A data management-based method for evaluating highway information

Abstract

This invention discloses a data-management-based highway information evaluation method, belonging to the field of highway evaluation technology. The evaluation steps are as follows: S1, collecting highway traffic flow data, road surface condition data, and local meteorological data, and preprocessing the collected data; S2, extracting relevant calculated feature values ​​from the preprocessed data, synchronizing the processed data according to the time dimension, and correlating the collected data; S3, calculating the road surface damage rate, traffic congestion rate, and meteorological impact rate based on the data. This invention comprehensively considers various factors to derive an evaluation score, providing a scientific basis for management decisions. Classifying and maintaining the highway by level clarifies the condition level, allowing for the development of maintenance strategies based on the level, achieving rational resource allocation, improving the overall operation and maintenance management level of the highway, avoiding the problem of relying on single data for evaluation, effectively improving the processing flow, and enabling precise evaluation and analysis.

Description

Technical Field

[0001] This invention relates to the field of highway evaluation technology, specifically to a method for evaluating highway information based on data management. Background Technology

[0002] In modern transportation systems, highways play a crucial role in transportation, and their operational status directly impacts transportation efficiency, traffic safety, and socio-economic development. However, traditional highway condition assessment methods have many limitations. Past evaluations often rely on single or limited types of data, such as focusing solely on traffic flow data to determine congestion, neglecting the combined effects of road surface condition and weather factors. This one-sided assessment approach fails to comprehensively and accurately reflect the true condition of highways. When judging road conditions based solely on traffic flow, potential road surface damage from long-term use may not be detected in time. Such damage, under adverse weather conditions, can easily lead to traffic accidents and disrupt normal traffic flow. Furthermore, the limited data collected lacks a systematic and effective preprocessing process, resulting in inconsistent data quality and hindering precise analysis. The lack of comprehensive and accurate data support significantly reduces the relevance and effectiveness of decision-making. Therefore, we propose a data management-based highway information evaluation method. Summary of the Invention

[0003] To address the aforementioned technical problems, this paper provides a data management-based highway information evaluation method. This technical solution solves the problems of relying solely on single data for evaluation, lacking a systematic and effective processing flow, and making accurate evaluation and analysis difficult.

[0004] To achieve the above objectives, the technical solution adopted by this invention is: a highway information evaluation method based on data management, the evaluation steps of which are as follows:

[0005] S1. Collect highway traffic flow data, road surface condition data, and local weather data, and preprocess the collected data.

[0006] S2. Extract relevant feature values ​​from the preprocessed data, synchronize the processed data according to the time dimension, and match the collected data with each other;

[0007] S3. Based on the data, calculate the road surface damage rate, traffic congestion rate and weather impact rate, calculate the weight vector of each indicator, and calculate the current highway evaluation score based on the weighted average method.

[0008] S4. Based on big data, different levels of evaluation scores are divided. The calculated evaluation scores are then substituted into different levels to assess the current condition of the highway and make corresponding maintenance decisions for each level.

[0009] Preferably, traffic flow data is collected based on cameras, and the traffic flow data within a unit time period is determined based on image analysis. Road surface condition data is collected in real time by a laser detection vehicle equipped with a laser sensor and navigation system. The laser sensor emits a laser beam, and the three-dimensional shape data of the road surface is accurately obtained by measuring the laser reflection time, and the road surface smoothness index is calculated. Local meteorological data is obtained based on meteorological stations.

[0010] Preferably, the laser sensor emits a laser beam and measures the reflection time during vehicle movement, using the formula:

[0011] d = c × t / 2

[0012] Where d is the distance value, c is the speed of light, and t is the reflection time. The reflection time is converted into the distance value between the road surface and the sensor.

[0013] Based on the installation height of the laser sensor and the measured distance, the elevation values ​​of each measuring point on the road surface are calculated, resulting in the road surface longitudinal profile elevation data sequence z. i , i = 1, 2, ..., n, where n is the number of measurement points;

[0014] The elevation difference between adjacent measuring points is calculated using the formula: Δz i =z i+1 -z i ;

[0015] Based on the transfer function H(k) of the quarter-car model, the elevation difference sequence is filtered to obtain the vertical displacement difference sequence Δy between the rear axle and the front axle during simulated vehicle movement. i Where k is the spatial frequency;

[0016] The approximate value of IRI is calculated using the following formula:

[0017]

[0018] Where L is the length of the measured road segment, and IRI is the International Roughness Index.

