according to claim 1, characterized in that: The first network information includes data transmission delay, average data transmission delay, and number of data packets, and the first network indicator includes delay jitter; and determining at least one first network indicator based on the first network information includes: Subtract the average data transmission delay from the data transmission delay corresponding to each data packet to obtain the transmission delay difference corresponding to each data packet; The transmission delay difference corresponding to each data packet is added and divided by the number of data packets to obtain the delay jitter.

3. The traffic simulation credibility evaluation method according to claim 1, characterized in that: The traffic information includes a signal light cycle duration, a lane saturation flow rate, and a green light signal duration; the traffic index includes a lane capacity; and determining at least one traffic index based on the traffic information includes: Divide the green light signal duration by the signal light cycle duration to obtain the green light signal ratio; The lane capacity is obtained by multiplying the green light signal ratio by the lane saturation flow rate.

4. The traffic simulation credibility evaluation method according to claim 1, characterized in that: The traffic information includes a vehicle arrival rate, a vehicle departure rate, and a queue duration; the traffic indicator includes a vehicle queue length; and determining at least one traffic indicator based on the traffic information includes: The vehicle retention rate is obtained by subtracting the vehicle departure rate from the vehicle arrival rate; The vehicle queue length is obtained by multiplying the vehicle retention rate by the queue duration.

5. The traffic simulation credibility evaluation method according to claim 1, characterized in that: Determining the subjective weight value corresponding to each indicator according to the judgment matrix includes: Normalizing the elements in the judgment matrix to obtain the target element value corresponding to each element; Generate a target matrix according to the element and the target element value; Adding the element values ​​of the row elements corresponding to each indicator in the target matrix to obtain the initial weight corresponding to each indicator; The initial weights are normalized to obtain the subjective weight values ​​corresponding to each indicator.

6. The traffic simulation credibility evaluation method according to claim 5, characterized in that: After adding the element values ​​of the row elements corresponding to each indicator in the target matrix to obtain the initial weight corresponding to each indicator, the method further includes: Determine the maximum eigenvalue corresponding to the target matrix according to the initial weight; Performing a consistency check on the target matrix according to the maximum eigenvalue to obtain a consistency check result; Normalizing the initial weights to obtain the subjective weight values ​​corresponding to each indicator includes: When the consistency check result is less than a preset consistency check value, the initial weight is normalized to obtain a subjective weight value corresponding to each indicator.

7. The traffic simulation credibility evaluation method according to claim 1, characterized in that: The determining of the information entropy corresponding to each of the at least one first network indicator, the at least one second network indicator, and the at least one traffic indicator includes: Generate a first matrix according to the index value corresponding to each of the at least one first network index, the at least one second network index, and the at least one traffic index and each of the indicators; Determine a normalized positive index value and a normalized negative index value of each element in the first matrix according to the maximum element value and the minimum element value in the first matrix; The information entropy corresponding to each indicator is determined according to the normalized value of the positive indicator and the normalized value of the negative indicator.

8. A traffic simulation credibility assessment device, characterized in that: Applied to a traffic network simulation environment, the traffic network simulation environment includes a microscopic traffic layer, a network topology layer, and a network communication layer, and the device includes: A first acquisition module is configured to acquire first network information corresponding to the network communication layer during a traffic simulation process, and determine at least one first network indicator based on the first network information; The first network information includes the number of nodes, the timestamp of node data reception, and the timestamp of node data forwarding, and the first network indicator includes the node processing delay; determining at least one first network indicator based on the first network information includes: subtracting the timestamp of node data forwarding from the timestamp of node data reception of each node to obtain the reception delay of each node; adding the reception delays of each node and dividing the sum by the number of nodes to obtain the node processing delay; A second acquisition module is used to obtain second network information corresponding to the network topology layer during the traffic simulation process, and determine at least one second network indicator based on the second network information; A third acquisition module is used to obtain traffic information corresponding to the microscopic traffic layer during the traffic simulation process, and determine at least one traffic indicator based on the traffic information; A first determination module is configured to obtain a score corresponding to each two of the at least one first network indicator, the at least one second network indicator, and the at least one traffic indicator; constructing a plurality of elements based on each of the at least one first network indicator, the at least one second network indicator, and the at least one traffic indicator; Generating a judgment matrix according to each element and the score value corresponding to each element, and determining the subjective weight value corresponding to each indicator according to the judgment matrix; A second determination module is configured to determine the information entropy corresponding to each of the at least one first network indicator, the at least one second network indicator, and the at least one traffic indicator; Subtract the objective weight value corresponding to each indicator from the preset value to obtain a first result corresponding to each indicator; Adding the first results corresponding to each indicator to obtain a second result; Divide the first result corresponding to each indicator by the second result to obtain an objective weight value corresponding to each indicator; The evaluation module is used to obtain the normalized index value corresponding to each indicator, and determine the traffic simulation credibility evaluation value according to the subjective weight value, the objective weight value and the normalized index value corresponding to each indicator.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor to execute the traffic simulation credibility assessment method according to any one of claims 1 to 7.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: When the processor executes the computer program, the traffic simulation credibility assessment method according to any one of claims 1 to 7 is implemented.

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