Microscopic 3D simulation method and system for macro-micro integration of composite transportation networks

Abstract

The present invention discloses a microscopic three-dimensional simulation method and system for macro-micro integration of a composite transportation network, and relates to the technical field of transportation simulation. The present invention includes: the construction of a real-time linked global dynamic transportation scene; the fusion and calibration of multi-source transportation intelligent body parameters; a front-end and back-end two-way integrated communication rendering module; the development of a composite transportation network scene reproduction analysis response dynamic visualization demonstration interaction and evaluation system, and full-view simulation video stream archiving and revisit analysis. Based on the macro-micro integration of a composite transportation network, the present invention completes the construction of a microscopic three-dimensional simulation system through the invented improved centralized learning-distributed execution deep multi-source intelligent body algorithm, multi-dimensional calibration of microscopic simulation model parameters, microscopic traffic flow individual interaction model, and microscopic traffic evaluation index calculation system, providing a reliable and practical means for urban traffic three-dimensional simulation from a microscopic perspective.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic simulation, and in particular to a microscopic three-dimensional simulation method and system for macro-micro integration of a composite traffic network. Background Art

[0002] With the continuous improvement of the social economy, the improvement of residents' living conditions, and the booming new energy vehicle industry, the number of motor vehicles and travel demand have increased significantly. Most regions in my country are facing urban traffic congestion to varying degrees. Urban traffic conditions are complex and prone to emergencies. Various traffic conditions frequently occur, such as inclement weather, road construction, isolated ramp bottlenecks, active intervention traffic accident risk scenarios, traffic flow fluctuations at bottlenecks, high truck traffic ratios, occasional and frequent consecutive ramp bottlenecks, large-scale emergency incidents, and emergency lane management. These traffic scenarios can cause severe and widespread congestion, significantly impacting urban transportation functions and residents' daily travel.

[0003] Traffic simulation technology can simulate the traffic congestion conditions that often occur in cities, restoring traffic conditions in a low-cost, low-manpower, and highly efficient manner. It provides technical support for cities to specify traffic management and control strategies and is an indispensable part of urban traffic congestion management. However, current microscopic three-dimensional traffic simulation technology relies on a single special scenario to calibrate parameters of a specific single data type, focusing on the behavior of individual vehicles while failing to fully consider the interactions between vehicles. In addition, it lacks a systematic microscopic traffic evaluation index calculation system and cannot effectively adapt to the needs of different traffic scenarios, resulting in reduced reliability and accuracy of simulation results and a lack of scientific basis for the formulation of traffic management and control strategies.

[0004] To this end, we propose a microscopic three-dimensional simulation method and system for complex transportation networks that integrates macro and micro aspects. Summary of the Invention

[0005] The purpose of the present invention is to provide a microscopic three-dimensional simulation method and system for a composite transportation network that integrates macro and micro aspects, so as to solve the problems raised in the above-mentioned background technology.

[0006] According to a first aspect of the present invention, to achieve the above-mentioned purpose, the present invention provides the following technical solution: a microscopic three-dimensional simulation method for a composite transportation network with macro-micro integration, specifically comprising the following steps:

[0007] Build a real-time, globally dynamic traffic scenario: Establish a real-time, globally dynamic traffic scenario interface. Based on the high-precision GIS map information platform API, develop a high-precision, editable 3D traffic scenario twin module. By online editing of weather and road models, add the dimension of time change to the 3D space to build a 4D traffic scenario model platform.

[0008] Multi-source traffic agent parameter fusion calibration: Construct control unit agent, traveler agent, road sensor agent and communication unit agent, and through the collaborative information collection and transmission of multiple agents, process the actual collected multi-source micro-travel data, perform gradient updates, and realize multi-dimensional calibration of micro-simulation model parameters through the multi-dimensional fusion algorithm matrix of traffic scene parameters.

[0009] Front-end and back-end bidirectional integrated communication rendering module: Based on the mature API of the micro-simulation platform, a front-end and back-end bidirectional integrated data communication module is developed to achieve simultaneous real-time rendering of vehicle trajectories and micro-simulation results in the 3D scene while realizing macro-simulation evolution;

[0010] Complex traffic network scenario recurrence analysis and response: For large-scale complex traffic network scenarios, a real-time model and rapid analysis and calculation model for typical urban road network traffic scenarios are constructed to achieve timely response of management and control strategies at the micro level;

[0011] Development of a dynamic visualization demonstration interaction and evaluation system: Based on a front-end and back-end two-way integrated communication rendering module, a micro-traffic evaluation index calculation system is constructed based on the principal component fuzzy fusion algorithm to achieve visualization of simulation analysis results on the screen;

[0012] Full-view simulation video stream archiving and revisit analysis: While the simulation is in progress, key data is stored in the background for revisit analysis.

[0013] Furthermore, the global dynamic traffic scene interface is linked in real time, and the time change dimension is added on the basis of three-dimensional space to build a four-dimensional traffic scene model platform. The details are as follows:

[0014] (11) Linking with the GIS map platform API, it can perform high-precision real-time conversion and positioning of the spatial geographic location information of the road property model surrounding the road scene, where the road property model includes roadside green facilities, buildings, signs and markings, and roadside equipment;

[0015] (12) By specifying the model size, border, and color multi-dimensional information through parameters, the full traffic scene model itself is digitally and dynamically defined;

[0016] (13) The interface supports seamless integration of real-time data streams and dynamic updates of the entire scene model module. Examples of dynamic scene updates include real-time changes to road alignment, real-time changes to road signs, and real-time addition or deletion of static obstacles.

[0017] Furthermore, the multi-source traffic agent parameter fusion calibration specifically includes the following:

[0018] (21) Constructing a signal control unit agent, a traveler agent, a road sensor agent, and a communication unit agent; the signal control unit agent is used to control traffic signals, the traveler agent is used to make intelligent predictions about traffic conditions based on historical traffic data, the road sensor agent is used to make intelligent and accurate collaborative perceptions of road traffic volume, and the communication unit agent is used to perform preliminary processing and transmission of traffic flow data;

[0019] (22) Through the collaborative information collection and transmission of multiple intelligent agents, the collected information is subjected to the improved centralized learning-distributed execution deep multi-source intelligent agent algorithm:

[0020] θ=(θ1,…,θ i ) represents the parameters of the signal control unit agent, traveler agent, road sensor agent, and communication unit agent. The gradient is updated through the strategy optimization function and gradient function of each traffic agent. The optimization function and its gradient function are:

[0021]

[0022] Where: i Represents the discount factor of each agent, which represents the degree of influence of each agent on the traffic conditions, usually between 0 and 1; r i,t Represents the reward function of each agent for improving traffic conditions at time t; β i The correction bias parameter representing the impact of each agent on the traffic situation; π i Represents the strategy of each agent; t i Represents the state value currently collected by each traffic agent; α=(t1,…,t i ) represents the state value vector collected by each traffic agent through centralized learning; a i,n Represents the nth control strategy action distributed and executed by each traffic agent; Represents the traffic impact-control strategy action function of each traffic agent under the improved centralized learning-distributed execution;

