CN117854283B

CN117854283B - Urban road carbon neutralization estimation analysis method

Info

Publication number: CN117854283B
Application number: CN202410245023.2A
Authority: CN
Inventors: 王新飞; 宫海; 张翀凯; 周振洋; 陈宗燕; 徐海洋; 李小华
Original assignee: Jiangsu Shantong Construction Co ltd; Nantong Assembly Building And Intelligent Structure Research Institute
Current assignee: Jiangsu Shantong Construction Co ltd; Nantong Assembly Building And Intelligent Structure Research Institute
Priority date: 2024-03-05
Filing date: 2024-03-05
Publication date: 2024-05-10
Anticipated expiration: 2044-03-05
Also published as: CN117854283A

Abstract

The invention relates to the technical field of carbon neutralization estimation, in particular to an urban road carbon neutralization estimation analysis method, which comprises the following steps of carrying out comprehensive analysis on the number, speed and type of vehicles by adopting a data fusion and chaos theory method based on traffic monitoring data of urban key intersections and roads, carrying out preliminary analysis on traffic flow situations, and generating urban traffic real-time data analysis. According to the invention, the quantity, the speed and the type of vehicles are analyzed through data fusion and chaos theory, traffic peaks and congestion modes are explored through time sequence analysis and nonlinear dynamic modeling, flow change trends are identified, an autoregressive model predicts traffic flow trends, peak time periods and congestion road sections are analyzed, a lattice Boltzmann model is combined with urban meteorological data to evaluate the interaction between a heat island effect and traffic flow, the urban temperature distribution is influenced, life cycle evaluation and cost benefit analysis assist urban traffic carbon emission comprehensive evaluation and planning are promoted, and urban carbon neutralization comprehensive planning is promoted.

Description

Urban road carbon neutralization estimation analysis method

Technical Field

The invention relates to the technical field of carbon neutralization estimation, in particular to an urban road carbon neutralization estimation analysis method.

Background

The core objective in the technical field of carbon neutralization estimation is to accurately evaluate and analyze carbon emission in an urban road system by developing advanced methods and tools so as to take targeted measures to realize carbon neutralization of the urban road. The carbon neutralization estimation technology is a part of sustainable urban development, and the main task of the carbon neutralization estimation technology is to quantify and understand the influence of a traffic system on climate change, so that scientific carbon emission reduction decision support is provided for planners and decision makers.

The urban road carbon neutralization estimation analysis method is a complex technical means, and aims to realize carbon neutralization of an urban road system. Concepts of carbon neutralization include reducing, counteracting and absorbing carbon emissions to minimize the total carbon emissions of urban roads. With this goal, cities can more effectively reduce their contribution to climate change, improve air quality, and promote urban sustainable development.

The traditional method has a plurality of defects in actual operation. The conventional method often neglects comprehensive analysis of multidimensional data such as traffic flow, vehicle type, speed and the like, and causes inaccuracy of traffic flow situation judgment. The traditional method lacks the application of time sequence analysis and nonlinear dynamic modeling in the exploration of traffic peaks and congestion modes, which limits the accurate identification of flow change trend. Furthermore, conventional approaches often lack scientific predictive model support for trend prediction and peak period analysis of future traffic flows. In the aspect of interaction analysis of heat island effect and traffic flow, the traditional method is not fully combined with urban meteorological data, so that the influence on urban temperature distribution is not deeply estimated.

Disclosure of Invention

The invention aims to solve the defects in the prior art, and provides an urban road carbon neutralization estimation analysis method.

In order to achieve the above purpose, the present invention adopts the following technical scheme: the urban road carbon neutralization estimation analysis method comprises the following steps:

s1: based on traffic monitoring data of urban key intersections and roads, comprehensively analyzing the number, the speed and the types of vehicles by adopting a data fusion and chaos theory method, and performing primary analysis on traffic flow situations to generate urban traffic real-time data analysis;

s2: based on the urban traffic real-time data analysis, adopting time sequence analysis and nonlinear dynamic modeling to explore traffic peaks and congestion modes, and identifying flow change trend to generate traffic flow dynamic characteristic identification;

S3: based on the traffic flow dynamic characteristic identification, adopting an autoregressive model to predict trend of future traffic flow, analyzing peak time period and congestion road sections, and generating traffic flow prediction analysis;

S4: based on the traffic flow prediction analysis, adopting an optimization theory and a traffic engineering method to optimally design traffic flow and routes, and formulating a traffic signal adjustment strategy to generate a traffic jam relief scheme;

S5: combining urban meteorological data and the traffic jam relieving scheme, analyzing the interaction between a heat island effect and traffic flow by adopting a lattice Boltzmann model, and carrying out influence assessment of urban temperature distribution to generate urban heat island traffic influence assessment;

s6: based on the urban heat island traffic influence assessment, a system dynamics method is adopted to make a traffic management strategy to slow down the heat island effect, and an adjustment scheme design of a traffic system is carried out to generate a heat island effect slow down traffic plan;

S7: the traffic jam relieving scheme and the heat island effect relieving traffic planning are synthesized, genetic algorithm and particle swarm optimization are adopted, comprehensive balance and optimization strategy design of multiple factors are carried out, and comprehensive carbon emission reduction strategy optimization is generated;

s8: and based on the comprehensive carbon emission reduction strategy optimization, carrying out comprehensive assessment and planning of urban traffic carbon emission by adopting life cycle assessment and cost benefit analysis, and generating an urban carbon neutralization comprehensive planning result.

As a further aspect of the present invention, the urban traffic real-time data analysis includes traffic density map, vehicle speed distribution and traffic flow type classification, the traffic flow characteristic recognition includes traffic flow stability assessment, congestion tendency recognition and traffic pattern change detection, the traffic flow prediction analysis specifically includes future traffic peak prediction and potential congestion area prediction, the traffic congestion relief scheme includes traffic signal adjustment plan, alternative route design and traffic guidance strategy, the urban heat island traffic influence assessment includes road surface temperature change simulation, contribution assessment of traffic flow to heat island effect and slow down measure effect prediction, the heat island effect slow down traffic plan includes traffic flow regulation strategy, public traffic priority scheme and green traffic popularization plan, the comprehensive carbon emission reduction strategy optimization includes optimal traffic flow control strategy, energy saving and emission reduction traffic pattern and low carbon trip excitation measures, and the urban carbon neutralization comprehensive planning result includes carbon emission reduction target setting, strategy implementation path planning and long-term environmental benefit prediction.

As a further scheme of the invention, based on traffic monitoring data of urban key intersections and roads, a data fusion and chaos theory method is adopted to comprehensively analyze the number, speed and type of vehicles, and to perform preliminary analysis of traffic flow situation, and the steps of generating urban traffic real-time data analysis are as follows:

S101: based on traffic monitoring data of urban key intersections and roads, performing time synchronization on data collected by multiple sensors by adopting a data fusion algorithm, calibrating geographic positions corresponding to the data by using spatial positions, then applying weighted average, distributing differentiated weights according to the reliability of each sensor, integrating the data of multiple sources, and generating a comprehensive traffic flow data set;

s102: based on the comprehensive traffic flow data set, adopting a time sequence analysis algorithm, analyzing historical data through an autoregressive model to identify long-term trend and periodic variation of traffic flow, distinguishing frequency components in the traffic data through a spectrum analysis method, revealing periodic fluctuation and trend variation of traffic flow, and generating a vehicle dynamic analysis result;

s103: based on the dynamic analysis result of the vehicle, extracting speed and size characteristics of the vehicle through a support vector machine by using a machine learning classification algorithm, dividing boundaries of a characteristic space, classifying the vehicle types according to the extracted characteristics by using a decision tree algorithm, and generating a vehicle type classification overview;

S104: based on the vehicle type classification overview, a traffic flow state analysis method is used for carrying out probabilistic analysis on a plurality of states of traffic flow through a Markov chain model, predicting a conversion path of the traffic flow, carrying out quantitative analysis on the randomness of the traffic flow through a random process model, evaluating future traffic flow changes and generating urban traffic real-time data analysis.

As a further scheme of the invention, based on the urban traffic real-time data analysis, adopting time sequence analysis and nonlinear dynamic modeling to explore traffic peaks and congestion modes and identify traffic variation trend, the step of generating traffic flow dynamic feature identification specifically comprises the following steps:

S201: based on the urban traffic real-time data analysis, adopting an autoregressive moving average model, and carrying out statistical modeling on time sequence data to analyze autocorrelation and moving average characteristics in historical traffic data, reveal periodic changes of traffic flow and generate traffic flow periodic analysis;

S202: based on the periodic analysis of traffic flow, using a system dynamics model, simulating dynamic changes of traffic flow by establishing and analyzing differential equations describing the traffic flow, including modes of peak and congestion periods, and generating traffic peak and congestion mode analysis;

s203: based on the traffic peak and congestion pattern analysis, a network analysis method is applied, and the mutual influence and the dependency relationship between different road segments are analyzed by calculating the connectivity and cluster coefficients of multiple nodes in the road network, and key nodes and weak links of the traffic network are identified to generate traffic network interactive analysis;

s204: based on the traffic network interactivity analysis, adopting multiple regression analysis, and quantitatively predicting the change trend of the future traffic flow by combining historical and real-time data through statistical modeling, wherein the change trend comprises potential growing points and traffic bottleneck areas, so as to generate traffic flow dynamic characteristic identification;

The autocorrelation and moving average characteristics in the analysis historical traffic data adopt an autoregressive integral moving average model formula;

Wherein, Is the emotion observation value of the current time point,/>Is a lag operator of time,/>Is an autoregressive coefficient,/>Is a moving average coefficient,/>Is the difference times/>Is a white noise error.

As a further scheme of the invention, based on the traffic flow dynamic characteristic identification, an autoregressive model is adopted to predict the trend of the future traffic flow, and the peak period and the congestion road section are analyzed, so that the traffic flow prediction analysis is generated by the steps of:

s301: based on the traffic flow dynamic characteristic identification, an autoregressive model is adopted, the change of traffic flow at a plurality of time points in the future is predicted by calculating the autocorrelation in the time sequence of historical traffic data, and the time dependence of the traffic flow data is analyzed and simulated to generate traffic flow trend prediction;

S302: based on the traffic flow trend prediction, carrying out weighted average processing on the prediction data by using an exponential smoothing method, highlighting the influence of recent data on future traffic flow, weakening the influence of random fluctuation on the prediction, and generating short-term traffic flow prediction;

s303: based on the short-term traffic flow prediction, a seasonal autoregressive comprehensive moving average model is used for analyzing seasonal changes in historical data and predicting traffic flow of future target seasons or dates, including comprehensive analysis of autoregressions and moving averages, and seasonal traffic flow analysis is generated by referring to seasonal factors;

S304: based on the seasonal traffic flow analysis, the data are divided into differentiated groups according to the characteristics of the traffic flow data through cluster analysis, peak time and congestion road sections are identified, and key traffic characteristics are highlighted by quantifying the similarity and the difference of the traffic flow data to generate traffic flow prediction analysis.

