Traffic convenience index evaluation method based on big data

Abstract

The invention relates to the technical field of traffic management, in particular to a traffic convenience index evaluation method based on big data, and the method comprises the following steps: receiving vehicle flow and driving speed data from a plurality of traffic monitoring nodes, transmitting the data to an analysis platform in real time, carrying out the vehicle speed and flow integration of each node, and obtaining a traffic flow state index. According to the invention, through real-time integration of the vehicle flow and the driving speed data of the monitoring nodes, the real-time performance and accuracy of the data are significantly improved, so that the traffic flow state monitoring is more accurate, urban planners and traffic management departments are assisted to quickly respond to changes, peak and valley periods are identified through analysis of daily and weekly changes, and the traffic flow state monitoring accuracy is improved. Through statistical analysis and calibration of flow abnormity, a management peak is effectively predicted, congestion is reduced, abnormal traffic flow changes are historically compared and analyzed, an emergency response plan is optimized, urban traffic adaptability is improved, a linear regression model is used to predict a future trend, a traffic convenience index is adjusted, and optimization of urban traffic layout is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic management, and in particular to a traffic convenience index evaluation method based on big data. Background Art

[0002] The field of traffic management technology involves the use of a variety of tools and strategies to monitor, control, and optimize road traffic. The main purpose of this field is to improve traffic efficiency, reduce congestion, enhance safety, and increase the sustainability of the transportation system. In modern traffic management, technologies such as traffic signal control systems, real-time traffic information systems, automatic license plate recognition technology, and various sensors and monitoring equipment are widely used to assist traffic management centers in collecting traffic flow data, vehicle types, speeds and other information in real time, and then analyze traffic conditions and formulate response measures. Traffic management also includes the planning and management of public transportation systems, such as adjusting bus routes and schedules to adapt to changes in traffic demand.

[0003] Among them, the traffic convenience index evaluation method is a technology that analyzes and evaluates the convenience of the transportation system. By calculating the traffic convenience index, it can provide quantitative data on the efficiency of the transportation system for urban planners, traffic engineers and policy makers. The calculation of the convenience index is based on factors such as traffic flow speed, road congestion, traffic accident frequency, accessibility and reliability of public transportation. Using this evaluation method, related departments can identify bottlenecks and problem areas in the transportation system, and then optimize traffic signal settings, improve road design or enhance public transportation services, improve the operating efficiency of the entire transportation system and the convenience of public travel.

[0004] Although existing technologies widely use traffic signal control systems, real-time traffic information systems, etc., they are insufficient in processing the integration of real-time data and real-time feedback. Especially during peak traffic hours, the existing system is unable to quickly identify and respond to emergencies, resulting in increased congestion. Existing technologies rely on historical trends when analyzing traffic data and lack the ability to quickly adapt to immediate changing factors such as weather or temporary events, which limits the efficiency and accuracy of traffic management. The lack of a mechanism to flexibly adjust the traffic convenience index also often makes it difficult for urban planners to effectively predict and plan for future traffic pressures, which in turn affects the long-term development and optimization of the entire urban transportation system, resulting in limited ability of urban traffic management to respond to emergencies and long-term planning, affecting the overall performance of the transportation system and public travel convenience. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a traffic convenience index evaluation method based on big data.

[0006] In order to achieve the above object, the present invention adopts the following technical solution, a traffic convenience index evaluation method based on big data, comprising the following steps:

[0007] S1: Receive vehicle flow and driving speed data from multiple traffic monitoring nodes, transmit the data to the analysis platform in real time, integrate the vehicle speed and flow of each node, and obtain traffic flow status indicators;

[0008] S2: Analyze the daily and weekly changes of traffic flow according to the traffic flow status indicators, determine the peak and valley periods, identify the time periods with abnormal standard deviation and mean, and obtain traffic fluctuation analysis results;

[0009] S3: using the traffic fluctuation analysis results to compare with the past data of the same period, identifying the change factors that do not conform to the normal pattern, analyzing the statistical impact of the change factors, and evaluating the impact on the traffic flow, and obtaining the evaluation results of traffic abnormality factors;

[0010] S4: According to the evaluation result of the traffic abnormality factor, the threshold of abnormal traffic is adjusted by event type, and the response threshold setting result is obtained by referring to the comparison result of the real-time data and the original data;

[0011] S5: Based on the response threshold setting result, use the linear regression model to predict the traffic trend in the future time period, adjust the traffic convenience index parameters, and obtain the traffic trend prediction and index adjustment results;

[0012] S6: Based on the traffic trend prediction and index adjustment results, the traffic convenience index is updated, the urban traffic layout is optimized and traffic congestion is alleviated, and the traffic guidance and route planning adjustment results are obtained.

[0013] As a further solution of the present invention, the traffic flow state indicators include a mean vehicle speed indicator, a flow accumulation indicator, and an average flow index; the traffic fluctuation analysis results include peak period identification, trough period identification, and abnormal flow period; the traffic abnormality factor assessment results include weather impact assessment, holiday impact assessment, and key event impact assessment; the response threshold setting results include event type threshold and real-time data comparison threshold; the traffic trend prediction and index adjustment results include future traffic trends and an adjusted convenience index; the traffic guidance and route planning adjustment results include an updated traffic convenience index, an optimized traffic layout, and congestion relief measures.

[0014] As a further solution of the present invention, the vehicle flow and driving speed data are received from multiple traffic monitoring nodes, and the data is transmitted to the analysis platform in real time, and the vehicle speed and flow of each node are integrated to obtain the traffic flow status index in the following steps:

[0015] S101: receiving vehicle flow and driving speed data from multiple traffic monitoring nodes, performing format verification on the data, removing data that does not conform to the format and erroneous data, sorting the data according to timestamps, and generating a traffic data sequence;

[0016] S102: Based on the traffic data sequence, the moving average method is used to calculate the average values ​​of vehicle speed and flow of multiple nodes, and each node is analyzed regularly to identify and record the flow and speed fluctuations in multiple time periods to obtain an overview of the node traffic pattern;

[0017] S103: Based on the overview of the node traffic pattern, integrate the node data, calculate the total traffic volume and the average speed of the node, accumulate the node data using a data aggregation method, and generate a traffic flow status indicator.