[0023] (23) Multi-dimensional calibration refers to the three dimensions of scene, vehicle type, and traffic flow parameters. The underlying layer selects various traffic conditions in complex urban traffic as scenes. The above traffic scenes realize the multi-dimensional calibration of micro-simulation model parameters through the multi-dimensional fusion algorithm matrix of traffic scene parameters;

[0024] Each scenario has corresponding types of vehicles. In complex urban traffic, there are many types of vehicles, including family cars, taxis, buses, trucks, trailers, and non-motorized vehicles. At the same time, each type of vehicle has different traffic parameters. Following, lane changing, and speed are selected as the matrix parameters of the last layer. The three traffic scenario parameters are multi-dimensionally integrated to form a set of traffic scenario parameter multi-dimensional fusion algorithm matrix:

[0025]

[0026]

[0027] micropra ij =[CF LC CS]

[0028] Among them, Microsim p is a multidimensional microscopic parameter matrix, scene n is the dimensional parameter matrix of a typical traffic scene, micropra ij is the vehicle model dimension parameter matrix under the typical traffic scenario dimension, CF is the vehicle following parameter model library, LC is the vehicle lane changing parameter model library, and CS is the vehicle speed model library.

[0029] Furthermore, various traffic conditions in complex urban traffic are selected as scenarios, which are specifically divided into severe weather scenarios, road construction scenarios, isolated ramp bottleneck section scenarios, active intervention traffic accident risk scenarios, bottleneck section traffic flow fluctuation scenarios, high truck ratio traffic scenarios, occasional continuous ramp multiple bottleneck section scenarios, frequent continuous ramp multiple bottleneck section scenarios, large-scale sudden emergency events and accidents, and emergency lane management scenarios.

[0030] Furthermore, the front-end and back-end bidirectional integrated communication rendering module uses JSON format data, specifically including the following:

[0031] Long polling technology is used to simulate real-time communication: the client sends a request to the server, and the server keeps the connection open until there is data to send to the client or the timeout occurs, thus achieving two-way communication;

[0032] Front-end implementation: Use JavaScript to send long polling requests to the server and process the data received from the server;

[0033] Backend implementation: Write code in Python to process requests from the frontend and send data to the frontend as needed. At the same time, set a timeout on the server side and send a response to the client when there is new data.

[0034] Message format and protocol: When transmitting data between the front-end and back-end, a unified message format and communication protocol is defined to ensure that the front-end and back-end can correctly parse and process the data.

[0035] Furthermore, a real-time model and a rapid analysis and calculation model for typical traffic scenarios in urban road networks are constructed, specifically including the following:

[0036] (41) Call the digital definition interface of the full traffic scene model network, link the GIS map platform to locate the scene construction content, and start updating the road three-dimensional modeling in real time;

[0037] (42) Use information collection equipment to monitor road conditions in real time, receive multi-source micro-travel data through the front-end and back-end two-way integrated communication rendering module, including road event type, event occurrence time, event generation space coordinates, and traffic flow scene information around the event occurrence point, and perform real-time rendering simulation of the traffic flow module;

[0038] (43) Form a fast computational analysis model for microscopic simulation to predict simulation results for management and control decisions.

[0039] Furthermore, the fast computational analysis model for microscopic simulation mainly includes microscopic traffic flow simulation. To reflect the interactions between vehicles and the diversity of driving behaviors, a microscopic traffic flow individual interaction model is constructed. Each vehicle is treated as an independent intelligent agent and simulated according to its individual characteristics and behavioral rules. The details are as follows:

[0040] (431) Individual state definition: Each vehicle is defined as an individual, and its state includes information such as position, speed, acceleration, lane position, etc. The state formula is as follows:

[0041] s i (t)=(x i (t),v i (t),a i (t),l i (t))

[0042] Where: x i (t) represents the position of vehicle i at time t, v i (t) represents speed, a i (t) represents acceleration, l i (t) represents the lane position;

[0043] (432) Individual behavior rules: define the situations in which the vehicle follows the speed of the preceding vehicle, maintains a safe following distance, and limits the maximum acceleration. In addition, lane position information is introduced into the behavior rule function and used to adjust the vehicle's acceleration. The formula for the individual behavior rule is as follows:

[0044] a i (t)=min(a max ,max(0,f desired (v i (t), Δx i (t))+f safe (Δv i (t),Δx i (t))+f lane (l i (t),l desired (i))))

[0045] Where a max is the maximum acceleration limit of the vehicle; Δv i (t) is the speed difference between vehicle i and the preceding vehicle at time t; Δx i (t) is the distance difference between vehicle i and the preceding vehicle at time t; f desired is a function of the vehicle's drag speed, which depends on the relative speed of the vehicle and the preceding vehicle, f safe It is a function of the vehicle's ability to maintain a safe following distance, which depends on the distance and speed difference between the vehicle and the preceding vehicle; i (t) is the lane position of vehicle i at time t; l desired (i) is the desired lane position of vehicle i; f lane is the lane position adjustment function that adjusts the vehicle acceleration, depending on the difference between the current lane and the desired lane position, using cubic spline interpolation.

[0046] (433) Define individual interaction rules. The interaction rules defined here adjust the speed based on the vehicle spacing, speed difference, acceleration difference and prediction of other vehicle behaviors to avoid collision or maintain appropriate spacing. The formula of the individual interaction rule function is as follows:

[0047]

[0048] Where, v desired is the desired speed function of the vehicle, which is adjusted according to the distance to the preceding vehicle and the current vehicle acceleration; v safe It is a speed function for maintaining a safe distance, which is adjusted according to the distance from the vehicle in front; T is a time constant used to control the safe distance between vehicles; T pis a prediction time, used to predict the speed of other vehicles; α, β, γ, δ, η, θ are adjustment parameters with a value range of [0, 1], used to control the impact of different factors on vehicle speed;

[0049] (434) Define the overall system behavior function of the traffic flow, which is specifically expressed as:

[0050]

[0051] Where: ρ(t) is the density of traffic flow at time t, which can be calculated based on the number of vehicles and road length; represents the acceleration change rate of vehicle i, represents the rate of change of traffic flow density; ω1, ω2, ω3, ω4, and ω5 are the weights of various factors, which are used to adjust the impact of each factor on the overall behavior; other factors include weather factors and road control.