As a further scheme of the invention, based on the traffic flow prediction analysis, an optimization theory and a traffic engineering method are adopted to perform the optimization design of traffic flow and route, and a traffic signal adjustment strategy is formulated, and the steps for generating a traffic jam relief scheme are specifically as follows:

S401: based on the traffic flow prediction analysis, setting traffic flow of multiple road sections of a road network as a decision variable by adopting a linear programming algorithm, taking the road capacity and traffic demand as constraint conditions, distributing the flow of each road by solving an optimization problem, reducing the congestion condition of traffic thoroughfares, and generating a road network flow optimization design;

S402: based on the road network flow optimization design, graph theory analysis is applied, wherein a traffic network graph is constructed, intersections are used as nodes, road sections are used as edges, shortest paths and flow distribution among the nodes are calculated, the route design is adjusted according to the flow distribution, the overall efficiency of the road network is optimized, and route optimization configuration is generated;

S403: based on the route optimization configuration, a dynamic traffic distribution model is applied, the time sequence and the period of the signal lamp are updated according to real-time traffic data, a signal lamp control system is adjusted in real time, traffic peak period flow changes are matched, traffic delay is reduced, and a traffic signal optimization strategy is generated;

S404: based on the traffic signal optimization strategy, the road network flow optimization design and the route optimization configuration, a comprehensive traffic management strategy is formulated, road network layout and signal control are adjusted, emergency measures are proposed to deal with emergency situations, traffic jams are relieved, and a traffic jam relieving scheme is generated;

the traffic flow of a plurality of road sections of the road network is set as a decision variable, the road capacity and the traffic demand are taken as constraint conditions, the flow of each road is distributed by solving an optimization problem, and an LWR model formula is adopted;

Wherein, For vehicle density, vehicle density at location x and time t is expressed,/>The product of density and velocity is expressed as a flow function.

As a further scheme of the invention, combining urban meteorological data and the traffic jam relieving scheme, adopting a lattice Boltzmann model to analyze the interaction between a heat island effect and traffic flow and evaluate the influence of urban temperature distribution, and specifically, the steps of generating the urban heat island traffic influence evaluation are as follows:

S501: based on urban meteorological data and the traffic jam relieving scheme, adopting a lattice Boltzmann model to simulate a heat island effect, simulating urban heat environment by simulating movement and collision of fluid microscopic particles, realizing meteorological data simulation of multiple areas of a city, analyzing potential influence of the traffic jam relieving scheme on the heat island effect, and generating an initial simulation diagram of urban heat distribution;

s502: based on the urban heat distribution initial simulation diagram, combining traffic flow data, analyzing the influence of traffic flow on an urban heat island effect by adopting a cellular automaton model, simulating traffic density change by defining a local rule of the traffic flow, evaluating the direct influence of the traffic flow on urban temperature distribution, and generating a traffic flow heat influence analysis diagram;

s503: based on the traffic flow heat influence analysis graph, combining traffic flow and meteorological data by adopting a data fusion technology, analyzing interaction of traffic flow and meteorological conditions by integrating and analyzing multi-source data, evaluating comprehensive influence of the interaction on a heat island effect, and generating a heat island traffic interaction comprehensive graph;

S504: based on the heat island traffic interaction comprehensive graph, the urban temperature distribution is comprehensively influenced and evaluated by applying an urban temperature distribution influence evaluation model, and the urban heat island effect and the influence of the interaction of the heat island effect and traffic flow on the urban environment are quantitatively analyzed, so that a countermeasure scheme is provided, the urban heat environment is comprehensively evaluated, and the urban heat island traffic influence evaluation is generated.

As a further scheme of the invention, based on the urban heat island traffic influence assessment, a system dynamics method is adopted to make a traffic management strategy to slow down the heat island effect, and an adjustment scheme design of a traffic system is carried out, so that the step of generating the heat island effect slow down traffic plan is specifically as follows:

S601: based on the urban heat island traffic influence assessment graph, analyzing by adopting a system dynamics method, simulating the influence of a differentiated traffic management strategy on the heat island effect by establishing a dynamic model of urban traffic flow, heat island effect and interaction thereof, and generating a traffic management strategy dynamic analysis result by referring to traffic flow, speed and urban layout factors;

S602: based on the dynamic analysis result of the traffic management strategy, adopting multi-criterion decision analysis, and combining urban traffic data and environmental influence parameters, evaluating and comparing the effectiveness of the differentiated traffic management strategy in the aspect of slowing down the heat island effect, and generating a traffic strategy optimization scheme for slowing down the heat island effect;

S603: based on the traffic strategy optimization scheme for slowing down the heat island effect, adopting Monte Carlo simulation, simulating a plurality of scenes after strategy implementation by a random sampling technology, predicting the performance of a new strategy under a plurality of prediction environment conditions, evaluating the feasibility and effect of the strategy, and generating a strategy implementation simulation prediction result;

s604: and (3) based on the strategy implementation simulation prediction result, adopting an urban traffic planning optimization model to adjust and design a traffic system, and referring to traffic flow distribution and road network optimization, generating a heat island effect to slow down traffic planning.

As a further scheme of the invention, the traffic jam relieving scheme and the heat island effect relieving traffic planning are integrated, genetic algorithm and particle swarm optimization are adopted, comprehensive balance of various factors and optimization strategy design are carried out, and the steps for generating comprehensive carbon emission reduction strategy optimization are specifically as follows:

S701: based on the comprehensive traffic jam relief scheme and the heat island effect, traffic planning is relieved, a genetic algorithm is adopted, and the traffic route selection and traffic flow distribution are optimized through simulation selection, intersection and variation, so that the optimized traffic system configuration is obtained;

s702: based on the optimized traffic system configuration, adopting a particle swarm optimization algorithm, simulating a shoring behavior of a bird swarm, optimizing traffic signal control and road network design so as to improve the efficiency of traffic flow and reduce the influence of heat island effect, and generating an optimized traffic control and road network design scheme;

S703: based on the optimized traffic control and road network design scheme, adopting a multi-objective genetic algorithm, capturing an optimal solution by simulating natural selection and genetic mechanism among a plurality of targets comprising reducing traffic congestion, reducing heat island effect and carbon emission, balancing the multi-targets, and generating a comprehensive optimized multi-objective traffic strategy;

S704: based on the comprehensively optimized multi-target traffic strategy, a particle swarm optimization algorithm is adopted, the positions and speeds of a plurality of particles representing a potential traffic strategy scheme are iteratively updated by simulating the movement and searching behaviors of a particle swarm, each particle is closed to the individual historical optimal position and the global optimal position, the traffic strategy is continuously adjusted and optimized, the original strategy is refined, and the comprehensive carbon emission reduction strategy optimization is generated.

As a further scheme of the invention, based on comprehensive carbon emission reduction strategy optimization, life cycle evaluation and cost benefit analysis are adopted to carry out comprehensive evaluation and planning of urban traffic carbon emission, and the steps for generating the urban carbon neutralization comprehensive planning result are specifically as follows:

S801: based on the comprehensive carbon emission reduction strategy optimization, adopting a statistical analysis method to perform trend analysis and difference exploration on carbon emission data of urban traffic, then adopting a machine learning prediction model to analyze future trend through random forests, and adopting a neural network to mine potential modes so as to perform comprehensive prediction analysis and generate a carbon emission current situation analysis result;

S802: based on the carbon emission current situation analysis result, analyzing the energy and material flows of multiple links of the transportation junction by adopting a life cycle assessment model, and analyzing the deep association of the energy and material flows with carbon emission so as to carry out life cycle assessment and generate life cycle carbon assessment of a transportation system;

S803: based on the life cycle carbon evaluation of the traffic system, a cost benefit analysis method is used for analyzing the economic value of the differentiated emission reduction strategy by a present value method, the financial return is evaluated by an internal benefit method, the optimized emission reduction strategy is selected, and the optimized emission reduction strategy analysis is generated;

S804: based on the optimization emission reduction strategy analysis, combining a system dynamic simulation method and a multi-objective optimization model, simulating the behavior of the urban traffic system through a system dynamic theory, analyzing various solutions by using a genetic algorithm, capturing an optimal balance point by using linear programming, and formulating a carbon neutralization planning scheme matched with urban characteristics to generate an urban carbon neutralization comprehensive planning result.

Compared with the prior art, the invention has the advantages and positive effects that:

According to the invention, through data fusion and application of chaos theory, more accurate analysis of the number, speed and type of vehicles is realized, and the judgment accuracy of traffic flow situation is improved. The method utilizes time sequence analysis and nonlinear dynamic modeling, effectively explores traffic peaks and congestion modes, and is beneficial to identifying flow change trend. In addition, the application of the autoregressive model makes trend prediction of future traffic flows more scientific, and provides a solid foundation for analysis of peak time periods and congestion road sections. By combining the lattice Boltzmann model and urban meteorological data, the method can deeply analyze the interaction of the heat island effect and traffic flow and evaluate the influence of urban temperature distribution. Comprehensive view angles are provided for comprehensive assessment and planning of urban traffic carbon emission by using life cycle assessment and cost benefit analysis, so that formation of urban carbon neutralization comprehensive planning is promoted.

Drawings

FIG. 1 is a schematic diagram of the main steps of the present invention;

FIG. 2 is a detailed schematic of the S1 of the present invention;

FIG. 3 is a schematic diagram of an S2 refinement of the present invention;

FIG. 4 is a schematic diagram of an S3 refinement of the present invention;

FIG. 5 is a schematic diagram of an S4 refinement of the present invention;

FIG. 6 is a schematic diagram of an S5 refinement of the present invention;

FIG. 7 is a schematic diagram of an S6 refinement of the present invention;

FIG. 8 is a schematic diagram of an S7 refinement of the present invention;

fig. 9 is a schematic diagram of the S8 refinement of the present invention.

Detailed Description

The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.

In the description of the present invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientation or positional relationships shown in the drawings, merely to facilitate describing the present invention and simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present invention. Furthermore, in the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.