[0018] As a further solution of the present invention, according to the traffic flow state indicators, the daily and weekly changes of traffic flow are analyzed, the peak and valley periods are determined, and the time periods with abnormal standard deviation and mean are identified. The steps of obtaining the traffic fluctuation analysis results are specifically as follows:

[0019] S201: Based on the traffic flow status indicator, record the traffic flow per hour, calculate the average traffic flow per hour, mark and analyze the data of working days and non-working days respectively, and obtain a daily traffic flow change graph;

[0020] S202: using the data in the daily traffic flow change graph, performing daily traffic flow comparison, calculating the mean and fluctuation of traffic flow on weekdays and weekends, identifying the peak and trough periods of the week, and plotting the periodic changes of traffic flow within a week to obtain a weekly traffic flow trend graph;

[0021] S203: According to the weekly traffic trend chart, calculate the traffic standard deviation and mean of each time period, compare the traffic changes in the same time period, identify and mark the time period of abnormal traffic increase or decrease, and generate traffic fluctuation analysis results.

[0022] As a further solution of the present invention, the traffic fluctuation analysis results are compared with the data of the same period in the past to identify the change factors that do not conform to the normal pattern, analyze the statistical impact of the change factors, and evaluate the impact on traffic flow. The steps of obtaining the evaluation results of traffic abnormality factors are specifically as follows:

[0023] S301: Based on the traffic fluctuation analysis result, current traffic data is compared with original data of the same period, and preliminary mode changes are determined by identifying deviation value marked dates and time periods, and a mode change marking result is generated;

[0024] S302: using the pattern change marking results, capturing weather records, holiday schedules and key event information corresponding to the marked date, associating the information with traffic data using a classification and sorting method, evaluating the influence of multiple factors on the differentiated marked dates, and obtaining the classification factor influence results;

[0025] S303: Based on the impact results of the classification factors, a linear regression algorithm is applied to integrate and calculate the impact of the factors on traffic flow within the time period, and the statistical values ​​of the impact of the factors are analyzed through data association to evaluate the real-time influence of each factor on traffic flow, thereby generating an evaluation result of traffic abnormality factors.

[0026] As a further solution of the present invention, the formula of the linear regression algorithm is as follows:

[0027]

[0028] Calculate the traffic flow impact weight and obtain the evaluation results of traffic anomaly factors, where y represents the traffic flow impact weight, β0 represents the intercept of the model, β1 represents the impact coefficient of weather conditions, β2 represents the impact coefficient of time period, x3 represents the current traffic flow, x4 represents the average value of the original traffic flow in the same period, x1 represents the current weather conditions, x2 represents whether the current time is a peak period, and β3 represents the impact weight.

[0029] As a further solution of the present invention, according to the evaluation result of the traffic abnormality factor, the threshold of abnormal traffic is adjusted by event type, and the step of obtaining the response threshold setting result by referring to the comparison result of the real-time data and the original data is specifically as follows:

[0030] S401: Based on the traffic anomaly factor evaluation result, key event types are classified, including holidays and large-scale events, and original traffic flow data during the event is collected. By comparing the original average flow with the current flow, the threshold of traffic anomaly is adjusted to generate an adjusted event threshold result;

[0031] S402: using the adjusted event threshold result, monitoring real-time traffic data, comparing the traffic flow at each time point with the threshold, identifying the period of abnormal traffic flow, revealing the real-time abnormal state of traffic flow, and obtaining real-time abnormal traffic flow monitoring results;

[0032] S403: According to the real-time abnormal traffic monitoring results, traffic signal adjustments and routes are optimized, corresponding traffic management measures are implemented, traffic changes of differentiated type events are matched, and response threshold setting results are generated.

[0033] As a further solution of the present invention, based on the response threshold setting result, a linear regression model is used to predict the traffic trend in the future time period, and the traffic convenience index parameters are adjusted. The steps of obtaining the traffic trend prediction and index adjustment results are specifically as follows:

[0034] S501: Based on the response threshold setting result, select time series data associated with the prediction period in the future time period, apply the basic linear regression model to predict the flow rate in the key period, and obtain the adjusted flow rate prediction model by calculating the data points in each period;

[0035] S502: using the adjusted traffic prediction model, analyzing the prediction data of key event days and non-event days, optimizing the calculation parameters of the traffic convenience index, refining the settings of the traffic convenience index, and obtaining an optimized traffic convenience index result;

[0036] S503: Based on the optimized traffic convenience index result, a weighted regression algorithm is applied to evaluate the traffic conditions in the future time period, the predicted data is compared with the current real-time data, the index is adjusted, and a traffic trend prediction and index adjustment result are generated.

[0037] As a further solution of the present invention, the formula of the weighted regression algorithm is as follows:

[0038]

[0039] Calculate the traffic convenience index in the future time period and generate the adjusted traffic trend forecast value, where T represents the adjusted traffic convenience index, α represents the weight of the original traffic data, β represents the weight of the real-time traffic data, γ represents the coefficient of change of traffic flow, δ represents the smoothing parameter of the model, ∈ represents the current real-time traffic convenience index, T c Represents the currently calculated traffic convenience index, T h Represents the traffic convenience index of the original traffic data, T r represents the traffic convenience index of real-time traffic data, V r Indicates the currently monitored traffic flow.

[0040] As a further solution of the present invention, according to the traffic trend prediction and index adjustment results, the traffic convenience index is updated, the urban traffic layout is optimized and the traffic congestion is alleviated, and the steps of obtaining the traffic guidance and route planning adjustment results are specifically as follows:

[0041] S601: Based on the traffic trend prediction and index adjustment results, update the traffic convenience index parameters, adjust the threshold and index weight according to the predicted traffic flow peak and trough, including adding the adjustment of the peak period weight and the buffer parameter setting of the trough period, and obtain the updated traffic convenience index;

[0042] S602: Analyze the current traffic flow conditions by using the updated traffic convenience index in combination with real-time traffic data, identify key traffic congestion points and smooth areas, adjust routes and signal light control logic, optimize traffic flow and reduce congestion, including adjusting traffic signal duration and optimizing bus lane configuration, and obtaining urban traffic layout optimization results;

[0043] S603: According to the urban traffic layout optimization result, set up temporary traffic diversion routes and increase traffic monitoring during key periods, evaluate the real-time impact of multiple measures on traffic flow, adjust and refine traffic management in future time periods, and generate traffic guidance and route planning adjustment results.