[0052] Furthermore, a microscopic traffic evaluation index calculation system is constructed based on the principal component fuzzy fusion algorithm, which specifically includes the following:

[0053] (51) Specify the traffic evaluation object system: For complex urban traffic scenarios, select vehicle delay time, number of stops, travel speed, travel time and fuel consumption indicators to construct an evaluation index set, P = {u1,u2,…,u i},u i ={u i1 ,u i2 ,…,u ij}(i=1,2,…n)(j=1,2,…m) is a single set of various traffic evaluation indicators;

[0054] (52) The standardization method of traffic indicators is expressed as:

[0055]

[0056] Where: is the average of the set of i traffic evaluation indicators, s i is the standard deviation of the set of i-item traffic evaluation indicators; α ij is u ij Indicator relative to the overall u i The scaling index of the indicator set is between 0 and 1; β i It is the indicator factor of each traffic indicator, representing the degree to which each traffic indicator indicates the traffic conditions; ε i is the dimensional conversion parameter of each traffic indicator, representing the sensitivity of the dimensional indicator to the traffic condition; the correction bias parameter δ i Correction dimension conversion parameters;

[0057] (53) Principal component analysis of traffic evaluation index data: Analyze the index set, extract the traffic evaluation index component load matrix, score matrix and weight coefficient, and obtain the traffic evaluation index weight vector C = {c1, c2, ..., c i}, representing vehicle delay time Vt, number of stops Vs, travel speed Vv, travel time VT and fuel consumption Ve;

[0058] (54) Determine the fuzzy evaluation level of traffic indicators and establish the fuzzy evaluation matrix R of traffic indicators: Determine the fuzzy evaluation level of traffic indicators as needed into i layers: V = {v1, v2, ..., v i}, from high to low;

[0059] According to the fuzzy evaluation level of traffic indicators, the evaluation index set P is fuzzy evaluated. According to the frequency, the traffic indicator fuzzy evaluation matrix R is established to complete the fuzzy evaluation of vehicle delay time, number of stops, travel speed, travel time and fuel consumption.

[0060]

[0061] Where: γ i,i is the discount factor of the i-th traffic index to the i-th fuzzy evaluation level, usually between 0 and 1; θ i,i is the fuzzy weight of the i-th level fuzzy evaluation level for the i-th traffic index pair;

[0062] (55) Synthesize the evaluation results of the principal component fuzzy fusion algorithm: The traffic evaluation index weight vector C and the traffic index fuzzy evaluation matrix R are synthesized to obtain the evaluation results S of the principal component fuzzy fusion algorithm:

[0063]

[0064] The evaluation result S is evaluated to complete the evaluation of traffic indicators such as vehicle delay time, number of stops, travel speed, travel time and fuel consumption.

[0065] According to a second aspect of the present invention, a microscopic three-dimensional simulation system for a composite transportation network with macroscopic and microscopic integration is provided, which is used to implement the above-mentioned microscopic three-dimensional method for the composite transportation network with macroscopic and microscopic integration, comprising:

[0066] Build a module for establishing a real-time linkage global dynamic traffic scene interface. Based on the high-precision GIS map information platform API, develop a high-precision editable three-dimensional traffic scene twin module. Through real-time online editing of weather and road property models, add the time change dimension on the basis of three-dimensional space to build a full four-dimensional traffic scene model platform.

[0067] The multi-dimensional calibration module is used to construct the control unit intelligent agent, traveler intelligent agent, road sensor intelligent agent and communication unit intelligent agent. Through the collaborative information collection and transmission of multiple intelligent agents, the actual multi-source micro-travel data collected is processed and gradient updated. The multi-dimensional fusion algorithm matrix of traffic scene parameters is used to realize the multi-dimensional calibration of the micro-simulation model parameters.

[0068] The communication rendering module is used to develop a two-way integrated data communication module for both the front and back ends based on the mature API of the micro-simulation platform. This module enables simultaneous real-time rendering of vehicle trajectories and micro-simulation results in a 3D scene while simultaneously implementing macro-simulation evolution.

[0069] The analysis and response module is used to build real-time models and rapid analysis and calculation models for typical urban road network traffic scenarios for large-scale complex traffic network scenarios, enabling timely response of management and control strategies at the micro level;

[0070] The visualization module is used to build a microscopic traffic evaluation index calculation system based on the principal component fuzzy fusion algorithm based on the front-end and back-end two-way integrated communication rendering module, and realize the visualization of simulation analysis results on the screen;

[0071] The archiving module is used to store key data in the background while the simulation is in progress for follow-up analysis.

[0072] According to the third aspect of the present invention, the present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the memory stores a computer program capable of running on the processor, and when the processor loads and executes the computer program, the above-mentioned microscopic three-dimensional simulation method of the macro-micro integration of the composite transportation network is adopted.

[0073] The present invention has at least the following beneficial effects:

[0074] The present invention uses a macro-micro integrated model to more comprehensively consider various traffic scenarios and factors, improve the accuracy of traffic flow simulation, and reflect real traffic conditions. At the same time, through multi-dimensional data calibration and micro traffic flow individual interaction models, it can effectively respond to various complex traffic scenarios, such as severe weather and sudden accidents, provide more flexible and real-time simulation support, help traffic management departments formulate emergency response measures, and provide a scientific basis for traffic management.

[0075] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 Schematic diagram of the process of the present invention;

[0077] Figure 2 Schematic diagram of the three-dimensional traffic scene twin module in the present invention;

[0078] Figure 3 This is a schematic diagram of the screen end of the high-precision model platform for all traffic scenarios in the present invention;

[0079] Figure 4 This is a schematic diagram of a road construction scene in the present invention;

[0080] Figure 5 This is a schematic diagram of an isolated ramp bottleneck section scenario in the present invention;

[0081] Figure 6 This is a schematic diagram of a three-dimensional simulation demonstration of a road construction scene in the present invention;

[0082] Figure 7 This is a schematic diagram of a three-dimensional simulation demonstration of an isolated ramp bottleneck section scenario in the present invention;

[0083] Figure 8 Schematic diagram of simulation analysis results in the present invention. DETAILED DESCRIPTION

[0084] The following will be combined with the accompanying drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. Obviously, the embodiments described are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present disclosure.

[0085] Example 1:

[0086] See also Figures 1-8 The present invention provides a technical solution: a microscopic three-dimensional simulation method for a composite transportation network with macro-micro integration, comprising the following steps:

[0087] S10. Real-time linkage global dynamic traffic scenario construction: A real-time linkage global dynamic traffic scenario interface is proposed. Based on the high-precision GIS map information platform API, a high-precision editable 3D traffic scenario twin module is developed. Through real-time online editing of weather and road property models, the time change dimension is added to the 3D space to complete the construction of a full 4D traffic scenario. Based on accurate road geometry information, a 2D underlying road network editing module is developed to build an integrated restoration function for multi-dimensional simulation scenarios, improving the dynamic and global nature of the simulation environment. The details are as follows:

[0088] (S11) Linking with the GIS map platform API to perform high-precision real-time conversion and positioning of spatial geographic location information of road property models surrounding the road scene, wherein the road property models include roadside green facilities, buildings, signs and markings, and roadside equipment;

[0089] (S12) performing digital dynamic definition of the entire traffic scene model itself by specifying model size, border, color multi-dimensional information through parameters;

[0090] (S13) The interface supports seamless integration of real-time data streams to dynamically update the entire scene model module. Examples of dynamic scene updates include real-time changes to road alignment, real-time changes to road signs, and real-time addition or deletion of static obstacles.