Example 1

Referring to fig. 1, the present invention provides a technical solution: the urban road carbon neutralization estimation analysis method comprises the following steps:

S2: based on urban traffic real-time data analysis, adopting time sequence analysis and nonlinear dynamic modeling to explore traffic peaks and congestion modes, and identifying flow change trend to generate traffic flow dynamic characteristic identification;

S3: based on the traffic flow dynamic characteristic identification, adopting an autoregressive model to predict the trend of the future traffic flow, analyzing the peak period and the congestion road section, and generating traffic flow prediction analysis;

S4: based on traffic flow prediction analysis, adopting an optimization theory and a traffic engineering method to optimally design traffic flow and routes, and formulating a traffic signal adjustment strategy to generate a traffic jam relief scheme;

S5: combining urban meteorological data and a traffic jam relieving scheme, adopting a lattice Boltzmann model, analyzing the interaction between a heat island effect and traffic flow, and carrying out influence assessment of urban temperature distribution to generate urban heat island traffic influence assessment;

S6: based on urban heat island traffic influence assessment, a system dynamics method is adopted, a traffic management strategy is formulated to slow down heat island effect, an adjustment scheme design of a traffic system is carried out, and a heat island effect slow down traffic plan is generated;

S7: comprehensive traffic jam relieving schemes and heat island effect relieving traffic planning are adopted, genetic algorithm and particle swarm optimization are adopted, comprehensive balance of various factors and optimization strategy design are carried out, and comprehensive carbon emission reduction strategy optimization is generated;

s8: based on comprehensive carbon emission reduction strategy optimization, comprehensive assessment and planning of urban traffic carbon emission are carried out by adopting life cycle assessment and cost benefit analysis, and an urban carbon neutralization comprehensive planning result is generated.

Urban traffic real-time data analysis comprises traffic density map, vehicle speed distribution and traffic flow type classification, traffic flow state feature recognition comprises traffic flow stability assessment, congestion trend recognition and traffic pattern change detection, traffic flow prediction analysis comprises future traffic peak prediction and potential congestion area prediction, traffic congestion relieving schemes comprise traffic signal adjustment planning, alternative route design and traffic guiding strategies, urban heat island traffic influence assessment comprises road surface temperature change simulation, contribution assessment of traffic flow to heat island effect and slowing down measure effect prediction, heat island effect slowing down traffic planning comprises traffic flow regulation strategies, public traffic priority schemes and green traffic popularization plans, comprehensive carbon emission reduction strategy optimization comprises optimal traffic flow control strategies, energy-saving emission reduction traffic patterns and low-carbon travel excitation measures, and urban carbon neutralization comprehensive planning results comprise carbon emission reduction target setting, strategy implementation path planning and long-term environmental benefit prediction.

In the step of urban traffic real-time data analysis, data with different sources and formats, including video monitoring images, sensor records and GPS tracking data, are integrated by using a data fusion technology through traffic monitoring data of urban key intersections and roads. These data are converted into a unified format such as vehicle number per minute, vehicle speed and vehicle type classification. These data are then analyzed using chaos theory methods, which include calculating the time series distribution of traffic density and speed for each monitoring point, and identifying non-linear features of traffic flow. In the process, algorithm parameters of the chaos theory, such as embedding dimension and delay time, are finely adjusted to adapt to the characteristics of data, so that the accuracy of analysis is ensured. The analysis result reveals the preliminary characteristics of traffic flow situation, such as the increase of traffic flow and the decrease of speed in peak time, provides real-time data support for urban traffic management, and finally generates a detailed report containing traffic density map, vehicle speed distribution and traffic flow type classification.

In the traffic flow dynamic characteristic recognition step, a time sequence analysis and nonlinear dynamic modeling are adopted to explore traffic peaks and congestion modes based on real-time data analysis results. Time series analysis involves the identification of long-term trends, periodicity, and random fluctuations in traffic flow and speed data using algorithms such as autoregressive moving average models (ARIMA), particularly seasonal adjustments and predictions for traffic data. The nonlinear dynamic model is used for capturing complex behaviors in traffic flow, such as congestion caused by an emergency, the refinement operation in the steps comprises the steps of adjusting model parameters to adapt to the characteristics of different roads and time periods, and the model is used for identifying the flow change trend so as to effectively predict and identify the congestion trend and the traffic mode change. Through the analysis, detailed reports are generated, the stability, the congestion tendency and the traffic mode change of traffic flow are described, and scientific basis is provided for traffic planning and management.

In the traffic flow predictive analysis step, an autoregressive model is used to predict the trend of future traffic flows, which involves in-depth analysis of historical traffic data to identify and model key patterns of future traffic flows. By adjusting parameters of models, such as lag period number and moving average term number, to best reflect time-dependent characteristics of traffic flow, these models are able to predict traffic flow, speed and vehicle type at specific times and places. In addition, the steps also comprise analysis of traffic peak periods and congested road segments, predicting future traffic conditions, such as peak period traffic flow and potential congested areas. The generated traffic flow predictive analysis report not only helps traffic planners identify and solve potential traffic problems, but also provides key information for daily traffic management and emergency response.

In the traffic jam relief scheme design step, an optimization scheme of traffic flow and routes is designed by adopting an optimization theory and a traffic engineering method based on traffic flow prediction analysis. This includes analyzing bottlenecks and efficiency problems of current traffic networks and designing improvements. For example, graph theory and network flow analysis are used to determine key nodes and routes in a traffic network, and find the optimal traffic signal control strategy by simulating different traffic signal adjustment schemes. Meanwhile, considering alternative route design and traffic guidance strategies, such as suggesting detouring routes or adjusting public traffic priority to relieve the pressure of main roads, the result of the steps is a comprehensive traffic jam relieving scheme, which not only reduces traffic pressure, but also improves the efficiency and reliability of the whole traffic system.

In the urban heat island traffic influence assessment step, the interaction of the heat island effect and traffic flow is analyzed by adopting a lattice Boltzmann model in combination with urban meteorological data and a traffic jam relieving scheme. By simulating the influence of traffic flow on urban temperature distribution, in particular road surface temperature change, the model can reflect the influence of different traffic densities and vehicle types on urban thermal environment in detail. Various factors are considered in the model, such as the amount of heat emitted by the vehicle, solar radiation, and the heat capacity of the building. By adjusting model parameters such as grid size and time step, accuracy and reliability of simulation results are ensured. In addition, the evaluation also comprises prediction of the effect of the slow-down measures, such as increasing greening coverage rate and improving public traffic efficiency to relieve heat island effect, and the report generated by the steps describes the influence of traffic flow on urban heat environment in detail and the potential effect of various slow-down measures, so that important decision support is provided for urban planning and environment management.

In the heat island effect slowing-down traffic planning step, a traffic management strategy is designed by adopting a system dynamics method based on traffic influence evaluation so as to slow down the heat island effect. The system dynamics model can simulate complex interactions between traffic systems and urban environments and predict long-term impact of different traffic management strategies. These strategies include adjusting traffic flow distribution, prioritizing public transportation and promoting green transportation. By fine-tuning of model parameters, such as traffic demand growth rate and public traffic usage rate, the effectiveness and feasibility of the strategy are ensured. The result of this step is a comprehensive heat island effect slow-down traffic plan, including traffic flow regulation strategies, public traffic priority schemes and green traffic popularization plans, providing specific guidance for realizing urban sustainable development and environmental improvement.

In the comprehensive carbon emission reduction strategy optimization step, a comprehensive traffic jam relieving scheme and a heat island effect relieving traffic planning are adopted, and genetic algorithm and particle swarm optimization are adopted to carry out comprehensive balance of various factors and optimization strategy design. By simulating the environmental effects and cost effectiveness of different strategy combinations, the optimization algorithm can identify the most effective carbon emission reduction measures. In this process, algorithm parameters such as population size, crossover rate and mutation rate are carefully adjusted to accommodate complex urban traffic and environmental systems, and the result of the steps is a comprehensive carbon emission reduction strategy, including optimal traffic flow control strategy, energy-saving emission reduction traffic mode and low-carbon trip excitation measures, aimed at achieving efficient operation of the traffic system and environmental sustainability.

In the step of generating the urban carbon neutralization comprehensive planning result, comprehensive assessment and planning of urban traffic carbon emission are carried out by adopting life cycle assessment and cost benefit analysis based on comprehensive carbon emission reduction strategy optimization. The life cycle assessment covers the entire process from vehicle manufacture to scrapping, including fuel consumption, vehicle maintenance, and carbon emissions from traffic infrastructure construction. The cost benefit analysis involves evaluating the economic cost and environmental benefit of different traffic strategies, ensuring the economic feasibility and environmental benefit of the strategy. The final generated urban carbon neutralization comprehensive planning result comprises a clear carbon emission reduction target, strategy implementation path planning and long-term environmental benefit prediction by carefully analyzing and comparing the long-term influence of different strategies, and scientific planning and implementation guidance are provided for realizing the carbon neutralization target in the city.

Referring to fig. 2, based on traffic monitoring data of urban key intersections and roads, comprehensive analysis of the number, speed and type of vehicles is performed by adopting a data fusion and chaos theory method, and preliminary analysis of traffic flow situation is performed, and the steps of generating urban traffic real-time data analysis are specifically as follows:

S103: based on a vehicle dynamic analysis result, extracting speed and size characteristics of a vehicle through a support vector machine by using a machine learning classification algorithm, dividing boundaries of a characteristic space, classifying vehicle types according to the extracted characteristics by using a decision tree algorithm, and generating a vehicle type classification overview;

S104: based on the vehicle type classification overview, a traffic flow state analysis method is used for carrying out probabilistic analysis on a plurality of states of traffic flows through a Markov chain model, predicting a conversion path of the traffic flows, carrying out quantitative analysis on the randomness of the traffic flows through a random process model, evaluating future traffic flow changes, and generating urban traffic real-time data analysis.

In the S101 substep, data fusion and synchronization are realized through traffic monitoring data of urban key intersections and roads. First, the data collected by the multiple sensors is time synchronized, which involves a time stamp correction and synchronization algorithm, ensuring that the data from different sources are aligned on the time axis. Spatial location calibration is then achieved by geocoding techniques, which map the data to specific geographic locations. The data for each sensor is then integrated by a weighted average method, wherein the weighting coefficients are assigned according to the reliability and accuracy of the sensor, which involves analysis of signal strength and historical performance metrics. For example, the data of one high resolution camera gets a higher weight than the low resolution camera. The process finally generates a comprehensive traffic flow data set which not only reflects real-time traffic conditions, but also integrates the advantages of different sensors, and improves the accuracy and reliability of data.