[0044] Compared with the prior art, the advantages and positive effects of the present invention are:

[0045] In the present invention, by collecting vehicle flow and driving speed data of each monitoring node in real time and performing integrated calculation, the real-time and accuracy of traffic data are significantly improved. The integrated data makes the monitoring of traffic flow status more accurate, allowing urban planners and traffic management departments to quickly respond to changes in traffic conditions. By analyzing the daily and weekly changes in traffic flow, identifying peak and trough periods, and calibrating traffic anomalies based on statistical analysis, traffic peaks can be more effectively predicted and managed, traffic congestion can be reduced, and historical comparative analysis of unconventional traffic flow changes, such as the impact of weather or special events, can be used to further optimize traffic emergency response plans, improve the adaptability and flexibility of urban transportation systems, and predict future traffic trends through linear regression models and adjust the traffic convenience index, which helps to plan and optimize urban transportation layout in advance, thereby reducing traffic pressure in future time periods and improving the efficiency of the overall transportation system. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a schematic diagram of the main steps of the present invention;

[0047] Figure 2 It is a schematic diagram of the refinement of S1 of the present invention;

[0048] Figure 3 It is a schematic diagram of the refinement of S2 of the present invention;

[0049] Figure 4 It is a schematic diagram of the refinement of S3 of the present invention;

[0050] Figure 5 It is a schematic diagram of the refinement of S4 of the present invention;

[0051] Figure 6 It is a detailed schematic diagram of S5 of the present invention;

[0052] Figure 7It is a detailed schematic diagram of S6 of the present invention. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0054] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0055] Embodiment 1

[0056] See also Figure 1 The present invention provides a technical solution, a method for evaluating a traffic convenience index based on big data, comprising the following steps:

[0057] S1: Receive vehicle flow and driving speed data from multiple traffic monitoring nodes, transmit the data to the analysis platform in real time, integrate the vehicle speed and flow of each node, including averaging and accumulation calculations, and obtain traffic flow status indicators;

[0058] S2: Analyze the daily and weekly changes of traffic flow according to traffic flow status indicators, determine the peak and valley periods through statistical analysis, identify the time periods of abnormal increase and decrease of traffic flow according to the standard deviation and mean of traffic flow, and obtain the results of traffic fluctuation analysis;

[0059] S3: Use the traffic fluctuation analysis results to compare with the data of the same period in the past to identify the change factors that do not conform to the normal pattern, including the impact of weather, holidays and key events, analyze the statistical impact of the change factors, and evaluate the impact on traffic flow to obtain the evaluation results of traffic abnormality factors;

[0060] S4: According to the evaluation results of traffic abnormality factors, adjust the threshold of abnormal traffic according to the event type, including holidays and large-scale events, refer to the comparison results of real-time data and original data, set response measures, and obtain the response threshold setting results;

[0061] S5: Based on the response threshold setting result, a linear regression model is used to predict the traffic trend in the future time period, and the traffic convenience index parameters are adjusted through regression analysis to reflect the expected traffic conditions in the future time period, and the traffic trend prediction and index adjustment results are obtained;

[0062] S6: Update the traffic convenience index based on traffic trend forecasts and index adjustment results, and apply current traffic data and convenience index to optimize urban traffic layout and alleviate traffic congestion, and obtain traffic guidance and route planning adjustment results.

[0063] Traffic flow status indicators include mean vehicle speed index, traffic accumulation index, and average traffic index. Traffic fluctuation analysis results include peak period identification, valley period identification, and abnormal traffic period. Traffic abnormality factor assessment results include weather impact assessment, holiday impact assessment, and key event impact assessment. Response threshold setting results include event type threshold and real-time data comparison threshold. Traffic trend prediction and index adjustment results include future traffic trends and adjusted convenience index. Traffic guidance and route planning adjustment results include updated traffic convenience index, optimized traffic layout, and congestion relief measures.

[0064] Specifically, if Figure 2 As shown in the figure, the vehicle flow and driving speed data are received from multiple traffic monitoring nodes, and the data is transmitted to the analysis platform in real time to integrate the vehicle speed and flow of each node. The specific steps to obtain the traffic flow status index are as follows:

[0065] S101: Receive vehicle flow and driving speed data from multiple traffic monitoring nodes, perform format verification on the data, remove data that does not conform to the format and errors, sort the data according to timestamps, and generate a traffic data sequence. The execution process is as follows;

[0066] The vehicle flow and speed data received from multiple traffic monitoring nodes need to pass format verification to ensure the accuracy and validity of the data. During the data reception process, the traffic monitoring nodes automatically collect vehicle flow and speed through their respective sensors. The data is presented in the form of timestamps, vehicle counts and speed readings. In order to ensure the consistency and integrity of the data, a format verification mechanism is introduced. This mechanism uses preset data format requirements, such as the timestamp format is YYYY-MM-DDHH:MM, the values ​​of vehicle flow and speed should be within a reasonable range, such as the speed cannot be negative and the flow count should be a non-negative integer. By comparing the actual format of each data point with the preset format, the data with incorrect format or illogical format is filtered out. After eliminating the data, the remaining valid data is sorted according to the timestamp to form a traffic data sequence arranged in chronological order. This sequence will provide a basis for subsequent traffic flow analysis.

[0067] S102: Based on the traffic data sequence, the moving average method is used to calculate the average values ​​of vehicle speed and flow of multiple nodes, and each node is analyzed regularly to identify and record the flow and speed fluctuations in multiple time periods. The execution process of obtaining the overview of the node traffic mode is as follows;

[0068] Based on the traffic data series, the moving average method is used to calculate the average values ​​of vehicle speed and flow at multiple nodes. The moving average method formula is:

[0069]

[0070] Calculate the average vehicle speed and flow rate of each node in multiple time periods, where MA t represents the moving average of time t, N represents the length of the time window, and X t-i Represents the data point at time ti;

[0071] Set the selected time window length to 5 minutes, with X data points recorded every minute. t , X t-1 , X t-2 , X t-3 , X t-4 The speed records are 20, 22, 19, 21, and 23 km / h respectively. Substituting them into the formula, we get

[0072] This result shows that the average vehicle speed at the node within this time window is 21km / h, which reflects the smoothness of vehicle driving during this time period.