[0091] S20. Multi-source traffic agent parameter fusion calibration: Construct a control unit agent, a traveler agent, a road sensor agent, and a communication unit agent. Through the coordinated information collection and transmission of these agents, the collected multi-source micro-travel data is processed and gradient updated. A multi-dimensional fusion algorithm matrix of traffic scenario parameters is used to achieve multi-dimensional calibration of micro-simulation model parameters. The details are as follows:

[0092] (S21) constructing a signal control unit agent, a traveler agent, a road sensor agent, and a communication unit agent; the signal control unit agent is used to control traffic signals, the traveler agent is used to intelligently predict traffic conditions based on historical traffic data, the road sensor agent is used to intelligently and accurately collaboratively perceive road traffic volume, and the communication unit agent is used to perform preliminary processing and transmission of traffic flow data;

[0093] (S22) Through the collaborative information collection and transmission of multiple agents, the collected information is subjected to the improved centralized learning-distributed execution deep multi-source agent algorithm:

[0094] θ=(θ1,…,θ i ) represents the parameters of the signal control unit agent, traveler agent, road sensor agent, and communication unit agent. The gradient is updated through the strategy optimization function and gradient function of each traffic agent. The optimization function and its gradient function are:

[0095]

[0096] Where: i Represents the discount factor of each agent, which represents the degree of influence of each agent on the traffic conditions, usually between 0 and 1; r i,t Represents the reward function of each agent for improving traffic conditions at time t; β i The correction bias parameter representing the impact of each agent on the traffic situation; π i Represents the strategy of each agent; t i Represents the state value currently collected by each traffic agent; α=(t1,…,t i) represents the state value vector collected by each traffic agent through centralized learning; a i,n Represents the nth control strategy action distributed and executed by each traffic agent; Represents the traffic impact-control strategy action function of each traffic agent under the improved centralized learning-distributed execution;

[0097] (S23) Multi-dimensional calibration refers to the three dimensions of scenario, vehicle type, and traffic flow parameters. The bottom layer uses various traffic conditions in complex urban traffic as scenarios, specifically divided into severe weather scenarios, road construction scenarios, isolated ramp bottleneck section scenarios, active intervention traffic accident risk scenarios, bottleneck section traffic flow fluctuation scenarios, high truck ratio traffic scenarios, occasional continuous ramp multiple bottleneck section scenarios, frequent continuous ramp multiple bottleneck section scenarios, large-scale emergency incidents and accidents, and emergency lane management scenarios. The above traffic scenarios realize multi-dimensional calibration of micro-simulation model parameters through the multi-dimensional fusion algorithm matrix of traffic scenario parameters;

[0098] Each scenario has corresponding types of vehicles. In complex urban traffic, there are many types of vehicles, including family cars, taxis, buses, trucks, trailers, and non-motorized vehicles. At the same time, each type of vehicle has different traffic parameters. Following, lane changing, and speed are selected as the matrix parameters of the last layer. The three traffic scenario parameters are multi-dimensionally integrated to form a set of traffic scenario parameter multi-dimensional fusion algorithm matrix:

[0099]

[0100] micropra ij =[CF LC CS]

[0101] Among them, Microsim p is a multidimensional microscopic parameter matrix, scene n is the dimensional parameter matrix of a typical traffic scene, micropra ij is the vehicle model dimension parameter matrix under the typical traffic scenario dimension, CF is the vehicle following parameter model library, LC is the vehicle lane changing parameter model library, and CS is the vehicle speed model library;

[0102] S30. Front-end and back-end bidirectional integrated communication rendering module: Developed and implemented based on the mature API of the micro-simulation platform, this module integrates the reception of macro-simulation data results with the transmission of micro-simulation results. This module enables simultaneous real-time rendering of micro-simulation results, such as vehicle trajectories and specific control information, within the 3D scene while simultaneously enabling macro-simulation evolution. This module specifically includes the following:

[0103] Long Polling: Long polling is a method that simulates real-time communication. The client sends a request to the server, and the server keeps the connection open until there is data to send to the client or the request times out, thus achieving two-way communication.

[0104] Front-end implementation: Use JavaScript to send long polling requests to the server and process the data received from the server;

[0105] Backend implementation: Write code in Python to process requests sent by the frontend and send data to the frontend as needed. At the same time, set an appropriate timeout on the server side and send a response to the client when there is new data.

[0106] Message format and protocol: When transmitting data between the front-end and back-end, a unified message format and communication protocol must be defined to ensure that both parties can correctly parse and process the data.

[0107] This module uses JSON format data;

[0108] S40. Recurrence Analysis and Response of Complex Traffic Network Scenario: For large-scale complex traffic network scenarios, a real-time model of typical traffic scenarios in urban road networks is constructed: road construction scenarios, isolated ramp bottleneck road scenarios, such as Figure 4 、 Figure 5 As shown in the figure, as well as the rapid analysis and calculation model, timely response of the control strategy at the micro level is achieved, as follows:

[0109] (S41) calling the network digital definition interface of the full traffic scene model, linking the GIS map platform to locate the scene construction content, and starting to update the road three-dimensional modeling in real time;

[0110] (S42) using information collection equipment to monitor road conditions in real time, receiving multi-source micro-travel data through a front-end and back-end bidirectional integrated communication rendering module, including road event type, event occurrence time, event generation spatial coordinates, and traffic flow scene information around the event occurrence point, and performing real-time rendering simulation of the traffic flow module;

[0111] (S43) Forming a fast computational analysis model for microscopic simulation to predict simulation results for control decisions. This mainly includes simulating traffic flow at the microscopic level. To reflect the interactions between vehicles and the diversity of driving behaviors, a microscopic traffic flow individual interaction model is constructed. Each vehicle is regarded as an independent intelligent agent and simulated according to its individual characteristics and behavioral rules. The details are as follows:

[0112] (431) Individual state definition: Each vehicle is defined as an individual, and its state includes information such as position, speed, acceleration, lane position, etc. The state formula is as follows:

[0113] s i (t)=(x i (t),v i (t),a i (t),l i (t))

[0114] Where: x i (t) represents the position of vehicle i at time t, v i (t) represents speed, a i (t) represents acceleration, l i (t) represents the lane position;

[0115] (432) Individual behavior rules: define the situations in which the vehicle follows the speed of the preceding vehicle, maintains a safe following distance, and limits the maximum acceleration. In addition, under normal circumstances, the vehicle will try to stay in its lane, but may also consider changing lanes. In the behavior rule function, lane position information is introduced and used to adjust the vehicle's acceleration. The formula for the individual behavior rule is as follows:

[0116] a i (t)=min(a max ,max(0,f desired (v i (t), Δx i (t))+f safe (Δv i (t),Δx i (t))+f lane (l i (t),l desired (i))))

[0117] Where a max is the maximum acceleration limit of the vehicle; Δv i (t) is the speed difference between vehicle i and the preceding vehicle at time t; Δx i (t) is the distance difference between vehicle i and the preceding vehicle at time t; f desired is a function of the vehicle's drag speed, which depends on the relative speed of the vehicle and the preceding vehicle, f safe The function of the vehicle maintaining a safe following distance depends on the distance and speed difference between the vehicle and the preceding vehicle. These two functions are usually fitted based on experience or actual data.