In the S102 substep, the integrated traffic flow dataset is used for time series analysis. First, an autoregressive model is applied to analyze historical traffic flow data to identify long-term trends and periodic changes, steps involving determining parameters of the model, such as the number of hysteresis terms, and training the model through the historical data to predict future trends. Next, frequency components in the traffic data are identified by spectral analysis, which includes fourier transforming the data to reveal periodic fluctuations in traffic flow. The analysis results help understand the dynamic change rule of traffic flow, and have important significance for urban traffic management and planning.

In the sub-step S103, the vehicle dynamic analysis result is processed using the machine learning classification algorithm. A Support Vector Machine (SVM) is used to extract the speed and size characteristics of the vehicle and to perform boundary division of the characteristic space. This process involves selecting an appropriate kernel function to process the nonlinear data and adjusting parameters to optimize classification performance. The decision tree algorithm is then used to categorize the vehicle type based on the extracted features, including selecting the depth of the tree and branching criteria, and the strategy of fitting is processed. The end product of this step is a vehicle type classification overview that can be used for traffic monitoring and management, providing useful information about the vehicle type distribution.

In the step S104, the traffic flow pattern analysis method is applied. First, a Markov chain model is used to analyze a plurality of states of traffic flows, with probabilistic predictions of state transitions for future traffic flows. This includes defining a state space, calculation of transition probabilities, and verification and adjustment of models. The stochastic process model is then used to quantify the randomness of the traffic flow, which involves parameter estimation and model selection of the stochastic process. Finally, the analysis generates real-time data analysis results of urban traffic, and has important value for predicting and relieving traffic jam and optimizing traffic flow management.

It is assumed that at one traffic monitoring node, the data collected by the sensors includes the number, speed and type of vehicles. These data are first time synchronized, e.g., all data are marked as a time stamp once per minute. Then, these data are integrated by a weighted average method, and the data weight of the high-definition camera is assumed to be 0.6, and the data weight of other sensors (such as a radar) is assumed to be 0.4. Next, in the time series analysis, an autoregressive model was used, assuming that 3 hysteresis terms were selected, the model revealed a trend of traffic flow increase in the early peak and the late peak. When using SVM to classify vehicles, radial Basis Functions (RBFs) are selected as kernel functions to distinguish between different types of vehicles. Finally, in the traffic flow situation analysis, it is assumed that the markov chain model predicts that the probability that the traffic flow will increase in the next hour is 70%. The analysis results together form a comprehensive and real-time urban traffic data analysis platform.

Referring to fig. 3, based on real-time data analysis of urban traffic, time sequence analysis and nonlinear dynamic modeling are adopted to explore traffic peaks and congestion modes, and identify flow change trend, and the steps of generating traffic flow dynamic feature identification are specifically as follows:

S201: based on urban traffic real-time data analysis, adopting an autoregressive moving average model, and carrying out statistical modeling on time sequence data to analyze autocorrelation and moving average characteristics in historical traffic data, reveal periodic changes of traffic flow and generate traffic flow periodic analysis;

S203: based on traffic peak and congestion pattern analysis, a network analysis method is applied, and the mutual influence and the dependency relationship between different road segments are analyzed by calculating the connectivity and cluster coefficients of multiple nodes in a road network, key nodes and weak links of a traffic network are identified, so that traffic network interaction analysis is generated;

S204: based on traffic network interactivity analysis, adopting multiple regression analysis, and quantitatively predicting the change trend of future traffic flow by combining historical and real-time data through statistical modeling, wherein the change trend comprises potential growing points and traffic bottleneck areas, so as to generate traffic flow dynamic characteristic recognition;

the autocorrelation and moving average characteristics in the historical traffic data are analyzed, and an autoregressive integral moving average model formula is adopted;

In a substep S201, the urban traffic real-time data is subjected to a time-series analysis by means of an autoregressive moving average (ARMA) model, which first involves the collection and formatting of historical traffic data, ensuring that the data is organized in a time-series form, typically comprising traffic flows, time stamps, etc. The implementation of the Autoregressive (AR) portion then focuses on analyzing the autocorrelation in the time series, identifying patterns by calculating the correlation coefficients between the data points and their previous time points. The Moving Average (MA) part is then used to smooth the time series, reducing the effect of random fluctuations by taking into account the average of the first few time points of the data. In terms of parameter selection, such as the order of AR and MA, optimization selection is typically performed by the red pool information criterion (AIC) or the Bayesian Information Criterion (BIC). In this way, the ARMA model is able to reveal periodic variations in traffic flow, and the resulting periodic analysis is of great value for understanding and predicting urban traffic patterns.

In the sub-step S202, the dynamic change of the traffic flow is simulated and analyzed by using the system dynamics model, and the process starts with constructing a differential equation set describing the traffic flow, including factors such as the inflow rate, outflow rate and road capacity of the vehicle. The differential equations are established based on traffic flow theory and actual observations such as lane number, vehicle speed and traffic density. By solving these differential equations, dynamic changes of traffic flow at different times and conditions, such as traffic flow increase and congestion phenomenon during peak time, can be simulated, and the key of the steps is to accurately describe the intrinsic dynamic characteristics of the traffic system and to solve the equations by numerical methods such as the Euler method or the Longer-Kutta method. The generated traffic peak and congestion pattern analysis provides a view for the deep understanding of urban traffic behaviors, and is helpful for traffic planning and management.

In the sub-step S203, the urban traffic network is studied intensively by applying the network analysis method. The mutual influence and the dependence among different road segments can be revealed by calculating the connectivity and cluster coefficients of the nodes in the road network. For example, nodes with high connectivity are key intersections with large traffic flows, while high cluster coefficients indicate strong interactions between certain road segments, and steps require detailed modeling of the topology of the urban road network and computation by network analysis tools such as Social Network Analysis (SNA) software. The result reveals key nodes and weak links in the traffic network, and the generated traffic network interactivity analysis is very important for optimizing traffic flow and reducing congestion.

In the sub-step S204, multiple regression analysis is used to predict future traffic flow trends, which involves creating statistical models, combining historical and real-time traffic data. In the model, traffic flow is taken as a dependent variable, and time, weather conditions, special events, etc. are taken as independent variables. Regression coefficients are estimated by statistical methods such as least squares, and the model is able to quantitatively predict future traffic flow changes, including potential growth points and traffic bottleneck areas. This analysis is critical to urban traffic managers because it helps them understand the pattern of traffic flow changes and formulate corresponding traffic regulation strategies.

It is assumed that a city has a set of traffic monitoring data including vehicle traffic, temperature, and whether there are special events (e.g., holidays or large activities) for different periods of the day. In step S201, these data are analyzed by the ARMA model, e.g. selecting AR term 2 and ma term 2, and significant periodic changes in the morning and evening peaks of the workday are found. In step S202, a differential equation set reflecting inflow and outflow of the vehicle is established, simulating traffic flow differences between weekdays and weekends. In step S203, network analysis is performed on the major intersections of the cities, and it is found that the connectivity of a certain traffic hub is particularly high, which is a potential congestion point. Finally, in step S204, a multiple regression model is built, including time, temperature and special events as arguments, predicting the traffic flow trend within one month in the future. These analysis results are of great significance for urban traffic planning and management.

Referring to fig. 4, based on traffic flow state feature recognition, an autoregressive model is adopted to predict trend of future traffic flow, and analyze peak period and congestion road sections, and the steps of generating traffic flow prediction analysis are specifically as follows:

S302: based on traffic flow trend prediction, carrying out weighted average processing on prediction data by using an exponential smoothing method, highlighting the influence of recent data on future traffic flow, weakening the influence of random fluctuation on prediction, and generating short-term traffic flow prediction;

S303: based on short-term traffic flow prediction, a seasonal autoregressive comprehensive moving average model is used for analyzing seasonal changes in historical data and predicting traffic flow of future target seasons or dates, including comprehensive analysis of autoregressions and moving averages, and seasonal traffic flow analysis is generated by referring to seasonal factors;

S304: based on seasonal traffic flow analysis, the data are divided into differentiated groups according to the characteristics of the traffic flow data through cluster analysis, peak time and congestion road sections are identified, and key traffic characteristics are highlighted by quantifying the similarity and the difference of the traffic flow data to generate traffic flow prediction analysis.

In the step S301, the traffic flow dynamics are predicted by means of an autoregressive model. First, historical traffic data is formatted as a time series, which typically includes information of traffic flow, vehicle type, speed, etc. at each point in time. The key to the autoregressive model is to calculate the autocorrelation in the time series, i.e., how the current traffic flow is affected by the traffic flow at the previous time. For this purpose, model parameters, such as hysteresis numbers, are optimally selected according to the red pool information criterion (AIC) or the Bayesian Information Criterion (BIC). By the method, the model can predict the change of the traffic flow at a plurality of time points in the future, and the time dependence of the traffic flow data is revealed. The generated traffic flow trend prediction is of vital importance for urban traffic planning and management, helps decision makers understand future traffic patterns and adjusts traffic management measures accordingly.

In the sub-step S302, traffic flow trend prediction data is processed using an exponential smoothing method. The exponential smoothing method effectively highlights the influence of recent data on future traffic flow by giving higher weight to the recent data, and weakens the influence of random fluctuation on prediction, in the process, the selection of smoothing parameters (such as alpha value) is important, and the smoothing parameters are usually adjusted according to the fluctuation of historical data. Through the exponential smoothing process, the generated short-term traffic flow prediction can more accurately reflect the recent traffic trend, and powerful data support is provided for short-term traffic management.

In a sub-step S303, a seasonal autoregressive integrated moving average (SARIMA) model is applied to analyze and predict traffic flow. The SARIMA model introduces seasonal components on the basis of the traditional ARIMA model, which is crucial for understanding and predicting the traffic flow of seasonal changes (e.g. holidays, peak travel hours, etc.). The model is built up by determining the seasonal period, the autoregressive term, the number of differences and the number of moving average terms. Optimization and validation of the SARIMA model relies on seasonal pattern analysis of historical data. The generated seasonal traffic flow analysis result is helpful for predicting traffic flow in specific seasons or dates, and has important significance for traffic planning in holidays and special events.