[0073] S103: Based on the overview of the node traffic pattern, the node data is integrated, the total traffic volume and the average speed of the node are calculated, and the node data are accumulated using the data aggregation method to generate the traffic flow state index. The execution process is as follows;

[0074] According to the overview of node traffic patterns, node data is integrated. This process first involves data aggregation from multiple nodes. The traffic flow status data generated by each node needs to be integrated in the central system, including the calculation of the total traffic volume and average speed of each node. The data aggregation method is used in the integration process, which involves merging and recalculating data from multiple data sources. Specific data aggregation can be performed by weighted average or simple accumulation to obtain global traffic status indicators, such as total traffic volume and average speed of each node. The indicators can provide decision support for traffic management departments, optimize traffic flow and improve road use efficiency.

[0075] Specifically, if Figure 3As shown in the figure, according to the traffic flow status indicators, the daily and weekly changes of traffic flow are analyzed, the peak and valley periods are determined, and the time periods with abnormal standard deviation and mean are identified. The specific steps to obtain the traffic fluctuation analysis results are as follows:

[0076] S201: Based on the traffic flow status indicator, record the traffic flow per hour, calculate the average traffic flow per hour, mark and analyze the data of working days and non-working days respectively, and obtain the execution process of the daily traffic flow change diagram as follows;

[0077] Based on the traffic flow status indicators, the hourly vehicle flow is recorded, which involves the automatic collection and recording of data. The traffic monitoring system automatically collects vehicle quantity data from each monitoring point every hour and records the data in the database. The data generated every hour includes specific vehicle flow figures. In order to ensure the accuracy of the data, a data verification process is introduced to verify the timestamp and data integrity. After ensuring that the vehicle flow data recorded every hour is correct, the average vehicle flow per hour is calculated through an algorithm, and the calculated average is stored in the database for later use. In order to analyze the difference in traffic flow between weekdays and non-working days, the system will automatically mark working days and non-working days according to the date, and analyze these two types of data separately to generate a daily traffic change graph, which shows the fluctuations in vehicle flow on weekdays and non-working days, providing a basis for traffic planning and management.

[0078] S202: Using the data in the daily traffic flow change graph, a daily traffic flow comparison is performed. By calculating the mean and fluctuation of the traffic flow on weekdays and weekends, the peak and trough periods of the week are identified, and the periodic changes of the traffic flow within a week are plotted to obtain the weekly traffic flow trend graph. The execution process is as follows;

[0079] Use the data in the daily flow change chart to compare daily flow according to the formula:

[0080]

[0081] and

[0082]

[0083] Calculate the mean and fluctuation of traffic flow on weekdays and weekends, where x j represents the traffic flow on the jth day, μ represents the average traffic flow, σ 2 represents the variance of traffic volume, n represents the number of days;

[0084] For example, if the traffic volume data on weekdays is x j =[2000, 2100, 2200, 2300, 2400], substitute into the formula to calculate:

[0085] The variance is calculated as: This result shows that the average traffic flow on weekdays is 2200 and the variance is 20000, which reflects the stability and volatility of traffic flow on weekdays.

[0086] S203: According to the weekly traffic trend chart, the standard deviation and mean of the traffic in each time period are calculated, the traffic changes in the same time period are compared, and the time period of abnormal traffic increase and decrease is identified and marked. The execution process of generating the traffic fluctuation analysis result is as follows;

[0087] According to the weekly traffic trend chart, we first need to calculate the standard deviation and mean of the traffic in each time period, including statistical analysis of the traffic data recorded in each time period. The statistical analysis includes finding the total traffic and its average in each time period, and then using the statistical data, the standard deviation formula is used to calculate the traffic fluctuation in each time period. The standard deviation calculation involves the degree of deviation of the traffic at each time point from the average traffic. The larger the standard deviation, the greater the traffic fluctuation. The calculation process plays a key role in identifying traffic anomalies. By comparing the traffic standard deviations of differentiated time periods, abnormal time periods with sudden increases or decreases in traffic can be identified. After being marked as abnormal, they will be used to adjust traffic management measures or conduct further traffic analysis. The generated traffic fluctuation analysis results will provide a scientific basis for urban traffic planning.

[0088] Specifically, if Figure 4 As shown in the figure, the traffic fluctuation analysis results are compared with the data of the same period in the past to identify the change factors that do not conform to the normal pattern, analyze the statistical impact of the change factors, and evaluate the impact on traffic flow. The specific steps for obtaining the evaluation results of traffic abnormality factors are as follows:

[0089] S301: Based on the traffic fluctuation analysis results, the current traffic data is compared with the original data of the same period, and the initial mode change is determined by identifying the deviation value marking date and time period, and the execution process of generating the mode change marking result is as follows;

[0090] Based on the results of traffic fluctuation analysis, the current traffic data is compared with the original data of the same period. The comparison involves extracting historical traffic data of the same period from the historical database and comparing it with the current data. The numerical deviation analysis method is used in the comparison process to quantify the difference in traffic volume in each time period, and automatically calculate the difference in traffic volume between the current data and the historical data to determine whether there is a significant change in traffic patterns. If the current traffic volume is significantly higher or lower than the historical data of the same period, the date and time period will be marked as abnormal. The deviation value will also be recorded during the marking process for further analysis of the cause. The calculation of this deviation value not only assists in identifying preliminary pattern changes, but also helps in subsequent in-depth analysis and decision-making. The generated pattern change marking results will be used for further traffic management and planning optimization.

[0091] S302: Using the pattern change marking results, capturing weather records, holiday schedules and key event information corresponding to the marked date, using a classification and sorting method to associate the information with traffic data, evaluating the influence of multiple factors on the differentiated marked dates, and obtaining the classification factor influence results. The execution process is as follows;

[0092] Using the pattern change marking results, weather records, holiday schedules and key event information corresponding to the marked dates are captured, and weather conditions, holiday arrangements and records of special events that occur on dates corresponding to traffic data, such as large-scale events or public works, are automatically extracted from multiple external data sources. The information is systematically associated with the traffic data of each marked date through data mining and classification and sorting methods. Through this association, analysts can evaluate the specific impact of classification factors such as weather, holidays or special events on traffic flow on differentiated dates. The analysis not only reveals how each factor affects daily traffic patterns, but also provides a basis for real-time adjustment of traffic flow management, ensuring the efficient operation of the transportation system.