[0019] Preferably, the relevant calculated feature values ​​in step S2 include: traffic flow data during congested periods on the highway within a unit time period, highway capacity data during that time period, total area of ​​road surface damage, total road surface area, and the number of meteorological factors. The time-dimension synchronization steps are as follows: during each data collection process, a timestamp is attached to the collected data. For traffic flow data, which changes frequently, the timestamp accuracy is accurate to the second and millisecond; for road surface condition data, which changes relatively slowly, the timestamp accuracy is accurate to the minute; for meteorological data, based on the monitoring frequency, the accuracy is accurate to the second and minute, i.e., traffic flow data is recorded as [timestamp 1, traffic flow value 1], road surface smoothness data is recorded as [timestamp 2, IRI value 2], and meteorological data is recorded as [timestamp 3, temperature value 3]; the collected data are then processed accordingly.

[0020] Preferably, in step S3, the pavement damage rate is used to reflect the proportion of damaged areas to the total pavement area on the highway, and the calculation formula is as follows:

[0021] A = (b / e) * 100%

[0022] Where A is the calculated road surface damage rate, b is the total damaged area of ​​the road surface, and e is the total area of ​​the road surface;

[0023] Traffic congestion rate is used to reflect the congestion situation on highways, and the calculation formula is as follows:

[0024] F' = (G / H) * 100%

[0025] Where F' is the calculated traffic congestion rate, G is the current traffic flow on the highway at the time of data collection, and H is the designed capacity of the highway.

[0026] Preferably, the formula for calculating the meteorological impact rate in step S3 is:

[0027]

[0028] Where J is the calculated meteorological impact rate, and n' represents the number of meteorological factors considered, including temperature, precipitation, wind speed, and visibility; w i This refers to the weight of the i-th meteorological factor, which is determined through an expert scoring method, f. i (x i ) is the influence function of the i-th meteorological factor, which represents the actual value x of the meteorological factor. i Converting this to the degree of impact on traffic, for visibility factors, the impact function is a piecewise function, expressed as:

[0029]

[0030] V1 and V2 are pre-set visibility thresholds. When the visibility is greater than or equal to V1, it is determined that there is no impact on traffic; when the visibility is less than or equal to V2, it is considered that the impact on traffic has reached its maximum.

[0031] Preferably, the steps for calculating the weight vectors of each indicator are as follows: Standardize the calculated values; construct a judgment matrix using the analytic hierarchy process (AHP); compare the relative importance of the two indicators; construct a 3×3 judgment matrix M; determine that the road surface damage rate is slightly more important than the traffic congestion rate; and assign the corresponding element M to the judgment matrix. 12 The value is 3, M 21 Then it is Determine the main diagonal element M of the matrix ii =1. After constructing the judgment matrix, a consistency check is performed, calculated using the consistency index CI:

[0032]

[0033] Where λ max This involves determining the largest eigenvalue of the matrix, where na is the matrix order. If na = 3, the random consistency index RI is found. For na = 3, there is a corresponding standard value. The consistency ratio CR is then calculated using the following formula:

[0034]

[0035] When CR < 0.1, the judgment matrix is ​​considered to have acceptable consistency; otherwise, the judgment matrix is ​​readjusted. After passing the consistency test, the weight vector is calculated using the eigenvector method. The eigenvector corresponding to the largest eigenvalue of the judgment matrix M is calculated, and the eigenvector is normalized. The normalized eigenvector is the weight vector W of each index.

[0036] Preferably, the evaluation score of the current expressway is calculated based on a weighted average method. The formula for calculating the evaluation score is as follows:

[0037] P = W1*A + W2*F' + W3*J

[0038] Where P is the current evaluation score of the expressway obtained by weighted comprehensive calculation, and W1, W2 and W3 are the weight vector values ​​of the corresponding indicators.

[0039] Preferably, in step S4, the evaluation classification level of the highway is obtained through big data, and different threshold ranges are divided into different levels. The levels are set as excellent, good, medium, passable and fail, and different levels have clear meaning and application value.