In the step S304, traffic flow data is clustered by a cluster analysis method. The goal of cluster analysis is to divide traffic flow data with similar characteristics into different groups, e.g., by rush hour, congested road segments, or different traffic patterns, which involves selecting an appropriate clustering algorithm (e.g., k-means or hierarchical clustering) and determining the number of clusters. By analyzing and comparing the characteristics of the different groups, it is possible to understand more deeply the characteristics of the traffic flow, such as why congestion is often occurring in certain time periods or road segments. The generated traffic flow prediction analysis result has practical significance for preventing and relieving traffic jams.

It is envisioned that a city traffic monitoring system gathers annual traffic flow data including number of vehicles per hour, average vehicle speed and vehicle type distribution. In step S301, the data is analyzed by an autoregressive model, such as selecting 3 lag phases, which model reveals the apparent periodicity of the morning and evening peaks of the workday. In step S302, an exponential smoothing method is applied, and the smoothing parameter α is set to 0.3, so that a traffic flow prediction per hour in the future week is generated. Next, in step S303, the SARIMA model analyzes traffic flow changes on holidays and predicts traffic flow during upcoming national celebrations. Finally, in step S304, a specific peak period and a road section that is easy to be congested in the city are identified through cluster analysis. The analysis results can help urban traffic management departments to plan traffic flow more effectively and reduce traffic jams.

Referring to fig. 5, based on traffic flow prediction analysis, an optimization theory and a traffic engineering method are adopted to perform optimization design of traffic flow and route, and a traffic signal adjustment strategy is formulated, so that the steps of generating a traffic jam relief scheme are specifically as follows:

s401: based on traffic flow prediction analysis, setting traffic flow of multiple road sections of a road network as a decision variable, taking road capacity and traffic demand as constraint conditions, distributing the flow of each road by solving an optimization problem, reducing the congestion condition of traffic thoroughfares, and generating a road network flow optimization design;

S402: based on the road network flow optimization design, graph theory analysis is applied, including constructing a traffic network graph, wherein an intersection is taken as a node, a road section is taken as an edge, the shortest path and flow distribution among the nodes are calculated, the route design is adjusted according to the flow distribution, the overall efficiency of the road network is optimized, and route optimization configuration is generated;

S403: based on route optimization configuration, a dynamic traffic distribution model is applied, the time sequence and the period of the signal lamp are updated according to real-time traffic data, a signal lamp control system is adjusted in real time, traffic peak period flow change is matched, traffic delay is reduced, and a traffic signal optimization strategy is generated;

S404: based on a traffic signal optimization strategy, a road network flow optimization design and a route optimization configuration, a comprehensive traffic management strategy is formulated, road network layout and signal control are adjusted, emergency measures are proposed to cope with emergency, traffic jam is relieved, and a traffic jam relieving scheme is generated;

Setting traffic flow of multiple road sections of a road network as a decision variable, taking the road capacity and traffic demand as constraint conditions, and distributing the flow of each road by solving an optimization problem by adopting an LWR model formula;

In the S401 substep, the optimization design of the road network flow is carried out through a linear programming algorithm. First, the traffic flow of a plurality of road segments in a road network is used as a decision variable, and the data format generally includes the traffic flow, the speed and the type of the vehicle for each road segment. In building the linear programming model, road capacity and traffic demand are set as constraints, which involve accurate calculation of maximum load carrying capacity and actual demand for each road segment. The objective of the optimization problem is to minimize the congestion level of the entire road network, which can be achieved by different algorithms, such as simplex or interior point methods. In the solving process, the algorithm adjusts the flow distribution of each road so as to achieve the purpose of reducing the main traffic trunk congestion. The generated road network flow optimization design not only improves the running efficiency of the road network, but also helps to relieve traffic jams, and has important significance for urban traffic management.

In the step S402, route optimization configuration is performed by using graph theory analysis. The initial step includes constructing a traffic network graph with intersections as nodes and road segments as edges. The data format mainly relates to the distance between nodes, traffic capacity and actual traffic. The shortest path between nodes is calculated using a graph theory algorithm, such as the dijkstra algorithm or the florid algorithm, which is important for understanding the distribution and dynamics of traffic flow. Based on the analysis result of the flow distribution, the route design can be adjusted, and the overall efficiency of the road network is optimized. The generated route optimization configuration not only improves the efficiency of the traffic system, but also enhances the capability of coping with extreme conditions (such as accidents or road maintenance).

In the step S403, a dynamic traffic distribution model is applied to make a traffic signal optimization strategy. The model updates the timing and period of the signal lights based on real-time traffic data, such as vehicle number, speed, and waiting time. In the implementation process, the model considers traffic flow and steering requirements of each intersection, and an optimization algorithm such as a genetic algorithm or a simulated annealing method is used for adjusting control parameters of the signal lamp. By the method, the signal system can respond to the change of traffic flow in real time, especially in the peak period of traffic, and traffic delay is effectively reduced. The generated traffic signal optimization strategy is beneficial to improving the traffic efficiency of the road network and reducing the vehicle emission, and has positive effects on environmental protection and sustainable development of urban traffic.

In the step S404, comprehensive traffic signal optimization strategies, road network flow optimization design and route optimization configuration are synthesized, and comprehensive traffic management strategies are formulated, wherein the process involves overall adjustment of road network layout and signal control, and the coping strategies of different traffic modes and special situations (such as accidents, large-scale activities and the like) are considered. The data format includes layout information of the road network, traffic flow data and signal lamp parameters. By various analysis and adjustment, emergency measures are made to cope with emergency, and traffic jam is relieved. The generated traffic jam relieving scheme not only improves the efficiency and the safety of urban traffic, but also enhances the capability of coping with emergencies.

Assuming that the primary traffic network of a city contains multiple intersections and road segments, the collected data includes average traffic flow, speed, and road capacity for each segment. In step S401, the flow distribution of each road segment is optimized by a linear programming algorithm, such as simplex method, so as to reduce the congestion of several key road segments. In step S402, a traffic network graph is constructed, and the shortest path between nodes is calculated, optimizing the route configuration. In step S403, the timing of the signal lights is adjusted based on the real-time data, particularly during the early-late peak hours, to reduce traffic delays. Finally, in step S404, these policies are integrated, and a comprehensive traffic management policy is formulated, so that traffic jam in the city is effectively relieved.

Referring to fig. 6, in combination with urban meteorological data and a traffic jam relief scheme, an interaction between a heat island effect and traffic flow is analyzed by adopting a lattice boltzmann model, and an influence evaluation of urban temperature distribution is performed, and the steps for generating the urban heat island traffic influence evaluation are specifically as follows:

s501: based on urban meteorological data and a traffic jam relieving scheme, adopting a lattice Boltzmann model to simulate a heat island effect, simulating urban heat environment by simulating movement and collision of fluid microscopic particles, realizing meteorological data simulation of multiple areas of a city, analyzing potential influence of the traffic jam relieving scheme on the heat island effect, and generating an initial simulation diagram of urban heat distribution;

S502: based on an urban heat distribution initial simulation diagram, combining traffic flow data, analyzing the influence of traffic flow on an urban heat island effect by adopting a cellular automaton model, simulating traffic density change by defining a local rule of the traffic flow, evaluating the direct influence of the traffic flow on urban temperature distribution, and generating a traffic flow heat influence analysis diagram;

S503: based on a traffic flow heat influence analysis graph, combining traffic flow and meteorological data by adopting a data fusion technology, analyzing interaction of traffic flow and meteorological conditions by integrating and analyzing multi-source data, evaluating comprehensive influence of the interaction on a heat island effect, and generating a heat island traffic interaction comprehensive graph;

S504: based on the heat island traffic interaction comprehensive graph, the urban temperature distribution influence evaluation model is applied to comprehensively influence and evaluate the urban temperature distribution, a countermeasure scheme is provided by quantitatively analyzing the influence of the heat island effect and the interaction of the heat island effect and traffic flow on the urban environment, the comprehensive evaluation of the urban heat environment is performed, and the urban heat island traffic influence evaluation is generated.

In the S501 substep, based on urban meteorological data and a traffic jam relief scheme, a lattice Boltzmann model is adopted to simulate a heat island effect. First, meteorological data for multiple areas of a city, including temperature, humidity, wind speed, etc., as well as traffic flow data, are collected. The lattice boltzmann model simulates urban thermal environments by simulating the movement and collision of fluid microscopic particles, and requires setting of mesh sizes and boundary conditions of the model, such as distribution of urban buildings, green land coverage, and the like. The model calculation process involves calculating the particle distribution function at each grid point to obtain macroscopic quantities such as density and temperature. Through the simulation, an initial simulation diagram of urban heat distribution can be generated, which not only reveals the heat environment characteristics of the city, but also analyzes the potential influence of a traffic jam relieving scheme on the heat island effect.

In the step S502, the influence of the traffic flow on the urban heat island effect is analyzed by using a cellular automaton model in combination with the urban heat distribution initial simulation map and the traffic flow data. The cellular automaton model simulates traffic density changes by defining local rules of traffic flow. The data formats include traffic flow and road network layout, and the rule set takes into account movement, stopping and speed changes of the vehicle. Through simulation, the direct influence of traffic flow on urban temperature distribution can be evaluated, and a traffic flow heat influence analysis chart is generated. This step is important to understand how traffic flow affects the temperature distribution of a city, and helps to further optimize traffic management strategies and reduce heat island effects.

In a substep S503, the data fusion technique is used to combine traffic flow with meteorological data and analyze the interaction of traffic flow and meteorological conditions, which involves integrating and analyzing multi-source data, such as meteorological observation data, satellite remote sensing data, and traffic monitoring data. After data fusion, the correlation between traffic flow changes and weather condition changes is assessed by data analysis means, such as multivariate statistical analysis. This comprehensive analysis helps understand how traffic flows interact with weather conditions, thereby affecting the heat island effect of the city. The generated heat island traffic interaction comprehensive graph provides important decision support information for city planners.

In the step S504, the urban temperature distribution influence evaluation model is applied to evaluate the overall influence of the urban temperature distribution. This involves a quantitative analysis of the impact of the heat island effect and its interaction with traffic flow on the urban environment. The model takes into account a number of factors such as urban structure, greenery, traffic flow and climatic conditions. In the evaluation process, historical and real-time data are utilized, and tools such as multiple regression analysis, geographic Information System (GIS) and the like are utilized to analyze influence factors of urban temperature distribution. The comprehensive evaluation result can generate an urban heat island traffic influence evaluation graph, provides scientific basis for urban planning and management, and helps to formulate an effective strategy to reduce heat island effect.