[0093] S303: Based on the impact results of the classified factors, a linear regression algorithm is applied to integrate and calculate the impact of the factors on the traffic flow within the time period, and the statistical values ​​of the impact of the factors are analyzed through data association to evaluate the real-time influence of each factor on the traffic flow pattern. The execution process of generating the evaluation results of traffic abnormality factors is as follows;

[0094] The formula for the linear regression algorithm is as follows:

[0095]

[0096] Calculate the traffic flow impact weight and obtain the traffic anomaly factor evaluation result, where y represents the traffic flow impact weight, β0 represents the intercept of the model, β1 represents the impact coefficient of weather conditions, β2 represents the impact coefficient of time period, x3 represents the current traffic flow, x4 represents the average value of the original traffic flow in the same period, x1 represents the current weather conditions, x2 represents whether the current time is a peak period, and β3 represents the impact weight;

[0097] Detailed explanation of the formula and the process of formula calculation and derivation:

[0098] This linear regression model is used to evaluate the impact of weather conditions, time period, and current traffic volume on baseline traffic volume, with the following parameters:

[0099] β0 (intercept) represents the baseline traffic flow without any influence, which is obtained from the report released by the city traffic management department. Historical data shows that when there is no external influence, the average baseline traffic flow is 500 vehicles / hour;

[0100] β1 (the influence coefficient of weather conditions) is obtained by analyzing the correlation between historical weather data and traffic flow data. The data shows that on rainy days, traffic flow decreases by an average of 15%, so β1 is set to -0.15;

[0101] β2 (the influence coefficient of time period) is obtained by analyzing the traffic flow statistics of differentiated time periods. The traffic flow during peak hours (such as morning and evening peaks) increases by 30%, so β2 = 0.30 is set;

[0102] x1 represents the current weather conditions, which are obtained through the real-time weather monitoring system, where sunny days are 1 and rainy days are 0;

[0103] x2 represents whether the current time is a peak period, which is monitored in real time by the traffic flow monitoring system. It is 1 during peak period and 0 during non-peak period;

[0104] x3 represents the current traffic flow, which is calculated in real time by road cameras and traffic monitoring equipment. Assume that the measured traffic flow is 800 vehicles per hour in a certain period of time;

[0105] x4 represents the average value of the raw traffic volume during the same period, calculated based on the data of the same period in the past three years, assumed to be 650 vehicles / hour;

[0106] Substitute actual values ​​for calculation:

[0107]

[0108] y=500+0+0.30×1+(-0.20)×1.23

[0109] y=500+0.30-0.246

[0110] y=500.054

[0111] The results show that after referring to the ratio of peak hours to current traffic flow and historical average, the predicted traffic flow is 500.054 vehicles / hour. This shows that during peak hours, even if the current traffic flow is slightly higher than the historical average, the overall traffic flow is still close to the baseline, indicating that the model is very effective in evaluating the real-time impact of traffic flow. This model can assist traffic management departments to more accurately predict and manage urban traffic flow patterns, thereby adjusting the traffic light system or issuing traffic control measures in advance to reduce traffic congestion.

[0112] Specifically, if Figure 5 As shown, according to the evaluation results of traffic abnormality factors, the threshold of abnormal traffic is adjusted by event type, and the steps of obtaining the response threshold setting result are as follows:

[0113] S401: Based on the evaluation results of traffic anomaly factors, key event types are classified, including holidays and large-scale events, and the original traffic flow data during the event is collected. By comparing the original average flow with the current flow, the threshold of traffic anomaly is adjusted, and the execution process of generating the adjusted event threshold result is as follows;

[0114] Based on the results of the traffic anomaly factor assessment, key event types are classified. This classification process includes identifying which event types—such as holidays or large-scale events—have a significant impact on traffic flow. The system identifies events that have a greater impact on traffic flow by analyzing historical data and classifies them as key event types. Next, the original traffic flow data during the event is collected. During the collection process, the data acquisition system obtains traffic data from multiple monitoring points to ensure that the data obtained can represent the traffic conditions of the entire city or a specific area. By comparing the original average flow with the current flow, statistical analysis methods such as z-scores or standard deviation analysis are used to adjust the threshold of traffic anomalies. This adjustment is based on historical data and currently observed data changes to generate adjusted event threshold results, which are used for further monitoring and management to ensure that the traffic system can respond quickly to predicted and actual events.

[0115] S402: Using the adjusted event threshold results, monitor real-time traffic data, compare the traffic flow at each time point with the threshold, identify the period of abnormal traffic flow, reveal the real-time abnormal state of traffic flow, and obtain the execution process of real-time abnormal traffic flow monitoring results as follows;

[0116] Using the adjusted event threshold results, monitor real-time traffic data according to the formula:

[0117] T=q+k×u

[0118] Conduct real-time monitoring of flow anomalies, where T represents the threshold, q represents the historical average flow, u represents the standard deviation of the historical flow, and k represents the coefficient for adjusting the sensitivity;

[0119] Assume that historical data shows that the average flow rate during holidays is q = 1000 vehicles / hour, and the standard deviation is u = 100 vehicles / hour. To increase the sensitivity of monitoring, k is set to 2, and the threshold is calculated as T = 1000 + 2 × 100 = 1200 vehicles / hour. This threshold is used to identify abnormal periods when the actual flow rate exceeds 1200 vehicles / hour. The result shows that any flow rate exceeding 1200 vehicles / hour will be marked as abnormal, providing a basis for real-time monitoring and immediate response.

[0120] S403: According to the real-time abnormal traffic monitoring results, traffic signal adjustment and route optimization are carried out, corresponding traffic management measures are implemented, traffic changes of differentiated type events are matched, and the execution process of generating response threshold setting results is as follows;

[0121] Traffic signal adjustments and route optimization are performed based on the results of real-time abnormal traffic flow monitoring, which involves analyzing real-time data and making corresponding traffic signal and route adjustments based on real-time changes in traffic flow. Optimization measures include changing the timing of traffic lights, increasing or decreasing the number of lanes on certain routes, or implementing traffic control measures. Adjustments are based on real-time abnormal traffic flow monitoring results and are automatically executed through advanced traffic management systems to ensure smooth traffic flow, reduce congestion and improve safety. They also include traffic adjustments based on differentiated types of events - such as holidays or special events - to ensure that each situation is effectively managed. The generated response threshold setting results will be used in future traffic forecasting and management to improve the overall efficiency and responsiveness of the urban transportation system.