[0040] Preferably, in step S4, for the divided threshold range, the calculated evaluation score is substituted into the corresponding range to obtain the current status information of the highway, and maintenance measures are made based on different levels.

[0041] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0042] This invention ensures data availability by comprehensively collecting and preprocessing traffic flow, road surface condition, and meteorological data. It provides a complete grasp of key highway operation information, simultaneously mines key information, synchronizes different data by time, improves analysis accuracy, calculates indicators and evaluation scores to achieve quantitative assessment, and comprehensively considers various factors to derive evaluation scores, providing a scientific basis for management decisions. It categorizes and clarifies condition levels for maintenance decisions, formulates maintenance strategies based on levels, achieves rational resource allocation, improves the overall operation and maintenance management level of highways, avoids the problem of relying on single data for evaluation, effectively improves the processing flow, and provides accurate evaluation and analysis. Attached Figure Description

[0043] Figure 1 This is a flowchart of the highway information evaluation steps of the present invention;

[0044] Figure 2 This is a framework diagram for highway information evaluation in this invention. Detailed Implementation

[0045] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0046] Reference Figure 1 As shown, a data management-based highway information evaluation method includes the following evaluation steps:

[0047] S1. Collect highway traffic flow data, road surface condition data, and local weather data, and preprocess the collected data.

[0048] S2. Extract relevant feature values ​​from the preprocessed data, synchronize the processed data according to the time dimension, and match the collected data with each other;

[0049] S3. Based on the data, calculate the road surface damage rate, traffic congestion rate and weather impact rate, calculate the weight vector of each indicator, and calculate the current highway evaluation score based on the weighted average method.

[0050] S4. Based on big data, different levels of evaluation scores are divided. The calculated evaluation scores are then substituted into different levels to assess the current condition of the highway and make corresponding maintenance decisions for each level.

[0051] This application comprehensively covers key factors affecting highway operation by simultaneously collecting traffic flow, road surface condition, and local meteorological data. Traffic flow reflects the real-time usage load of the road; road surface condition is related to driving safety and comfort; and meteorological data has a dynamic impact on traffic and road surface conditions. In rainy weather, combining meteorological data and traffic flow can determine the degree of obstruction to traffic caused by flooded road sections. The preprocessing stage removes noise, outliers, and missing values ​​from the collected data, improving data accuracy and usability. It also corrects erroneous counts in traffic flow data, ensuring that subsequent analysis is based on reliable data and laying a solid foundation for accurately evaluating highway conditions.

[0052] Extracting relevant computational features, such as average vehicle speed and flow rate change rate from traffic flow data, can reveal the patterns and trends behind the data. These features provide quantitative basis for evaluating highways and help to accurately judge traffic operation status. For example, an excessively large flow rate change rate may indicate that traffic congestion is about to occur. Synchronizing different types of data and making them correspond to each other along the time dimension establishes the inherent connection between the data. This allows for a comprehensive analysis of the synergistic effects of traffic, road surface, and meteorological factors at the same time scale. If the road surface becomes slippery due to rainfall at a certain moment, combined with the high traffic flow and reduced vehicle speed at that time, the safety of the highway during that period can be more accurately assessed. Calculating the road surface damage rate, traffic congestion rate, and meteorological impact rate transforms the complex highway conditions into specific quantitative indicators. These indicators intuitively reflect the severity of various problems. For example, a high road surface damage rate indicates that the road surface needs timely repair, and a high traffic congestion rate requires optimization of traffic management measures.

[0053] Based on the evaluation score, corresponding maintenance decisions are made to improve management efficiency and the rationality of resource utilization. For road sections with poor evaluation scores, large-scale road maintenance, traffic organization optimization, or enhanced meteorological monitoring and early warning can be arranged in a timely manner. For road sections with better evaluation scores, routine inspections and small-scale maintenance can be carried out to achieve precise management and ensure the safe and efficient operation of highways.

[0054] Traffic flow data is collected based on cameras, and image analysis is used to determine the traffic flow data within a unit time period. Road surface condition data is collected in real time by a laser detection vehicle equipped with a laser sensor and navigation system. The laser sensor emits a laser beam, and the three-dimensional shape data of the road surface is accurately obtained by measuring the laser reflection time, and the road surface smoothness index is calculated. Local meteorological data is obtained from meteorological stations.