[0118] Among them, l i (t) is the lane position of vehicle i at time t; l desired (i) is the desired lane position of vehicle i; f lane is the lane position adjustment function that adjusts the vehicle acceleration, depending on the difference between the current lane and the desired lane position, using cubic spline interpolation.

[0119] (433) Define individual interaction rules. The interaction rules defined here adjust the speed based on the vehicle spacing, speed difference, acceleration difference and prediction of other vehicle behaviors to avoid collision or maintain appropriate spacing. The formula of the individual interaction rule function is as follows:

[0120]

[0121] Where, v desired is the desired speed function of the vehicle, which is adjusted according to the distance to the preceding vehicle and the current vehicle acceleration; v safe It is a speed function for maintaining a safe distance, which is adjusted according to the distance from the vehicle in front; T is a time constant used to control the safe distance between vehicles; T p is a prediction time, used to predict the speed of other vehicles; α, β, γ, δ, η, θ are adjustment parameters with a value range of [0, 1], used to control the impact of different factors on vehicle speed;

[0122] (434) Define the overall system behavior function of the traffic flow, which is specifically expressed as:

[0123]

[0124] Where: ρ(t) is the density of traffic flow at time t, which can be calculated based on the number of vehicles and road length; represents the acceleration change rate of vehicle i, represents the rate of change of traffic flow density; ω1, ω2, ω3, ω4, and ω5 are the weights of various factors, which are used to adjust the impact of each factor on the overall behavior; other factors include weather factors and road control;

[0125] S50. Development of a dynamic visualization demonstration interaction and evaluation system: Based on a front-end and back-end bidirectional integrated communication rendering module, a microscopic traffic evaluation index calculation system is constructed based on the principal component fuzzy fusion algorithm to achieve visualization of simulation analysis results on the screen. The details are as follows:

[0126] (51) Specify the traffic evaluation object system: For complex urban traffic scenarios, select vehicle delay time, number of stops, travel speed, travel time and fuel consumption indicators to construct an evaluation index set, P = {u1,u2,…,u i},u i ={u i1 ,u i2 ,…,u ij}(i=1,2,…n)(j=1,2,…m) is a single set of various traffic evaluation indicators;

[0127] (52) The standardization method of traffic indicators is expressed as:

[0128]

[0129] Where: is the average of the set of i traffic evaluation indicators, s i is the standard deviation of the set of i-item traffic evaluation indicators; α ij is u ij Indicator relative to the overall u i The scaling index of the indicator set is between 0 and 1; β i It is the indicator factor of each traffic indicator, representing the degree to which each traffic indicator indicates the traffic conditions; ε i is the dimensional conversion parameter of each traffic indicator, representing the sensitivity of the dimensional indicator to the traffic condition; the correction bias parameter δ i Correction dimension conversion parameters;

[0130] (53) Principal component analysis of traffic evaluation index data: Analyze the index set, extract the traffic evaluation index component load matrix, score matrix and weight coefficient, and obtain the traffic evaluation index weight vector C = {c1, c2, ..., c i}, representing vehicle delay time Vt, number of stops Vs, travel speed Vv, travel time VT and fuel consumption Ve;

[0131] (54) Determine the fuzzy evaluation level of traffic indicators and establish the fuzzy evaluation matrix R of traffic indicators: Determine the fuzzy evaluation level of traffic indicators as needed into i layers: V = {v1, v2, ..., v i}, from high to low;

[0132] According to the fuzzy evaluation level of traffic indicators, the evaluation index set P is fuzzy evaluated. According to the frequency, the traffic indicator fuzzy evaluation matrix R is established to complete the fuzzy evaluation of vehicle delay time, number of stops, travel speed, travel time and fuel consumption.

[0133]

[0134] Where: γ i,i is the discount factor of the i-th traffic index to the i-th fuzzy evaluation level, usually between 0 and 1; θ i,i is the fuzzy weight of the i-th level fuzzy evaluation level for the i-th traffic index pair;

[0135] (55) Synthesize the evaluation results of the principal component fuzzy fusion algorithm: The traffic evaluation index weight vector C and the traffic index fuzzy evaluation matrix R are synthesized to obtain the evaluation results S of the principal component fuzzy fusion algorithm:

[0136]

[0137] Evaluate the evaluation results S and complete the evaluation of traffic indicators such as vehicle delay time, number of stops, travel speed, travel time and fuel consumption;

[0138] S60. Archiving and revisiting analysis of full-view simulation video streams: While the simulation is in progress, key data is stored in the background for revisiting analysis.

[0139] Next, the present invention will be further described with reference to specific embodiments:

[0140] like Figure 1 The present embodiment describes a microscopic three-dimensional simulation method based on macro-micro integration of a composite transportation network, as shown in the flowchart. Figure 1 As shown, the steps are as follows:

[0141] Step 1: Based on the real-time linkage global dynamic traffic scene interface and the high-precision GIS map information platform API, develop a high-precision editable three-dimensional traffic scene twin module, through real-time online editing of weather and road property models, such as Figure 2 As shown;

[0142] On the basis of three-dimensional space, the time change dimension is added to complete the four-dimensional scene construction function of the whole traffic. Based on the precise road geometry information, a two-dimensional underlying road network editing module is developed to build a high-precision model platform for the whole traffic scene, such as Figure 3 As shown;

[0143] Step 2: Based on the multi-source micro-travel data collected, the multi-agent collaborative sensing interface of the improved centralized learning-distributed execution deep multi-source agent algorithm is used, including the signal control unit agent, the traveler agent, the road sensor agent, and the communication unit agent. The optimization function and gradient function of each traffic agent strategy are updated. The optimization function and its gradient function are:

[0144]

[0145] Various traffic conditions that often occur in complex urban traffic are selected as scenarios, such as road construction scenarios and isolated ramp bottleneck sections. The micro-simulation model parameters are calibrated in three dimensions: scenario, vehicle type, and traffic flow parameters, using a multi-dimensional fusion algorithm matrix of traffic scenario parameters.

[0146] Based on the various types of vehicles corresponding to the scenario, as well as the different traffic parameters of following, lane changing, and speed for each type of vehicle, a set of multi-dimensional fusion algorithm matrix of traffic scenario parameters is formed:

[0147]

[0148] micropra ij =[CF LC CS]

[0149] Step 3: Develop a front-end and back-end bidirectional data communication module based on the mature API of the micro-simulation platform. This module integrates the reception of macro-simulation data results with the transmission of micro-simulation results. This allows for simultaneous real-time rendering of micro-simulation results, such as vehicle trajectories and specific control information, in a 3D scene while simultaneously enabling macro-simulation evolution.

[0150] The front-end and back-end bidirectional data communication module establishes a mechanism between the user interface and the server, enabling real-time data transmission between the front-end and back-end. It includes the following components: Long Polling: Long polling is a method that simulates real-time communication. The client sends a request to the server, and the server keeps the connection open until there is data to send to the client or the connection times out, thus achieving two-way communication. Front-end implementation: Long polling requests are sent to the server using JavaScript and the data received from the server is processed. Back-end implementation: Python code is written to process requests sent by the front-end and send data to the front-end as needed. At the same time, an appropriate timeout is set on the server and a response is sent to the client when new data is available. Message format and protocol: When transmitting data between the front-end and back-end, a unified message format and communication protocol must be defined to ensure that both parties can correctly parse and process the data. This module uses JSON format data.