[0122] Specifically, if Figure 6 As shown in the figure, based on the response threshold setting result, the linear regression model is used to predict the traffic trend in the future time period, and the traffic convenience index parameters are adjusted. The specific steps for obtaining the traffic trend prediction and index adjustment results are as follows:

[0123] S501: Based on the response threshold setting result, select the time series data associated with the prediction period in the future time period, apply the basic linear regression model to predict the flow rate in the key period, and correct the model by calculating the data points in each period to obtain the adjusted flow rate prediction model. The execution process is as follows;

[0124] Based on the response threshold setting results, time series data associated with the forecast period in the future time period are selected, which involves data selection and processing to ensure that the data used is closely related to the forecast period in the future time period. The traffic prediction in the key time period is carried out using a basic linear regression model. The model learns and predicts future traffic based on historical data. It is necessary to extract the time series corresponding to the forecast period from batch historical traffic data. The data is used to train the model in order to accurately predict the traffic in the key time period. By calculating the data points in each time period, the model is corrected to adapt to new trends or patterns. Adjusting the traffic prediction model is a dynamic process, which requires cyclic optimization based on real-time data to improve the accuracy of the prediction, and finally obtains the adjusted traffic prediction model. This model will be applied to traffic management and planning in the future time period to provide scientific basis and decision support.

[0125] S502: using the adjusted traffic forecast model, analyzing the forecast data of key event days and non-event days, optimizing the calculation parameters of the traffic convenience index, refining the settings of the traffic convenience index, and obtaining the optimized traffic convenience index results. The execution process is as follows;

[0126] The adjusted traffic forecast model is used to analyze the forecast data of key event days and non-event days. During the analysis, the model will process the differentiated types of days separately according to whether they are event days to ensure the accuracy and adaptability of the forecast. The steps to optimize the calculation parameters of the traffic convenience index include analyzing the deviation between the model prediction and the actual data, adjusting the model parameters according to the results of the deviation, and optimizing the calculation of the traffic convenience index. This involves a complex mathematical process, including statistical analysis and pattern recognition. The setting of the traffic convenience index is refined through quantitative analysis of various influencing factors, such as traffic density, driving speed and traffic accidents, to ensure that the index more accurately reflects the actual traffic conditions. The optimized traffic convenience index results will more effectively assist urban planners and traffic managers in evaluating and improving urban traffic conditions.

[0127] S503: Based on the optimized traffic convenience index result, a weighted regression algorithm is applied to evaluate the traffic conditions in the future time period, the predicted data is compared with the current real-time data, the index is adjusted, and the execution process of generating the traffic trend prediction and index adjustment result is as follows;

[0128] The formula for the weighted regression algorithm is as follows:

[0129]

[0130] Calculate the traffic convenience index in the future time period and generate the adjusted traffic trend forecast value, where T represents the adjusted traffic convenience index, α represents the weight of the original traffic data, β represents the weight of the real-time traffic data, γ represents the coefficient of change of traffic flow, δ represents the smoothing parameter of the model, ∈ represents the current real-time traffic convenience index, T c Represents the currently calculated traffic convenience index, T h Represents the traffic convenience index of the original traffic data, T r represents the traffic convenience index of real-time traffic data, V r Indicates the currently monitored traffic flow;

[0131] The specific meaning and calculation process of each parameter in the formula are as follows:

[0132] α represents the weight of the original traffic data, which is obtained by analyzing the average traffic index in the same time period of the past week. The weight of historical traffic data is calculated as the average of the traffic convenience index at the same time every day. The historical data is set to the traffic index of the following seven days: 80, 75, 82, 78, 84, 77, 79. Then the average value of α is divided by 100 for normalization, and the calculation is

[0133] β represents the weight of real-time traffic data, which is adjusted in real time by analyzing the traffic convenience index of the same time period on the same day. The weight of real-time traffic data depends on the difference between the real-time traffic index and the historical traffic index. If the real-time traffic convenience index is set to 85, the value of β is

[0134] γ represents the coefficient of change of traffic flow, reflecting the sensitivity of traffic flow change to traffic condition prediction. This parameter is calculated from the traffic flow data obtained by traffic cameras and sensors. The average traffic flow during the monitoring period is set to 3200 vehicles / hour and the reference flow is 3000 vehicles / hour. Then γ is

[0135] δ is the smoothing parameter of the model to reduce the impact of data outliers. It is set to 1 to ensure that the denominator is not 0 and to ensure calculation stability;

[0136] ∈ represents the current real-time traffic convenience index, which is obtained through real-time data and is currently set to 85;

[0137] T c Represents the currently calculated traffic convenience index, which is calculated using the aforementioned formula and is a comprehensive evaluation result of the aforementioned weights and traffic data;

[0138] T h: Traffic convenience index of historical traffic data, which is the average value calculated from the traffic data of the same time period in the past period (such as the past week or month) to reflect the historical traffic conditions. The current value is 80;

[0139] T r : Traffic convenience index of real-time traffic data, which is the traffic convenience index of the current or most recent time point obtained during the calculation process, reflecting the immediate traffic conditions, and is currently set to 85;

[0140] V r : The current monitored traffic flow, data obtained by real-time monitoring of traffic cameras and sensors, indicating the number of vehicles passing through a certain road section within a specific time, set to 10;

[0141] Substitute all parameter values ​​into the formula for calculation:

[0142]

[0143] The result shows that under the given parameters and current traffic conditions, the adjusted traffic convenience index is 2.01, which is a low value, indicating that under the current conditions, the traffic situation is relatively congested or there are some inconveniences. Through this result, further traffic management and adjustment measures can be taken to improve the actual traffic conditions and make them smoother.