[0055] This application utilizes cameras and image analysis to comprehensively and accurately monitor traffic flow on various road sections, providing real-time feedback on dynamic changes in traffic flow. It can also distinguish vehicle types, providing a basis for traffic management, such as adjusting traffic lights during peak hours and planning truck passage. The laser inspection vehicle uses laser sensors and a navigation system to collect three-dimensional road surface topography data with high precision and speed, calculate smoothness indicators, and provide comprehensive and intuitive data to help road maintenance personnel understand road surface defects and rationally allocate maintenance resources. The weather station collects meteorological data in real time, helping management departments to understand weather conditions in advance and take countermeasures to ensure traffic safety, such as severe weather warnings and traffic control. It also provides support for road maintenance and rationally arranges maintenance work.

[0056] The laser sensor emits a laser beam and measures the reflection time during vehicle movement, using the formula:

[0057] d = c × t / 2

[0058] Where d is the distance value, c is the speed of light, and t is the reflection time. The reflection time is converted into the distance value between the road surface and the sensor.

[0059] Based on the installation height of the laser sensor and the measured distance, the elevation values ​​of each measuring point on the road surface are calculated, resulting in the road surface longitudinal profile elevation data sequence z. i , i = 1, 2, ..., n, where n is the number of measurement points;

[0060] The elevation difference between adjacent measuring points is calculated using the formula: Δz i =z i+1 -z i ;

[0061] Based on the transfer function H(k) of the quarter-car model, the elevation difference sequence is filtered to obtain the vertical displacement difference sequence Δy between the rear axle and the front axle during simulated vehicle movement. i Where k is the spatial frequency;

[0062] The approximate value of IRI is calculated using the following formula:

[0063]

[0064] Where L is the length of the measured road segment, and IRI is the International Roughness Index.

[0065] This application uses a laser sensor to emit a laser beam and measure the reflection time. The reflection time is converted into a distance value using a formula, allowing for precise determination of the distance between the road surface and the sensor. Based on this, the elevation values ​​of each measuring point on the road surface are calculated according to the installation height of the laser sensor, forming a longitudinal profile elevation data sequence. This provides accurate data support for a comprehensive and detailed understanding of the road surface's undulations, helping to accurately locate uneven areas and potential defects. The elevation difference between adjacent measuring points is calculated, and the elevation difference sequence is filtered based on the transfer function of a quarter-car model to obtain a sequence simulating the vertical displacement difference between the rear and front axles of a vehicle during driving. This process simulates the actual situation of a vehicle driving on the road, more realistically reflecting the impact of road surface smoothness on vehicle movement. Finally, by calculating an approximate value of the IRI (Increase in Relief), a scientific and objective indicator is provided for quantitatively evaluating road surface smoothness, enabling accurate comparison of the smoothness of different road sections and facilitating targeted maintenance and repair plans by management departments.

[0066] The relevant calculated characteristic values ​​in step S2 include: traffic flow data during congested periods on the highway within a unit time period, highway capacity data during that period, total area of ​​road surface damage, total road surface area, and the number of meteorological factors. The synchronization steps according to the time dimension are as follows: during each data collection process, a timestamp is attached to the collected data. For traffic flow data, which changes frequently, the timestamp accuracy is accurate to the second and millisecond; road surface condition data changes relatively slowly, and the timestamp accuracy is accurate to the minute; meteorological data is based on the monitoring frequency and is accurate to the second and minute, that is, traffic flow data is recorded as [timestamp 1, traffic flow value 1], road surface smoothness data is recorded as [timestamp 2, IRI value 2], and meteorological data is recorded as [timestamp 3, temperature value 3]; the collected data are then processed accordingly.