[0151] Step 4: Based on the data from the real-time linkage global dynamic traffic scene interface and the front-end and back-end two-way data communication modules, a real-time model of typical traffic scenarios in urban road networks is constructed for large-scale complex traffic network scenarios: road construction scenarios, isolated ramp bottleneck road section scenarios, such as Figure 4 、 Figure 5 As shown, as well as the fast analysis calculation module;

[0152] The fast calculation and analysis module analyzes the individual interaction model of microscopic traffic flow and calculates individual states, individual behavior rules, individual interaction rule functions, and overall system behavior functions:

[0153] s i (t)=(x i (t),v i (t),a i (t),l i (t))

[0154] a i (t)=min(a max ,max(0,f desired (v i (t), Δx i (t))+f safe (Δv i(t),Δx i (t))+f lane (l i (t),l desired (i))))

[0155]

[0156] Step 5: Based on the data communication module, a visual intelligent transportation front-end large screen is designed and developed to realize microscopic 3D simulation demonstration of road construction scenes and isolated ramp bottleneck sections, such as Figure 6 、 7 At the same time, the microscopic traffic evaluation index calculation system based on the principal component fuzzy fusion algorithm outputs the simulation analysis results, such as Figure 8 ;

[0157] In step 6, while the simulation is in progress, key data is stored in the background for multiple review by decision makers, facilitating a more comprehensive analysis of the micro-behavior of urban travelers.

[0158] In summary, this embodiment, through the macro-micro integrated model, can more comprehensively consider various traffic scenarios and factors, improve the accuracy of traffic flow simulation, and reflect the real traffic conditions. At the same time, through multi-dimensional data calibration and micro traffic flow individual interaction model, it can effectively respond to various complex traffic scenarios, such as severe weather and sudden accidents, and provide more flexible and real-time simulation support, helping traffic management departments to formulate emergency response measures and provide a scientific basis for traffic management.

[0159] Example 2:

[0160] The present invention provides a microscopic three-dimensional simulation system for a composite transportation network with macroscopic and microscopic integration, which is used to implement the above-mentioned microscopic three-dimensional method for the composite transportation network with macroscopic and microscopic integration, including:

[0161] Build a module for establishing a real-time linkage global dynamic traffic scene interface. Based on the high-precision GIS map information platform API, develop a high-precision editable three-dimensional traffic scene twin module. Through real-time online editing of weather and road property models, add the time change dimension on the basis of three-dimensional space to build a full four-dimensional traffic scene model platform.

[0162] The multi-dimensional calibration module is used to construct the control unit intelligent agent, traveler intelligent agent, road sensor intelligent agent and communication unit intelligent agent. Through the collaborative information collection and transmission of multiple intelligent agents, the actual multi-source micro-travel data collected is processed and gradient updated. The multi-dimensional fusion algorithm matrix of traffic scene parameters is used to realize the multi-dimensional calibration of the micro-simulation model parameters.

[0163] The communication rendering module is used to develop a two-way integrated data communication module for both the front and back ends based on the mature API of the micro-simulation platform. This module enables simultaneous real-time rendering of vehicle trajectories and micro-simulation results in a 3D scene while simultaneously implementing macro-simulation evolution.

[0164] The analysis and response module is used to build real-time models and rapid analysis and calculation models for typical urban road network traffic scenarios for large-scale complex traffic network scenarios, enabling timely response of management and control strategies at the micro level;

[0165] The visualization module is used to build a microscopic traffic evaluation index calculation system based on the principal component fuzzy fusion algorithm based on the front-end and back-end two-way integrated communication rendering module, and realize the visualization of simulation analysis results on the screen;

[0166] The archiving module is used to store key data in the background while the simulation is in progress for follow-up analysis.

[0167] Specifically, the above-mentioned building module, multi-dimensional calibration module, communication rendering module, analysis and response module, visualization module and archiving module can be embedded in a computer processing system. The computer can call the above-mentioned building module, multi-dimensional calibration module, communication rendering module, analysis and response module, visualization module and archiving module according to the above-mentioned microscopic three-dimensional simulation method for macro-micro integration of composite transportation network, and can perform operations according to the specific steps given in the microscopic three-dimensional simulation method for macro-micro integration of composite transportation network.

[0168] It should be noted that it should be understood that the division of the various modules of the above system is only a division of logical functions. In actual implementation, they can be fully or partially integrated into one physical entity, or they can be physically separated, and these modules can all be implemented in the form of software called by processing elements; they can also all be implemented in the form of hardware; some modules can also be implemented in the form of processing elements calling software, and some modules can be implemented in the form of hardware. For example, the construction module can be a separately established processing element, or it can be integrated into a chip of the above-mentioned device. In addition, it can also be stored in the memory of the above-mentioned device in the form of program code, and called and executed by a processing element of the above-mentioned device to perform the functions of the above signal processing module. The implementation of other modules is similar. In addition, these modules can all or partly be integrated together, or they can be implemented independently. The processing element described here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each of the above modules can be completed by the hardware integrated logic circuit in the processor element or the instructions in the form of software.

[0169] For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as one or more application-specific integrated circuits (ASICs), one or more digital signal processors (DSPs), or one or more field programmable gate arrays (FPGAs). For another example, when a module is implemented by scheduling program code through a processing element, the processing element may be a general-purpose processor, such as a central processing unit (CPU) or other processor that can call program code. For another example, these modules may be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0170] Example 3:

[0171] The present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. The memory stores a computer program capable of running on the processor. When the processor loads and executes the computer program, it adopts the microscopic three-dimensional simulation method of the composite transportation network with macro-micro integration in Example 1.

[0172] It should be noted that the terminal device can be a computer device such as a desktop computer, a laptop computer or a cloud server, and the terminal device includes but is not limited to a processor and a memory. For example, the terminal device can also include input and output devices, network access devices and buses, etc.

[0173] Furthermore, the processor may adopt a central processing unit (CPU). Of course, depending on the actual usage, other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. may also be adopted. The general-purpose processor may adopt a microprocessor or any conventional processor, etc., and this application does not impose any restrictions on this.

[0174] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0175] For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances. When an element is referred to as being "assembled on", "installed on", "fixed on" or "set on" another element, it can be directly on the other element or there can be a central element. When an element is considered to be "connected" to another element, it can be directly connected to the other element or there can be a central element at the same time. The terms "vertical", "horizontal", "up", "down", "left", "right" and similar expressions used herein are for illustrative purposes only and are not intended to be the only embodiment.