[0144] Specifically, if Figure 7 As shown in the figure, according to the traffic trend prediction and index adjustment results, the traffic convenience index is updated, the urban traffic layout is optimized and traffic congestion is alleviated, and the steps for obtaining the traffic guidance and route planning adjustment results are as follows:

[0145] S601: Based on the traffic trend prediction and index adjustment results, the traffic convenience index parameters are updated. The threshold and index weight are adjusted according to the predicted traffic flow peaks and valleys, including adding the adjustment of the peak period weight and the buffer parameter setting of the valley period. The execution process of obtaining the updated traffic convenience index is as follows;

[0146] Based on the traffic trend forecast and index adjustment results, the traffic convenience index parameters are updated, which involves data analysis and new parameter setting, including consideration of traffic flow peak and trough periods, adjustment of thresholds and index weights, including emphasis on the weight of peak periods and setting buffer parameters for trough periods. The adjustment needs to be accurately completed based on historical traffic flow data and model predictions. The specific value of the weight adjustment is determined by analyzing the traffic flow changes in differentiated time periods. For example, the weight of peak periods is increased to reflect their actual impact on the pressure on the traffic system, while the weight of trough periods is reduced accordingly to adapt to the actual changes in traffic flow, resulting in an updated traffic convenience index. This index more accurately reflects the actual traffic conditions and provides a more scientific basis for urban traffic management.

[0147] S602: Utilize the updated traffic convenience index and real-time traffic data to analyze the current traffic flow conditions, identify key traffic congestion points and smooth areas, adjust routes and signal light control logic, optimize traffic flow and reduce congestion, including adjusting traffic signal duration and optimizing bus lane configuration. The execution process of obtaining the optimization results of urban traffic layout is as follows;

[0148] Utilize the updated traffic convenience index and combine it with real-time traffic data to analyze the current traffic flow conditions, including identifying key congestion points and smooth areas in the traffic system, adjusting routes and signal light control logic to optimize traffic flow and reduce congestion. Adjustment of traffic signal duration and optimization of bus lane configuration are key measures. Through data analysis, determine which sections need to increase signal duration, which sections can be reduced, and where to add or optimize bus lanes. Adjustments are based on the results of real-time and historical data analysis to ensure that current and predicted traffic conditions can be effectively responded to and the optimization results of urban traffic layout can be obtained, which will directly affect the daily travel of urban residents and the sustainable development of urban transportation.

[0149] S603: According to the optimization results of urban traffic layout, temporary traffic diversion routes are set and traffic monitoring during key periods is increased, the real-time impact of multiple measures on traffic flow is evaluated, traffic management in future time periods is adjusted and refined, and the execution process of generating traffic guidance and route planning adjustment results is as follows;

[0150] Evaluate the traffic conditions in the future time period according to the formula:

[0151]

[0152] Compare the predicted data with the current real-time data and adjust the index, where Y represents the predicted traffic condition index and w z represents the weight of the zth factor, x z represents the value of the zth factor, and m represents the total number of factors;

[0153] There are three factors: traffic volume x1 = 300 vehicles / hour, driving speed x2 = 50km / h, number of accidents x3 = 2, weights are set to w1 = 0.5, w2 = 0.3, w3 = 0.2, then calculate: Y = 0.5 × 300 + 0.3 × 50 + 0.2 × 2 = 160;

[0154] This result shows that under the given weights and factor values, the predicted traffic condition index is 160, which is helpful for real-time monitoring and evaluation of traffic conditions and providing decision support for traffic management.

[0155] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A traffic convenience index evaluation method based on big data, characterized in that: The following steps are involved: Receive vehicle flow and driving speed data from multiple traffic monitoring nodes, and transmit the data to the analysis platform in real time to integrate the vehicle speed and flow of each node to obtain traffic flow status indicators; According to the traffic flow status indicators, analyze the daily and weekly changes of traffic flow, determine the peak and valley periods, identify the time periods with abnormal standard deviation and mean, and obtain traffic fluctuation analysis results; By comparing the traffic fluctuation analysis results with the data of the same period in the past, the changing factors that do not conform to the normal pattern are identified, the statistical impact of the changing factors is analyzed, and the impact on the traffic flow is evaluated to obtain the evaluation results of traffic abnormality factors; According to the evaluation results of the traffic abnormality factors, the threshold of abnormal traffic is adjusted by event type, and the response threshold setting result is obtained by referring to the comparison results of real-time data and original data; Based on the response threshold setting result, a linear regression model is used to predict the traffic trend in the future time period, and the traffic convenience index parameters are adjusted to obtain the traffic trend prediction and index adjustment results; According to the traffic trend prediction and index adjustment results, the traffic convenience index is updated, the urban traffic layout is optimized and traffic congestion is alleviated, and the traffic guidance and route planning adjustment results are obtained.

2. The traffic convenience index evaluation method based on big data according to claim 1 is characterized in that: The traffic flow status indicators include the mean vehicle speed indicator, the traffic accumulation indicator, and the average traffic index. The traffic fluctuation analysis results include peak period identification, valley period identification, and abnormal traffic period. The traffic abnormality factor assessment results include weather impact assessment, holiday impact assessment, and key event impact assessment. The response threshold setting results include event type threshold and real-time data comparison threshold. The traffic trend prediction and index adjustment results include future traffic trends and adjusted convenience index. The traffic guidance and route planning adjustment results include updated traffic convenience index, optimized traffic layout, and congestion relief measures.

3. The traffic convenience index evaluation method based on big data according to claim 1 is characterized in that: The vehicle flow and driving speed data are received from multiple traffic monitoring nodes. The data is transmitted to the analysis platform in real time to integrate the vehicle speed and flow of each node. The specific steps to obtain traffic flow status indicators are as follows: Receive vehicle flow and driving speed data from multiple traffic monitoring nodes, perform format verification on the data, remove data that does not conform to the format and errors, sort the data by timestamp, and generate a traffic data sequence; Based on the traffic data sequence, the moving average method is used to calculate the average values ​​of vehicle speed and flow at multiple nodes, and each node is analyzed regularly to identify and record the flow and speed fluctuations in multiple time periods to obtain an overview of the node traffic pattern; Based on the overview of the node traffic pattern, the node data is integrated, the total traffic volume and the average speed of the node are calculated, and the node data are accumulated using the data aggregation method to generate the traffic flow status index.