[0067] This application enables precise analysis of traffic congestion by acquiring traffic flow and capacity data during peak congestion periods, assisting traffic management departments in developing traffic management plans; it calculates the total area of ​​road damage and the total area of ​​road surface damage, allowing for assessment of road conditions and assisting maintenance departments in carrying out targeted work; it clarifies the number of meteorological factors, facilitating a comprehensive consideration of the impact of weather on highways and providing a basis for traffic and maintenance decisions under severe weather conditions; and it attaches different precision timestamps to the collected data according to their change characteristics, with traffic flow accurate to the second or millisecond, road surface condition accurate to the minute, and meteorological data accurate to the second or minute depending on the monitoring frequency, ensuring data timeliness. Using timestamps as a link, it achieves multi-source data correlation of traffic, road surface, and meteorological data, uncovering potential connections between factors and providing support for the formulation of comprehensive management strategies.

[0068] In step S3, the pavement damage rate reflects the proportion of damaged areas to the total pavement area on the highway. The calculation formula is as follows:

[0069] A = (b / e) * 100%

[0070] Where A is the calculated road surface damage rate, b is the total damaged area of ​​the road surface, and e is the total area of ​​the road surface;

[0071] Traffic congestion rate is used to reflect the congestion situation on highways, and the calculation formula is as follows:

[0072] F' = (G / H) * 100%

[0073] Where F' is the calculated traffic congestion rate, G is the current traffic flow on the highway at the time of data collection, and H is the designed capacity of the highway.

[0074] This application enables quantitative assessment of road surface conditions, helping maintenance departments to develop plans and allocate resources based on damage rates, ensuring safe and comfortable driving; it also provides a scientific and objective basis for highway management decisions, facilitating the development of maintenance and traffic control strategies.

[0075] The formula for calculating the meteorological impact rate in step S3 is:

[0076]

[0077] Where J is the calculated meteorological impact rate, and n' represents the number of meteorological factors considered, including temperature, precipitation, wind speed, and visibility; w i This refers to the weight of the i-th meteorological factor, which is determined through an expert scoring method, f. i (x i Let be the influence function of the i-th meteorological factor, which converts the actual value xi of the meteorological factor into the degree of its impact on traffic. For the visibility factor, its influence function is a piecewise function, expressed as:

[0078]

[0079] V1 and V2 are pre-set visibility thresholds. When the visibility is greater than or equal to V1, it is determined that there is no impact on traffic; when the visibility is less than or equal to V2, it is considered that the impact on traffic has reached its maximum.

[0080] This application's formula encompasses meteorological factors such as temperature, precipitation, wind speed, and visibility, comprehensively reflecting their combined impact on highway traffic. It avoids the limitations of single-factor assessments, employing expert scoring to determine the weights of each factor and assigning reasonable values ​​based on actual conditions and experience. This ensures the calculation results more accurately reflect the role of meteorological factors in traffic, providing a reliable basis for management. The formula quantifies the impact of meteorological factors on traffic using influence functions; for example, the piecewise visibility function clearly presents the changes in impact at different levels. Specific numerical values ​​facilitate analysis and comparison by management departments, enabling them to formulate measures. The formula can be adjusted according to different regional weather, traffic, and road conditions. By changing the weights and function parameters, the accuracy and practicality of the calculation can be improved. Furthermore, it can be refined with research development, accurately calculating meteorological impact rates to assist traffic management departments in making decisions under severe weather conditions, developing contingency plans in advance, rationally allocating resources, evaluating the effectiveness of management measures, and optimizing strategies.

[0081] The steps for calculating the weight vectors of each indicator are as follows: Standardize the calculated values; construct a judgment matrix using the analytic hierarchy process (AHP); compare the relative importance of two indicators; construct a 3×3 judgment matrix M; determine that the road surface damage rate is slightly more important than the traffic congestion rate; and assign the corresponding element M to the judgment matrix. 12 The value is 3, M 21 Then it is Determine the main diagonal element M of the matrix ii =1. After constructing the judgment matrix, a consistency check is performed, calculated using the consistency index CI:

[0082]

[0083] Where λ max This involves determining the largest eigenvalue of the matrix, where na is the matrix order. If na = 3, the random consistency index RI is found. For na = 3, there is a corresponding standard value. The consistency ratio CR is then calculated using the following formula:

[0084]

[0085] When CR < 0.1, the judgment matrix is ​​considered to have acceptable consistency; otherwise, the judgment matrix is ​​readjusted. After passing the consistency test, the weight vector is calculated using the eigenvector method. The eigenvector corresponding to the largest eigenvalue of the judgment matrix M is calculated, and the eigenvector is normalized. The normalized eigenvector is the weight vector W of each index.