[0176] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

[0177] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

Claims

1. A microscopic three-dimensional simulation method for a complex transportation network with macro-micro integration, characterized by: The following steps are involved: Build a real-time, globally dynamic traffic scenario: Establish a real-time, globally dynamic traffic scenario interface. Based on the high-precision GIS map information platform API, develop a high-precision, editable three-dimensional traffic scenario twin module. Through real-time online editing of weather and road property models, add the dimension of time change on the basis of three-dimensional space to build a four-dimensional traffic scenario model platform. Multi-source traffic agent parameter fusion calibration: Construct control unit agents, traveler agents, road sensor agents, and communication unit agents. Through the collaborative information collection and transmission of multiple agents, the actual multi-source micro-travel data collected is processed and gradient updated. The multi-dimensional fusion algorithm matrix of traffic scenario parameters is used to achieve multi-dimensional calibration of micro-simulation model parameters. Front-end and back-end bidirectional integrated communication rendering module: Based on the mature API of the micro-simulation platform, a front-end and back-end bidirectional integrated data communication module is developed to achieve simultaneous real-time rendering of vehicle trajectories and micro-simulation results in the 3D scene while realizing macro-simulation evolution; Complex traffic network scenario recurrence analysis and response: For large-scale complex traffic network scenarios, a real-time model and rapid analysis and calculation model for typical urban road network traffic scenarios are constructed to achieve timely response of management and control strategies at the micro level; Development of a dynamic visualization demonstration interaction and evaluation system: Based on a front-end and back-end two-way integrated communication rendering module, a micro-traffic evaluation index calculation system is constructed based on the principal component fuzzy fusion algorithm to achieve visualization of simulation analysis results on the screen; Full-view simulation video stream archiving and revisit analysis: While the simulation is in progress, key data is stored in the background for revisit analysis.

2. The microscopic three-dimensional simulation method for a composite transportation network with macro-micro integration according to claim 1 is characterized in that: Real-time linkage of the global dynamic traffic scene interface, adding the time change dimension on the basis of three-dimensional space, and building a full-traffic four-dimensional scene model platform, as follows: (11) Linking with the GIS map platform API, it can perform high-precision real-time conversion and positioning of the spatial geographic location information of the road property model around the road scene, where the road property model includes roadside green facilities, buildings, signs and markings, and roadside equipment; (12) By specifying the model size, border, color and multi-dimensional information through parameters, the full traffic scene model itself is digitally and dynamically defined; (13) The interface supports seamless integration of real-time data streams and dynamic updates of the entire scene model module. Examples of dynamic scene updates include real-time changes to road alignment, real-time changes to road signs, and real-time addition or deletion of static obstacles.

3. The microscopic three-dimensional simulation method for a complex transportation network with macro-micro integration according to claim 2 is characterized by: The multi-source traffic agent parameter fusion calibration specifically includes the following: (21) Construct a signal control unit agent, a traveler agent, a road sensor agent, and a communication unit agent; the signal control unit agent is used to control traffic signals, the traveler agent is used to make intelligent predictions of traffic conditions based on historical traffic data, the road sensor agent is used to make intelligent and accurate collaborative perceptions of road traffic volume, and the communication unit agent is used to perform preliminary processing and transmission of traffic flow data; (22) Through the collaborative information collection and transmission of multiple intelligent agents, the collected information is improved by using the centralized learning-distributed execution deep multi-source intelligent agent algorithm: Represents the parameters of the signal control unit agent, traveler agent, road sensor agent, and communication unit agent. The gradient is updated through the strategy optimization function and gradient function of each traffic agent. The optimization function and its gradient function are: in: The discount factor represents each agent, which represents the degree of influence of each agent on the traffic conditions, and is usually between 0 and 1; Represents The reward function of each agent in improving traffic conditions under the time state; Correction bias parameters representing the impact of each agent on traffic conditions; Represents the strategy of each agent; Represents the current state value collected by each traffic agent; = Represents the state value vector collected by each traffic agent through centralized learning; Represents the nth control strategy action distributed and executed by each traffic agent; Represents the traffic impact-control strategy action function of each traffic agent under the improved centralized learning-distributed execution; (23) Multi-dimensional calibration refers to the three dimensions of scene, vehicle type, and traffic flow parameters. The underlying layer uses various traffic conditions in complex urban traffic as scenes. The above traffic scenes realize the multi-dimensional calibration of micro-simulation model parameters through the multi-dimensional fusion algorithm matrix of traffic scene parameters; Each scenario has corresponding types of vehicles. In complex urban traffic, there are many types of vehicles, including family cars, taxis, buses, trucks, trailers, and non-motorized vehicles. At the same time, each type of vehicle has different traffic parameters. Following, lane changing, and speed are selected as the matrix parameters of the last layer. The three traffic scenario parameters are multi-dimensionally integrated to form a set of traffic scenario parameter multi-dimensional fusion algorithm matrix: in, is a multidimensional microscopic parameter matrix, is the dimensional parameter matrix of a typical traffic scene, is the vehicle model dimension parameter matrix under the typical traffic scene dimension, is the vehicle following parameter model library, is the vehicle lane-changing parameter model library, It is the vehicle speed model library.

4. The microscopic three-dimensional simulation method for a composite transportation network with macro-micro integration according to claim 3 is characterized in that: Various traffic conditions in complex urban traffic are selected as scenarios, which are specifically divided into severe weather scenarios, road construction scenarios, isolated ramp bottleneck section scenarios, active intervention traffic accident risk scenarios, bottleneck section traffic flow fluctuation scenarios, high truck ratio traffic scenarios, occasional continuous ramp multiple bottleneck section scenarios, frequent continuous ramp multiple bottleneck section scenarios, large-scale sudden emergency events and accidents, and emergency lane management scenarios.

5. The microscopic three-dimensional simulation method for a complex transportation network with macro-micro integration according to claim 3 is characterized by: The front-end and back-end bidirectional integrated communication rendering module uses JSON format data, specifically including the following: Long polling technology is used to simulate real-time communication: the client sends a request to the server, and the server keeps the connection open until there is data to send to the client or the timeout occurs, thus achieving two-way communication; Front-end implementation: Use JavaScript to send long polling requests to the server and process the data received from the server; Backend implementation: Write code in Python to process requests from the frontend and send data to the frontend as needed. At the same time, set a timeout on the server side and send a response to the client when there is new data. Message format and protocol: When transmitting data between the front-end and back-end, a unified message format and communication protocol is defined to ensure that the front-end and back-end can correctly parse and process the data.

6. The microscopic three-dimensional simulation method for a composite transportation network with macro-micro integration according to claim 5 is characterized in that: Construct a real-time model and a rapid analysis and calculation model for typical traffic scenarios in urban road networks, specifically including the following: (41) Call the digital definition interface of the full traffic scene model network, link the GIS map platform to locate the scene construction content, and start updating the road 3D model in real time; (42) Use information collection equipment to monitor road conditions in real time, receive multi-source micro-travel data through the front-end and back-end two-way integrated communication rendering module, including road event type, event occurrence time, event generation space coordinates, and traffic flow scene information around the event occurrence point, and perform real-time rendering simulation of the traffic flow module; (43) Form a fast computational analysis model for microscopic simulation to predict simulation results for management and control decisions.