4. The traffic convenience index evaluation method based on big data according to claim 1 is characterized in that: According to the traffic flow status indicators, the daily and weekly changes of traffic flow are analyzed, the peak and valley periods are determined, and the time periods with abnormal standard deviation and mean are identified. The specific steps for obtaining the traffic fluctuation analysis results are as follows: Based on the traffic flow status indicators, record the traffic volume per hour, calculate the average traffic volume per hour, mark and analyze the data of working days and non-working days respectively, and obtain a daily traffic volume change graph; Using the data in the daily traffic change graph, daily traffic comparison is performed, by calculating the mean and fluctuation of traffic flow on weekdays and weekends, identifying the peak and trough periods of the week, and plotting the periodic changes of traffic flow within a week to obtain a weekly traffic trend graph; According to the weekly traffic trend chart, the standard deviation and mean of the traffic in each time period are calculated, the traffic changes in the same time period are compared, the time periods of abnormal traffic increase and decrease are identified and marked, and the traffic fluctuation analysis results are generated.

5. The traffic convenience index evaluation method based on big data according to claim 1 is characterized in that: By comparing the traffic fluctuation analysis results with the data of the same period in the past, the changing factors that do not conform to the normal pattern are identified, the statistical impact of the changing factors is analyzed, and the impact on traffic flow is evaluated. The specific steps for obtaining the evaluation results of traffic abnormality factors are as follows: Based on the traffic fluctuation analysis results, current traffic data is compared with original data of the same period, and preliminary mode changes are determined by identifying deviation value marked dates and time periods, and a mode change marking result is generated; Using the pattern change marking results, capturing weather records, holiday schedules and key event information corresponding to the marked date, using a classification and sorting method to associate the information with traffic data, evaluating the influence of multiple factors on the differentiated marked dates, and obtaining the classification factor influence results; According to the impact results of the classification factors, a linear regression algorithm is applied to integrate and calculate the impact of the factors on traffic flow within the time period. The statistical values ​​of the impact of the factors are analyzed through data association, the real-time influence of each factor on traffic flow is evaluated, and the evaluation results of traffic abnormality factors are generated.

6. The method for evaluating the traffic convenience index based on big data according to claim 5 is characterized in that: The formula for the linear regression algorithm is as follows: Calculate the traffic flow impact weight and obtain the evaluation results of traffic anomaly factors, where y represents the traffic flow impact weight, β0 represents the intercept of the model, β1 represents the impact coefficient of weather conditions, β2 represents the impact coefficient of time period, x3 represents the current traffic flow, x4 represents the average value of the original traffic flow in the same period, x1 represents the current weather conditions, x2 represents whether the current time is a peak period, and β3 represents the impact weight.

7. The method for evaluating traffic convenience index based on big data according to claim 1 is characterized in that: According to the evaluation results of the traffic abnormality factors, the threshold of abnormal traffic is adjusted by event type, and the steps of obtaining the response threshold setting result by referring to the comparison results of real-time data and original data are specifically as follows: Based on the traffic anomaly factor evaluation results, key event types are classified, including holidays and large-scale events, and original traffic flow data during the event is collected. By comparing the original average flow with the current flow, the threshold of traffic anomaly is adjusted to generate an adjusted event threshold result; Using the adjusted event threshold results, real-time traffic data is monitored, traffic flow at each time point is compared with the threshold, the period of abnormal traffic flow is identified, the real-time abnormal state of traffic flow is revealed, and real-time abnormal traffic flow monitoring results are obtained; According to the real-time abnormal traffic monitoring results, traffic signal adjustments and routes are optimized, corresponding traffic management measures are implemented, traffic changes of differentiated type events are matched, and response threshold setting results are generated.

8. The traffic convenience index evaluation method based on big data according to claim 1 is characterized in that: Based on the response threshold setting result, the linear regression model is used to predict the traffic trend in the future time period, and the traffic convenience index parameters are adjusted. The steps of obtaining the traffic trend prediction and index adjustment results are as follows: Based on the response threshold setting result, time series data associated with the prediction period in the future time period are selected, and the basic linear regression model is applied to predict the flow rate in the key time period, and the model is corrected by calculating the data points in each time period to obtain an adjusted flow rate prediction model; Using the adjusted traffic forecast model, analyzing forecast data for key event days and non-event days, optimizing calculation parameters of the traffic convenience index, refining the settings of the traffic convenience index, and obtaining optimized traffic convenience index results; According to the optimized traffic convenience index results, a weighted regression algorithm is applied to evaluate the traffic conditions in the future time period, the predicted data is compared with the current real-time data, the index is adjusted, and the traffic trend prediction and index adjustment results are generated.

9. The method for evaluating traffic convenience index based on big data according to claim 8, characterized in that: The formula of the weighted regression algorithm is as follows: Calculate the traffic convenience index in the future time period and generate the adjusted traffic trend forecast value, where T represents the adjusted traffic convenience index, α represents the weight of the original traffic data, β represents the weight of the real-time traffic data, γ represents the coefficient of change of traffic flow, δ represents the smoothing parameter of the model, ∈ represents the current real-time traffic convenience index, T c Represents the currently calculated traffic convenience index, T h Represents the traffic convenience index of the original traffic data, T r represents the traffic convenience index of real-time traffic data, V r Indicates the currently monitored traffic flow.

10. The method for evaluating traffic convenience index based on big data according to claim 1, characterized in that: According to the traffic trend prediction and index adjustment results, the traffic convenience index is updated, the urban traffic layout is optimized and traffic congestion is alleviated, and the steps of obtaining traffic guidance and route planning adjustment results are specifically as follows: Based on the traffic trend prediction and index adjustment results, update the traffic convenience index parameters, adjust the threshold and index weight through the predicted traffic flow peaks and troughs, including adding adjustments to the peak period weights and setting buffer parameters for the trough period, and obtain an updated traffic convenience index; Utilize the updated traffic convenience index and combine it with real-time traffic data to analyze the current traffic flow conditions, identify key traffic congestion points and smooth areas, adjust routes and signal light control logic, optimize traffic flow and reduce congestion, including adjusting traffic signal duration and optimizing bus lane configuration, and obtain urban traffic layout optimization results; According to the urban traffic layout optimization results, temporary traffic diversion routes are set up and traffic monitoring is increased during key periods, the real-time impact of multiple measures on traffic flow is evaluated, traffic management in future time periods is adjusted and refined, and traffic guidance and route planning adjustment results are generated.

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