[0086] This application eliminates the differences in dimensions and orders of magnitude of different indicators, enabling comparative analysis of all indicators on the same scale. This objectively reflects relative importance, avoids weight bias, and transforms the multi-indicator weighting problem into pairwise comparisons. It utilizes professional knowledge and experience to determine weights, accurately expresses the relative importance of indicators, clarifies the relationships between indicators, lays the foundation for weight calculation, ensures the logical consistency of the judgment matrix, and identifies inconsistencies by calculating relevant indicators. If the conditions are met, the result is reliable; otherwise, the matrix needs to be adjusted to improve the accuracy and reliability of weights. The weight vector is calculated based on the mathematical properties of the judgment matrix, and the result accurately reflects the relative importance of indicators. It has a rigorous theoretical basis, is objective and stable, and the normalization process facilitates comparative analysis, providing a scientific and reliable basis for weighting.

[0087] The current highway's evaluation score is calculated using a weighted average method. The formula for calculating the evaluation score is as follows:

[0088] P = W1*A + W2*F' + W3*J

[0089] Where P is the current evaluation score of the expressway obtained by weighted comprehensive calculation, and W1, W2 and W3 are the weight vector values ​​of the corresponding indicators.

[0090] The weighted average method proposed in this application is highly flexible and the weights can be adjusted according to actual needs and specific circumstances. As highways develop and change, the importance of each indicator may change, new traffic management measures may reduce the impact of traffic congestion, and new pavement materials may improve the pavement's resistance to damage.

[0091] In step S4, the evaluation classification level of the highway is obtained through big data, and different threshold ranges are divided into different levels. The levels are set as excellent, good, medium, passable and fail, and different levels have clear meaning and application value. In step S4, the calculated evaluation score is substituted into the corresponding range range to obtain the current status information of the highway. Based on different levels, maintenance measures are made.

[0092] The clear classification and corresponding status information in this application provide a scientific basis for managers to formulate maintenance measures. Managers can formulate targeted and feasible maintenance plans based on different levels, combined with the actual situation and development plan of the expressway.

[0093] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A method for evaluating highway information based on data management, characterized in that, The evaluation steps are as follows: S1. Collect highway traffic flow data, road surface condition data, and local weather data, and preprocess the collected data. S2. Extract relevant feature values ​​from the preprocessed data, synchronize the processed data according to the time dimension, and match the collected data with each other; S3. Based on the data, calculate the road surface damage rate, traffic congestion rate and weather impact rate, calculate the weight vector of each indicator, and calculate the current highway evaluation score based on the weighted average method. S4. Based on big data, different levels of evaluation scores are divided. The calculated evaluation scores are then substituted into different levels to assess the current condition of the highway and make corresponding maintenance decisions for each level. In step S3, the pavement damage rate reflects the proportion of damaged areas to the total pavement area on the highway. The calculation formula is as follows: A = (b / e) * 100% Where A is the calculated road surface damage rate, b is the total damaged area of ​​the road surface, and e is the total area of ​​the road surface; Traffic congestion rate is used to reflect the congestion situation on highways, and the calculation formula is as follows: F' = (G / H) * 100% Where F' is the calculated traffic congestion rate, G is the current traffic flow on the highway when the data was collected, and H is the designed capacity of the highway. The formula for calculating the meteorological impact rate in step S3 is: Where J is the calculated meteorological impact rate, and n' represents the number of meteorological factors considered, including temperature, precipitation, wind speed, and visibility; w i This refers to the weight of the i-th meteorological factor, which is determined through an expert scoring method, f. i (x i ) is the influence function of the i-th meteorological factor, which represents the actual value x of the meteorological factor. i Converting this to the degree of impact on traffic, for visibility factors, the impact function is a piecewise function, expressed as: V1 and V2 are pre-set visibility thresholds. When the visibility is greater than or equal to V1, it is determined that there is no impact on traffic; when the visibility is less than or equal to V2, it is considered that the impact on traffic has reached its maximum.