It is assumed that a city collects weather data and traffic flow data, including air temperature, humidity, wind speed, and traffic flow for each major road segment. In step S501, the thermal environment of the city is simulated using the boltzmann model of the lattice, revealing the temperature difference between the urban center and suburban area. In step S502, the cellular automaton model simulates the change in traffic density, and finds that the peak traffic period has a significant effect on the temperature in certain areas of the city. In step S503, the data fusion technique reveals the correlation between traffic flow and climate conditions, and generates a heat island traffic interaction comprehensive map. Finally, in step S504, the urban temperature distribution influence evaluation model analyzes the influence of each factor on the urban temperature distribution, and proposes a strategy suggestion for reducing the heat island effect.

Referring to fig. 7, based on urban heat island traffic impact assessment, a system dynamics method is adopted to make a traffic management strategy to slow down heat island effect, and an adjustment scheme design of a traffic system is performed, so that the steps of generating heat island effect slow down traffic planning are specifically as follows:

S601: based on an urban heat island traffic influence evaluation graph, analyzing by adopting a system dynamics method, simulating the influence of a differentiated traffic management strategy on the heat island effect by establishing a dynamic model of urban traffic flow, heat island effect and interaction thereof, and generating a traffic management strategy dynamic analysis result by referring to traffic flow, speed and urban layout factors;

s603: based on a traffic strategy optimization scheme for slowing down the heat island effect, adopting Monte Carlo simulation, simulating a plurality of scenes after strategy implementation by a random sampling technology, predicting the performance of a new strategy under a plurality of prediction environment conditions, evaluating the feasibility and effect of the strategy, and generating a strategy implementation simulation prediction result;

S604: based on the strategy implementation simulation prediction result, an urban traffic planning optimization model is adopted to adjust and design a traffic system, and the traffic planning is slowed down by referring to traffic flow distribution and road network optimization to generate a heat island effect.

In the S601 substep, dynamic analysis of traffic management strategies is performed by adopting a system dynamics method based on the urban heat island traffic impact evaluation graph. First, a dynamic model of urban traffic flow, heat island effects and their interactions is built. The data formats include urban traffic flow, speed, urban layout factors (e.g., building density, green land coverage), and weather data. The system dynamics model is used herein to model and analyze the dynamic relationships and interactions between these factors. In the process of establishing a model, the key lies in defining the relationship between factors, such as the relationship between traffic flow and heat island effect, and how the city layout affects the relationship.

In the model operation process, the influence of different traffic management strategy parameters, such as traffic limitation, road network adjustment and the like, on the heat island effect is simulated by setting the strategy parameters. The system dynamics model has the advantage of being capable of showing the variation trend of urban heat island effect and traffic flow dynamics under different strategies. The generated dynamic analysis result of the traffic management strategy helps a decision maker to understand the long-term effect and potential influence brought by different management strategies, and provides support for formulating more effective traffic and environmental policies.

In the step S602, based on the dynamic analysis result of the traffic management policy, the effectiveness of different traffic management policies is evaluated and compared by adopting a multi-criterion decision analysis method. In this process, urban traffic data and environmental impact parameters, such as reduced greenhouse gas emissions, improved traffic flow and slowed heat island effects, are integrated. The multi-criterion decision analysis method involves weighing the various effects of different strategies, including economic cost, environmental benefit and social impact.

By evaluating the effect of different strategies in terms of mitigating the heat island effect, the generated traffic strategy optimization scheme provides a hole on how to balance urban development and environmental protection, and the importance of the steps is that not only the direct effect of traffic management strategies but also the long-term sustainability and social acceptance of the strategies are considered.

In the step S603, based on the traffic policy optimization scheme for slowing down the heat island effect, the implementation effect of the new policy is predicted by adopting monte carlo simulation. In this process, random sampling techniques are used to simulate a variety of scenarios after policy enforcement. This includes scenarios in different climatic conditions, traffic flow variations and city development patterns. The key to Monte Carlo simulation is the ability to provide the behavior of a policy under uncertainty conditions, thereby assessing the feasibility and effectiveness of the policy.

The generated simulated prediction result of the policy implementation provides a view to a decision maker regarding various results after the policy implementation. This helps identify those policies that remain valid under a variety of conditions, as well as the portion of the policies that need to be adjusted.

In the step S604, based on the result of the policy implementation simulation prediction, the urban traffic planning optimization model is adopted to adjust and design the traffic system, and the steps involve comprehensively considering traffic flow distribution and road network optimization. The urban traffic planning optimization model is used here to evaluate the effect of different road network designs and traffic management measures on improving the heat island effect.

By means of this model, specific traffic planning schemes, such as road reconstruction, traffic signal optimization and improvement of public transportation systems, can be generated. These schemes not only consider traffic efficiency and safety, but also how to mitigate the impact of traffic on urban thermal environments. The generated heat island effect slowing down traffic planning provides a comprehensive scheme for improving traffic conditions and reducing environmental impact for cities.

In one specific city case, city traffic flow data, weather data, and city layout information are collected. In step S601, the system dynamics model analyzes the impact of different traffic management strategies on the heat island effect, and finds that some strategies can significantly reduce the temperature of the urban central area. In step S602, a multi-criterion decision analysis reveals the advantages of these strategies in terms of reducing greenhouse gas emissions and improving traffic efficiency. The monte carlo simulation in step S603 shows that these strategies perform well in a variety of climates and traffic conditions. Finally, in step S604, a comprehensive traffic planning scheme is formulated based on the analysis results, aiming at alleviating urban heat island effect by optimizing traffic flow distribution and road network.

Referring to fig. 8, the comprehensive traffic congestion relieving scheme and the heat island effect relieving traffic planning are implemented by adopting genetic algorithm and particle swarm optimization, and the comprehensive balance and optimization strategy design of multiple factors are implemented, and the steps for generating the comprehensive carbon emission reduction strategy optimization are specifically as follows:

S701: based on a comprehensive traffic jam relief scheme and a heat island effect, slowing down traffic planning, adopting a genetic algorithm, optimizing traffic route selection and traffic flow distribution through simulation selection, intersection and variation, and obtaining optimized traffic system configuration;

S702: based on the optimized traffic system configuration, adopting a particle swarm optimization algorithm, simulating a shotgun hunting behavior of the particle swarm, optimizing traffic signal control and road network design so as to improve the efficiency of traffic flow and reduce the influence of heat island effect, and generating an optimized traffic control and road network design scheme;

s703: based on the optimized traffic control and road network design scheme, adopting a multi-objective genetic algorithm, capturing an optimal solution by simulating natural selection and genetic mechanism among a plurality of targets comprising reducing traffic jam, reducing heat island effect and carbon emission, balancing the multi-targets, and generating a comprehensive optimized multi-objective traffic strategy;

S704: based on comprehensive optimized multi-target traffic strategies, a particle swarm optimization algorithm is adopted, the positions and speeds of a plurality of particles representing a potential traffic strategy scheme are iteratively updated by simulating the movement and searching behaviors of particle swarms, each particle is closed to the historical optimal position and the global optimal position of an individual, the traffic strategies are continuously adjusted and optimized, the original strategies are refined, and comprehensive carbon emission reduction strategy optimization is generated.

In the step S701, traffic planning is slowed down based on the integrated traffic congestion mitigation scheme and the heat island effect, and traffic route selection and traffic flow distribution are optimized through a genetic algorithm. First, data of the traffic system including road network layout, traffic flow, vehicle type, and traveling speed are collected and organized. Genetic algorithms are used herein to simulate natural selection processes to optimize traffic configurations through selection, crossover and mutation operations. In the initialization phase, a set of random solutions is generated as an initial population, each solution representing a traffic system configuration.

The selection process evaluates the advantage of each solution according to a fitness function, which typically involves evaluating traffic flow distribution, delay time, and vehicle emissions, among other metrics. The crossover and mutation operations are used to generate a new solution, where the crossover operation combines features of two solutions, while the mutation operation randomly alters some parts of the solution. This optimization process is iterated through multiple generations until a certain condition (e.g., number of iterations or fitness threshold) is reached.

By the method, the optimized traffic system configuration can be obtained, and the configuration aims at balancing traffic flow distribution, reducing traffic jam and reducing heat island effect. The generated optimal configuration can be used for guiding actual traffic planning and management decision, and the efficiency and environmental friendliness of the whole traffic system are improved.

In the step S702, based on the optimized traffic system configuration, the traffic signal control and the road network design are further optimized by adopting a particle swarm optimization algorithm. The particle swarm optimization algorithm simulates the shotgun behavior of a swarm, and each particle represents a traffic control and road network design scheme. In the initial stage, particles are randomly placed within the search space, each particle having a velocity that is indicative of the direction and distance it moves in the solution space.

In an iterative process, each particle updates its position based on its own experience and the experience of the other particles in the population. This includes directing the particles towards their historical optimal location and the current global optimal location. By the method, the algorithm gradually finds optimal traffic control and road network design schemes, and the schemes can effectively improve the efficiency of traffic flow and reduce the influence of heat island effect. The generated scheme provides practical guidance for urban traffic management and is beneficial to improving the overall performance of an urban traffic system.

In S703, based on the optimized traffic control and road network design, a multi-objective genetic algorithm is used to balance the multiple optimization objectives. These objectives include reducing traffic congestion, reducing heat island effects, and reducing carbon emissions. The multi-objective genetic algorithm is used here to consider multiple objectives simultaneously, finding the best overall solution. In the execution of the algorithm, solutions with high fitness are generated by simulating natural selection and genetic mechanisms.

In this process, it is critical to define an fitness function that can comprehensively consider all targets. The algorithm needs to be able to identify a solution that achieves the best balance among the multiple targets. The generated multi-target traffic strategy provides a comprehensive view for urban traffic planning, so that a decision maker can consider environmental protection and sustainable development while improving traffic efficiency.

In the step S704, based on the comprehensively optimized multi-objective traffic strategy, the particle swarm optimization algorithm is applied again to continuously adjust and optimize the traffic strategy. In this process, each particle represents a potential traffic strategy scheme, and the algorithm iteratively updates the position and velocity of the particle to search for the optimal solution. The updating process of the particles is based on the information of the individual historical optimal position and the global optimal position, so that the particles can be ensured to effectively explore the solution space.

By the method, the particle swarm optimization algorithm can refine and optimize the original traffic strategy, and a more accurate and effective comprehensive carbon emission reduction strategy is generated. These strategies not only take into account traffic efficiency, but also include environmental impact considerations, providing a more sustainable traffic planning scheme for cities.