7. The microscopic three-dimensional simulation method for a composite transportation network with macro-micro integration according to claim 6 is characterized in that: The fast computational analysis model for microscopic simulation includes microscopic traffic flow simulation. To reflect the interactions between vehicles and the diversity of driving behaviors, a microscopic traffic flow individual interaction model is constructed. Each vehicle is treated as an independent intelligent agent and simulated according to its individual characteristics and behavioral rules. The details are as follows: (431) Individual state definition: Each vehicle is defined as an individual, and its state includes position, speed, acceleration, and lane position information. The state formula is as follows: Where: Indicates vehicle At the moment location, Indicates speed, represents acceleration, Indicates lane position; (432) Individual behavior rules: define the situations in which the vehicle follows the speed of the preceding vehicle, maintains a safe following distance, and limits the maximum acceleration. In addition, lane position information is introduced into the behavior rule function and used to adjust the vehicle's acceleration. The formula for the individual behavior rule is as follows: a i (t)=min(a max ,max(0,f desired (v i (t),Δx,(t))+f safe (Δv i (t),Δx i (t))+f lane (l i (t),l desired (i)))) Where, is the maximum acceleration limit of the vehicle; It's a vehicle At the moment Speed ​​difference with the vehicle ahead; It's a vehicle At the moment The distance difference to the vehicle in front; is a function of the vehicle's drag speed, which depends on the relative speed of the vehicle to the preceding vehicle. It is a function of the safe following distance maintained by the vehicle, which depends on the distance and speed difference between the vehicle and the vehicle ahead; It's a vehicle At the moment Lane location; It's a vehicle The desired lane position of is the lane position adjustment function that adjusts the vehicle acceleration, depending on the difference between the current lane and the desired lane position, using cubic spline interpolation; (433) Define individual interaction rules. The interaction rules defined here adjust the speed according to the vehicle spacing, speed difference, acceleration difference and prediction of other vehicle behavior to avoid collision or maintain appropriate spacing. The formula of the individual interaction rule function is as follows: Where, is a function of the vehicle's desired speed, adjusted according to the distance to the preceding vehicle and the current vehicle's acceleration; It is a function of the speed when maintaining a safe distance, and is adjusted according to the distance to the vehicle in front; is a time constant used to control the safe distance between vehicles; is a prediction time used to predict the speed of other vehicles; It is an adjustment parameter with a value range of [0,1], which is used to control the impact of different factors on vehicle speed; (434) Define the overall system behavior function of the traffic flow, which is specifically expressed as: Where: Traffic flow at time The density can be calculated based on the number of vehicles and the length of the road; Indicates vehicle The rate of change of acceleration, It represents the rate of change of traffic flow density; is the weight of each factor, which is used to adjust the impact of each factor on the overall behavior; other factors include weather factors and road control.

8. The microscopic three-dimensional simulation method for a composite transportation network with macro-micro integration according to claim 1 is characterized in that: The calculation system of microscopic traffic evaluation index is constructed based on the principal component fuzzy fusion algorithm, which includes the following: (51) Designated traffic evaluation object system: For complex urban traffic scenarios, vehicle delay time, number of stops, travel speed, travel time and fuel consumption indicators are selected to construct an evaluation index set. , ( i =1,2, )( j =1,2, ) is a single set of various traffic evaluation indicators; (52) The standardization method of traffic indicators is expressed as: Where: yes i The average of the set of traffic evaluation indicators, yes i The standard deviation of the set of traffic evaluation indicators; yes Indicators relative to the overall The scaling index of the indicator set is between 0 and 1; It is the indicator factor of each traffic indicator, representing the degree to which each traffic indicator indicates the traffic conditions; It is the dimensional conversion parameter of each traffic indicator, which represents the sensitivity of the dimensional indication of traffic conditions; the correction bias parameter Correction dimension conversion parameters; (53) Principal component analysis of traffic evaluation index data: Analyze the index set, extract the traffic evaluation index component load matrix, score matrix and weight coefficient, and obtain the traffic evaluation index weight vector after normalization. , indicating the vehicle delay time V t 、Number of stops V s , travel speed V v , travel time V T and fuel consumption V e ; (54) Determine the fuzzy evaluation level of traffic indicators and establish the fuzzy evaluation matrix R of traffic indicators: Determine the fuzzy evaluation level of traffic indicators as needed: i layer: , from high to low; According to the fuzzy evaluation level of traffic indicators, the evaluation index set P is fuzzy evaluated, and the traffic indicator fuzzy evaluation matrix R is established according to the frequency to complete the fuzzy evaluation of vehicle delay time, number of stops, travel speed, travel time and fuel consumption indicators: R = Where: yes i Traffic indicators for the i The discount factor of the fuzzy evaluation level is usually between 0 and 1; It is i Layer fuzzy evaluation level i Fuzzy weights of traffic indicator pairs; (55) Synthesize the evaluation results of principal component fuzzy fusion algorithm: transform the traffic evaluation index weight vector The principal component fuzzy fusion algorithm evaluation result S is synthesized with the traffic index fuzzy evaluation matrix R: S= The evaluation result S is evaluated to complete the evaluation of traffic indicators such as vehicle delay time, number of stops, travel speed, travel time and fuel consumption.

9. A microscopic three-dimensional simulation system for a composite transportation network with macroscopic and microscopic integration, for implementing the microscopic three-dimensional method for a composite transportation network with macroscopic and microscopic integration as claimed in any one of claims 1 to 8, characterized in that: include: Build a module for establishing a real-time linkage global dynamic traffic scene interface. Based on the high-precision GIS map information platform API, develop a high-precision editable three-dimensional traffic scene twin module. Through real-time online editing of weather and road property models, add the time change dimension on the basis of three-dimensional space to build a full four-dimensional traffic scene model platform. The multi-dimensional calibration module is used to construct control unit agents, traveler agents, road sensor agents, and communication unit agents. Through the collaborative information collection and transmission of multiple agents, the actual multi-source micro-travel data collected is processed and gradient updated. The multi-dimensional fusion algorithm matrix of traffic scenario parameters is used to achieve multi-dimensional calibration of micro-simulation model parameters. The communication rendering module is used to develop a two-way integrated data communication module for both the front and back ends based on the mature API of the micro-simulation platform. This module enables simultaneous real-time rendering of vehicle trajectories and micro-simulation results in a 3D scene while simultaneously implementing macro-simulation evolution. The analysis and response module is used to build real-time models and rapid analysis and calculation models for typical urban road network traffic scenarios for large-scale complex traffic network scenarios, enabling timely response of management and control strategies at the micro level; The visualization module is used to build a microscopic traffic evaluation index calculation system based on the principal component fuzzy fusion algorithm based on the front-end and back-end two-way integrated communication rendering module, and realize the visualization of simulation analysis results on the screen; The archiving module is used to store key data in the background while the simulation is in progress for follow-up analysis.

10. A terminal device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: The memory stores a computer program that can be run on the processor. When the processor loads and executes the computer program, the microscopic three-dimensional simulation method for macro-micro integration of a composite transportation network according to any one of claims 1 to 8 is adopted.

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