2. The highway information evaluation method based on data management according to claim 1, characterized in that, Traffic flow data is collected based on cameras, and image analysis is used to determine the traffic flow data within a unit time period. Road surface condition data is collected in real time by a laser detection vehicle equipped with a laser sensor and navigation system. The laser sensor emits a laser beam, and the three-dimensional shape data of the road surface is accurately obtained by measuring the laser reflection time, and the road surface smoothness index is calculated. Local meteorological data is obtained from meteorological stations.

3. The method for evaluating highway information based on data management according to claim 2, characterized in that, The laser sensor emits a laser beam and measures the reflection time during vehicle movement, using the formula: d = c × t / 2 Where d is the distance value, c is the speed of light, and t is the reflection time. The reflection time is converted into the distance value between the road surface and the sensor. Based on the installation height of the laser sensor and the measured distance, the elevation values ​​of each measuring point on the road surface are calculated, resulting in the road surface longitudinal profile elevation data sequence z. i , i = 1, 2, ..., n, where n is the number of measurement points; The elevation difference between adjacent measuring points is calculated using the formula: Δz i =z i+1 -z i ; Based on the transfer function H(k) of the quarter-car model, the elevation difference sequence is filtered to obtain the vertical displacement difference sequence Δy between the rear axle and the front axle during simulated vehicle movement. i Where k is the spatial frequency; The approximate value of IRI is calculated using the following formula: Where L is the length of the measured road segment, and IRI is the International Roughness Index.

4. The method for evaluating highway information based on data management according to claim 1, characterized in that, The relevant calculated characteristic values ​​in step S2 include: traffic flow data during congested periods on highways within a unit time period, highway capacity data during congested periods, total area of ​​road surface damage, total road surface area, and the number of meteorological factors. The synchronization steps according to the time dimension are as follows: during each data collection process, a timestamp is attached to the collected data. For traffic flow data, which changes frequently, the timestamp accuracy is accurate to the second or millisecond; road surface condition data changes relatively slowly, and the timestamp accuracy is accurate to the minute; meteorological data is based on the monitoring frequency and is accurate to the second or minute, that is, traffic flow data is recorded as [timestamp 1, traffic flow value 1], road surface smoothness data is recorded as [timestamp 2, IRI value 2], and meteorological data is recorded as [timestamp 3, temperature value 3]; the collected data are then processed accordingly.

5. The method for evaluating highway information based on data management according to claim 1, characterized in that, The steps for calculating the weight vectors of each indicator are as follows: Standardize the calculated values; construct a judgment matrix using the analytic hierarchy process (AHP); compare the relative importance of two indicators; construct a 3×3 judgment matrix M; determine that the road surface damage rate is more important than the traffic congestion rate; and assign the corresponding element M to the judgment matrix. 12 The value is 3, M 21 Then it is Determine the main diagonal element M of the matrix ii =1. After constructing the judgment matrix, a consistency check is performed, calculated using the consistency index CI: Where λ max This involves determining the largest eigenvalue of the matrix, where na is the matrix order. If na = 3, the random consistency index RI is found. For na = 3, there is a corresponding standard value. The consistency ratio CR is then calculated using the following formula: When CR < 0.1, the judgment matrix is ​​considered to have acceptable consistency; otherwise, the judgment matrix is ​​readjusted. After passing the consistency test, the weight vector is calculated using the eigenvector method. The eigenvector corresponding to the largest eigenvalue of the judgment matrix M is calculated, and the eigenvector is normalized. The normalized eigenvector is the weight vector W of each index.

6. The highway information evaluation method based on data management according to claim 1, characterized in that, The current highway's evaluation score is calculated using a weighted average method. The formula for calculating the evaluation score is as follows: P = W1*A + W2*F' + W3*J Where P is the current evaluation score of the expressway obtained by weighted comprehensive calculation, and W1, W2 and W3 are the weight vector values ​​of the corresponding indicators.

7. The method for evaluating highway information based on data management according to claim 1, characterized in that, In step S4, the highway's grade is obtained through big data, and different threshold ranges for different grades are divided. The grades are set as excellent, good, medium, passable, and failable, and each grade has a clear meaning and application value.

8. The method for evaluating highway information based on data management according to claim 1, characterized in that, In step S4, the calculated evaluation score is substituted into the corresponding threshold interval to obtain the current condition information of the highway, and maintenance measures are made based on different levels.

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