In the case of one particular city, the traffic system data is used to initialize genetic algorithms and particle swarm optimization algorithms. Through iterative operation of the algorithms, traffic route selection, traffic flow distribution, traffic signal control and road network design of the city are optimized. Finally, a set of comprehensive traffic management strategies aimed at improving traffic efficiency, reducing heat island effect and reducing carbon emission are generated. These strategies are further adjusted by the particle swarm optimization algorithm to form a customized traffic planning scheme aiming at specific city conditions.

Referring to fig. 9, based on the optimization of the comprehensive carbon emission reduction strategy, the comprehensive assessment and planning of the carbon emission of the urban traffic are performed by adopting life cycle assessment and cost benefit analysis, and the steps for generating the comprehensive planning result of the urban carbon neutralization are specifically as follows:

S801: based on comprehensive carbon emission reduction strategy optimization, adopting a statistical analysis method to perform trend analysis and difference exploration on carbon emission data of urban traffic, then adopting a machine learning prediction model to analyze future trend through random forests, and adopting a neural network to mine potential modes so as to perform comprehensive prediction analysis and generate a carbon emission current situation analysis result;

S802: based on the analysis result of the current situation of carbon emission, a life cycle assessment model is adopted to analyze the energy and material flows of multiple links of the transportation junction, and the deep association of the energy and material flows with the carbon emission is analyzed, so that the life cycle assessment is carried out, and the life cycle carbon assessment of the transportation system is generated;

S803: based on the life cycle carbon evaluation of the traffic system, the economic value of the differentiated emission reduction strategy is analyzed by using a present value method by using a cost benefit analysis method, the financial return is evaluated by using an internal benefit rate method, and the optimized emission reduction strategy is selected to generate an optimized emission reduction strategy analysis;

s804: based on the analysis of the optimizing emission reduction strategy, combining a system dynamic simulation method and a multi-objective optimizing model, simulating the behavior of the urban traffic system through a system dynamic theory, analyzing various solutions by using a genetic algorithm, capturing an optimal balance point by using a linear programming, and formulating a carbon neutralization planning scheme matched with urban characteristics to generate an urban carbon neutralization comprehensive planning result.

In the S801 substep, based on comprehensive carbon emission reduction strategy optimization, a statistical analysis method is adopted to conduct trend analysis and difference exploration on carbon emission data of urban traffic. First, carbon emission data of urban traffic systems are collected, and the format of these data generally includes the carbon emission amount of each traffic mode (such as automobiles, buses, rail traffic) arranged in time series. In the data preprocessing stage, the data are cleaned and normalized to ensure the accuracy of analysis.

Subsequently, using statistical analysis methods, such as time series analysis, to explore trends and patterns in the carbon emissions data, the steps involve using statistical models, such as autoregressive moving average (ARMA) models, to evaluate seasonal and long-term trends in the data. Next, machine learning predictive models, such as random forests and neural networks, are employed to analyze future trends and mine potential patterns, respectively. Random forest models are particularly effective in processing data with high dimensional features, while neural networks are suitable for identifying complex nonlinear relationships.

By the method, the generated carbon emission current analysis result provides deep understanding of carbon emission influencing factors of the urban traffic system and data support for future emission reduction strategies. These results are critical to the establishment of effective traffic abatement measures and environmental policies.

In the S802 substep, based on the analysis result of the current carbon emission situation, a life cycle assessment model is adopted to analyze the energy and the material flow of the transportation junction multi-link. The life cycle assessment model takes into account the energy consumption and material flow of the traffic system from construction, operation to abandonment. The data formats include energy consumption and emissions data during production, use and scrapping of the vehicle.

The key to life cycle assessment is to analyze the association of various links of the traffic system with carbon emissions. This involves quantifying the contribution of each link to the total carbon emissions and identifying key links to implement effective emissions abatement measures. The generated traffic system lifecycle carbon assessment report provides an overall view of the environmental impact of the traffic system, helping to identify key intervention points that reduce the overall system carbon footprint.

In the S803 substep, based on the traffic system lifecycle carbon assessment, different emission reduction strategies are economically evaluated using a cost-benefit analysis method. Cost-benefit analysis involves quantifying the economic value of the emission abatement strategy using a present value method, while evaluating the financial return using an internal rate of return method. The data formats include implementation costs of emission reduction strategies, operational costs, and environmental costs that are expected to be saved.

Through cost-effectiveness analysis, the economic feasibility and effectiveness of different emission abatement strategies may be compared. The generated optimized emission reduction strategy analysis provides an emission reduction scheme which balances between economy and environment for a decision maker, so that the emission reduction strategy with highest cost performance can be selected.

In the S804 substep, an urban carbon neutralization planning scheme is formulated based on the optimization emission reduction strategy analysis and by combining a system dynamic simulation method and a multi-objective optimization model. The system dynamic simulation simulates the behavior of urban traffic systems, while genetic algorithms and linear programming are used to analyze and capture the optimal balance point among a variety of solutions. The data formats include traffic flow, carbon emissions, and city development parameters.

By these methods, carbon neutral planning schemes suitable for urban characteristics can be generated, which consider the dynamics of the traffic system and various influencing factors. The generated urban carbon neutralization comprehensive planning result is not only helpful for realizing the environmental target, but also can promote the sustainable development of the urban traffic system.

Traffic system data for a city, including carbon emissions and lifecycle energy consumption data for traffic patterns, is used to initialize the analysis. In step S801, the current situation and trend of carbon emission of urban traffic are analyzed through statistical analysis and machine learning models. The life cycle assessment in step S802 reveals the carbon emission contribution of the different links in the traffic system. The cost-benefit analysis of step S803 determines the most cost-effective emission abatement strategy. Finally, in step S804, a carbon neutralization planning scheme conforming to city characteristics is formulated through system dynamic simulation and multi-objective optimization.

The present invention is not limited to the above embodiments, and any equivalent embodiments which can be changed or modified by the technical disclosure described above can be applied to other fields, but any simple modification, equivalent changes and modification made to the above embodiments according to the technical matter of the present invention will still fall within the scope of the technical disclosure.

Claims

1. The urban road carbon neutralization estimation analysis method is characterized by comprising the following steps of:

Based on traffic monitoring data of urban key intersections and roads, comprehensively analyzing the number, the speed and the types of vehicles by adopting a data fusion and chaos theory method, and performing primary analysis on traffic flow situations to generate urban traffic real-time data analysis;

Based on the urban traffic real-time data analysis, adopting time sequence analysis and nonlinear dynamic modeling to explore traffic peaks and congestion modes, and identifying flow change trend to generate traffic flow dynamic characteristic identification;

based on the traffic flow dynamic characteristic identification, adopting an autoregressive model to predict trend of future traffic flow, analyzing peak time period and congestion road sections, and generating traffic flow prediction analysis;

Based on the traffic flow prediction analysis, adopting an optimization theory and a traffic engineering method to optimally design traffic flow and routes, and formulating a traffic signal adjustment strategy to generate a traffic jam relief scheme;

Combining urban meteorological data and the traffic jam relieving scheme, analyzing the interaction between a heat island effect and traffic flow by adopting a lattice Boltzmann model, and carrying out influence assessment of urban temperature distribution to generate urban heat island traffic influence assessment;

Based on the urban heat island traffic influence assessment, a system dynamics method is adopted to make a traffic management strategy to slow down the heat island effect, and an adjustment scheme design of a traffic system is carried out to generate a heat island effect slow down traffic plan;

the traffic jam relieving scheme and the heat island effect relieving traffic planning are synthesized, genetic algorithm and particle swarm optimization are adopted, comprehensive balance and optimization strategy design of multiple factors are carried out, and comprehensive carbon emission reduction strategy optimization is generated;

Based on the comprehensive carbon emission reduction strategy optimization, carrying out comprehensive assessment and planning of urban traffic carbon emission by adopting life cycle assessment and cost benefit analysis, and generating an urban carbon neutralization comprehensive planning result;

based on the urban traffic real-time data analysis, adopting time sequence analysis and nonlinear dynamic modeling to explore traffic peaks and congestion modes and identify flow change trend, the step of generating traffic flow dynamic feature identification specifically comprises the following steps:

Based on the urban traffic real-time data analysis, adopting an autoregressive moving average model, and carrying out statistical modeling on time sequence data to analyze autocorrelation and moving average characteristics in historical traffic data, reveal periodic changes of traffic flow and generate traffic flow periodic analysis;

Based on the periodic analysis of traffic flow, using a system dynamics model, simulating dynamic changes of traffic flow by establishing and analyzing differential equations describing the traffic flow, including modes of peak and congestion periods, and generating traffic peak and congestion mode analysis;

Based on the traffic peak and congestion pattern analysis, a network analysis method is applied, and the mutual influence and the dependency relationship between different road segments are analyzed by calculating the connectivity and cluster coefficients of multiple nodes in the road network, and key nodes and weak links of the traffic network are identified to generate traffic network interactive analysis;

based on the traffic network interactivity analysis, adopting multiple regression analysis, and quantitatively predicting the change trend of the future traffic flow by combining historical and real-time data through statistical modeling, wherein the change trend comprises potential growing points and traffic bottleneck areas, so as to generate traffic flow dynamic characteristic identification;

Wherein, Is the emotion observation value of the current time point,/>Is a lag operator of time,/>Is an autoregressive coefficient,/>Is a moving average coefficient,/>Is the difference times/>White noise error;

Based on the traffic flow dynamic characteristic identification, adopting an autoregressive model to predict the trend of the future traffic flow, analyzing the peak time period and the congestion road section, and generating traffic flow prediction analysis specifically comprises the following steps:

Based on the traffic flow dynamic characteristic identification, an autoregressive model is adopted, the change of traffic flow at a plurality of time points in the future is predicted by calculating the autocorrelation in the time sequence of historical traffic data, and the time dependence of the traffic flow data is analyzed and simulated to generate traffic flow trend prediction;

based on the traffic flow trend prediction, carrying out weighted average processing on the prediction data by using an exponential smoothing method, highlighting the influence of recent data on future traffic flow, weakening the influence of random fluctuation on the prediction, and generating short-term traffic flow prediction;

Based on the short-term traffic flow prediction, a seasonal autoregressive comprehensive moving average model is used for analyzing seasonal changes in historical data and predicting traffic flow of future target seasons or dates, including comprehensive analysis of autoregressions and moving averages, and seasonal traffic flow analysis is generated by referring to seasonal factors;

Based on the seasonal traffic flow analysis, dividing the data into different groups according to the characteristics of traffic flow data through cluster analysis, identifying peak time and congestion road sections, and generating traffic flow prediction analysis by quantifying the similarity and the difference of the traffic flow data and highlighting key traffic characteristics;

Based on the traffic flow prediction analysis, adopting an optimization theory and a traffic engineering method to optimally design traffic flow and routes, and formulating a traffic signal adjustment strategy, the steps of generating a traffic jam relief scheme are as follows:

Based on the traffic flow prediction analysis, setting traffic flow of multiple road sections of a road network as a decision variable by adopting a linear programming algorithm, taking the road capacity and traffic demand as constraint conditions, distributing the flow of each road by solving an optimization problem, reducing the congestion condition of traffic thoroughfares, and generating a road network flow optimization design;

based on the road network flow optimization design, graph theory analysis is applied, wherein a traffic network graph is constructed, intersections are used as nodes, road sections are used as edges, shortest paths and flow distribution among the nodes are calculated, the route design is adjusted according to the flow distribution, the overall efficiency of the road network is optimized, and route optimization configuration is generated;

based on the route optimization configuration, a dynamic traffic distribution model is applied, the time sequence and the period of the signal lamp are updated according to real-time traffic data, a signal lamp control system is adjusted in real time, traffic peak period flow changes are matched, traffic delay is reduced, and a traffic signal optimization strategy is generated;

Based on the traffic signal optimization strategy, the road network flow optimization design and the route optimization configuration, a comprehensive traffic management strategy is formulated, road network layout and signal control are adjusted, emergency measures are proposed to deal with emergency situations, traffic jams are relieved, and a traffic jam relieving scheme is generated;

Wherein, For vehicle density, vehicle density at location x and time t is expressed,/>Representing the product of density and speed as a flow function;

Combining urban meteorological data and the traffic jam relieving scheme, adopting a lattice Boltzmann model to analyze the interaction between a heat island effect and traffic flow, and carrying out influence assessment of urban temperature distribution, wherein the step of generating the urban heat island traffic influence assessment specifically comprises the following steps:

Based on urban meteorological data and the traffic jam relieving scheme, adopting a lattice Boltzmann model to simulate a heat island effect, simulating urban heat environment by simulating movement and collision of fluid microscopic particles, realizing meteorological data simulation of multiple areas of a city, analyzing potential influence of the traffic jam relieving scheme on the heat island effect, and generating an initial simulation diagram of urban heat distribution;

Based on the urban heat distribution initial simulation diagram, combining traffic flow data, analyzing the influence of traffic flow on an urban heat island effect by adopting a cellular automaton model, simulating traffic density change by defining a local rule of the traffic flow, evaluating the direct influence of the traffic flow on urban temperature distribution, and generating a traffic flow heat influence analysis diagram; based on the traffic flow heat influence analysis graph, combining traffic flow and meteorological data by adopting a data fusion technology, analyzing interaction of traffic flow and meteorological conditions by integrating and analyzing multi-source data, evaluating comprehensive influence of the interaction on a heat island effect, and generating a heat island traffic interaction comprehensive graph;

Based on the heat island traffic interaction comprehensive graph, an urban temperature distribution influence evaluation model is applied to comprehensively influence and evaluate urban temperature distribution, a countermeasure scheme is provided by quantitatively analyzing the influence of a heat island effect and the interaction of the heat island effect and traffic flow on urban environment, comprehensive evaluation of the urban heat environment is performed, and urban heat island traffic influence evaluation is generated;

Based on the urban heat island traffic influence assessment, a system dynamics method is adopted to make a traffic management strategy to slow down the heat island effect, an adjustment scheme design of a traffic system is carried out, and the steps of generating the heat island effect slow down traffic planning are specifically as follows: based on the urban heat island traffic influence assessment graph, analyzing by adopting a system dynamics method, simulating the influence of a differentiated traffic management strategy on the heat island effect by establishing a dynamic model of urban traffic flow, heat island effect and interaction thereof, and generating a traffic management strategy dynamic analysis result by referring to traffic flow, speed and urban layout factors;

Based on the dynamic analysis result of the traffic management strategy, adopting multi-criterion decision analysis, and combining urban traffic data and environmental influence parameters, evaluating and comparing the effectiveness of the differentiated traffic management strategy in the aspect of slowing down the heat island effect, and generating a traffic strategy optimization scheme for slowing down the heat island effect;

Based on the traffic strategy optimization scheme for slowing down the heat island effect, adopting Monte Carlo simulation, simulating a plurality of scenes after strategy implementation by a random sampling technology, predicting the performance of a new strategy under a plurality of prediction environment conditions, evaluating the feasibility and effect of the strategy, and generating a strategy implementation simulation prediction result;

based on the strategy implementation simulation prediction result, adopting an urban traffic planning optimization model to adjust and design a traffic system, and referring to traffic flow distribution and road network optimization, generating a heat island effect to slow down traffic planning;

the traffic jam relieving scheme and the heat island effect relieving traffic planning are synthesized, genetic algorithm and particle swarm optimization are adopted, comprehensive balance and optimization strategy design of multiple factors are carried out, and the steps for generating comprehensive carbon emission reduction strategy optimization are specifically as follows:

Based on the comprehensive traffic jam relief scheme and the heat island effect, traffic planning is relieved, a genetic algorithm is adopted, and the traffic route selection and traffic flow distribution are optimized through simulation selection, intersection and variation, so that the optimized traffic system configuration is obtained;

based on the optimized traffic system configuration, adopting a particle swarm optimization algorithm, simulating a shoring behavior of a bird swarm, optimizing traffic signal control and road network design so as to improve the efficiency of traffic flow and reduce the influence of heat island effect, and generating an optimized traffic control and road network design scheme;

based on the optimized traffic control and road network design scheme, adopting a multi-objective genetic algorithm, capturing an optimal solution by simulating natural selection and genetic mechanism among a plurality of targets comprising reducing traffic congestion, reducing heat island effect and carbon emission, balancing the multi-targets, and generating a comprehensive optimized multi-objective traffic strategy;

Based on the comprehensively optimized multi-target traffic strategy, adopting a particle swarm optimization algorithm, and simulating the motion and search behaviors of a particle swarm to iteratively update the positions and speeds of a plurality of particles representing a potential traffic strategy scheme, wherein each particle is close to the individual historical optimal position and the global optimal position, continuously adjusting and optimizing the traffic strategy, refining the original strategy and generating comprehensive carbon emission reduction strategy optimization;

Based on comprehensive carbon emission reduction strategy optimization, comprehensive assessment and planning of urban traffic carbon emission are carried out by adopting life cycle assessment and cost benefit analysis, and the steps for generating an urban carbon neutralization comprehensive planning result are specifically as follows:

Based on the comprehensive carbon emission reduction strategy optimization, adopting a statistical analysis method to perform trend analysis and difference exploration on carbon emission data of urban traffic, then adopting a machine learning prediction model to analyze future trend through random forests, and adopting a neural network to mine potential modes so as to perform comprehensive prediction analysis and generate a carbon emission current situation analysis result; based on the carbon emission current situation analysis result, analyzing the energy and material flows of multiple links of the transportation junction by adopting a life cycle assessment model, and analyzing the deep association of the energy and material flows with carbon emission so as to carry out life cycle assessment and generate life cycle carbon assessment of a transportation system;

Based on the life cycle carbon evaluation of the traffic system, a cost benefit analysis method is used for analyzing the economic value of the differentiated emission reduction strategy by a present value method, the financial return is evaluated by an internal benefit method, the optimized emission reduction strategy is selected, and the optimized emission reduction strategy analysis is generated;

Based on the optimization emission reduction strategy analysis, combining a system dynamic simulation method and a multi-objective optimization model, simulating the behavior of the urban traffic system through a system dynamic theory, analyzing various solutions by using a genetic algorithm, capturing an optimal balance point by using linear programming, and formulating a carbon neutralization planning scheme matched with urban characteristics to generate an urban carbon neutralization comprehensive planning result.

2. The urban road carbon neutralization estimation analysis method according to claim 1, characterized in that the urban traffic real-time data analysis comprises traffic density map, vehicle speed distribution and traffic flow type classification, the traffic flow state feature recognition comprises traffic flow stability assessment, congestion tendency recognition and traffic pattern change detection, the traffic flow prediction analysis comprises specifically future traffic peak prediction and potential congestion area prediction, the traffic congestion relief scheme comprises traffic signal adjustment planning, alternative route design and traffic guidance strategy, the urban heat island traffic impact assessment comprises road surface temperature change simulation, contribution assessment of traffic flow to heat island effect and mitigation measure effect prediction, the heat island effect mitigation traffic planning comprises traffic flow regulation strategy, public traffic priority scheme and green traffic popularization plan, the comprehensive carbon emission reduction strategy optimization comprises optimal traffic flow control strategy, energy saving emission reduction traffic mode and low-carbon excitation measures, and the urban carbon neutralization comprehensive planning result comprises carbon emission reduction target setting, strategy implementation path planning and long-term environmental benefit prediction.

3. The urban road carbon neutralization estimation analysis method according to claim 1, wherein the steps of carrying out comprehensive analysis of the number, speed and type of vehicles and carrying out preliminary analysis of traffic flow situation based on traffic monitoring data of urban key intersections and roads by adopting a data fusion and chaos theory method, and generating urban traffic real-time data analysis are specifically as follows:

based on traffic monitoring data of urban key intersections and roads, performing time synchronization on data collected by multiple sensors by adopting a data fusion algorithm, calibrating geographic positions corresponding to the data by using spatial positions, then applying weighted average, distributing differentiated weights according to the reliability of each sensor, integrating the data of multiple sources, and generating a comprehensive traffic flow data set;

Based on the comprehensive traffic flow data set, adopting a time sequence analysis algorithm, analyzing historical data through an autoregressive model to identify long-term trend and periodic variation of traffic flow, distinguishing frequency components in the traffic data through a spectrum analysis method, revealing periodic fluctuation and trend variation of traffic flow, and generating a vehicle dynamic analysis result;

Based on the dynamic analysis result of the vehicle, extracting speed and size characteristics of the vehicle through a support vector machine by using a machine learning classification algorithm, dividing boundaries of a characteristic space, classifying the vehicle types according to the extracted characteristics by using a decision tree algorithm, and generating a vehicle type classification overview;

Based on the vehicle type classification overview, a traffic flow state analysis method is used for carrying out probabilistic analysis on a plurality of states of traffic flow through a Markov chain model, predicting a conversion path of the traffic flow, carrying out quantitative analysis on the randomness of the traffic flow through a random process model, evaluating future traffic flow changes and generating urban traffic real-time data analysis.