A method and system for evaluating road disease maintenance priority based on trend prediction

Abstract

This invention relates to the field of road maintenance technology, specifically to a method and system for prioritizing pavement distress maintenance based on trend prediction. The method includes: S1, establishing a road network lane calculation grid, dividing each lane into continuous calculation segments along the longitudinal direction, marking the lane affiliation and spatial location of each segment, and establishing a spatial mapping relationship between adjacent lanes and longitudinal segments; S2, simulating the lateral offset trajectory of vehicles passing through the distressed area based on the lane and longitudinal segment location of the distress. This invention, by constructing lane-level gridded calculation units and incorporating the lateral position dimension of wheel tracks, simulates the lateral offset trajectory and lane-changing behavior of vehicles avoiding distress, quantifies the longitudinal attenuation distribution law of axle load transfer to adjacent lanes, predicts the scale of secondary distress increase in adjacent lanes under additional load, and converts the traffic efficiency loss caused by vehicle lane changes into a uniform-dimensional equivalent index.

Description

Technical Field

[0001] This invention relates to the field of road maintenance technology, specifically to a method and system for prioritizing pavement distress maintenance based on trend prediction. Background Technology

[0002] Road surfaces, under the long-term influence of vehicle loads and changes in environmental temperature and humidity, will gradually develop various defects such as cracks, potholes, and subsidence. Maintenance priority assessment is a core aspect of road operation and maintenance management, directly determining the efficiency of the allocation of limited maintenance resources and affecting the overall traffic safety and service life of the road network. Currently, the industry generally uses automated inspections to collect road surface defect data, combining this data with parameters such as traffic flow and service status to quantify and rank maintenance priorities, thereby guiding the scheduling of maintenance operations. With the improvement of inspection and data collection accuracy, richer information on the location of defects can be obtained, placing higher demands on the precision of priority assessment.

[0003] Patent CN119990639A discloses a smart city road maintenance management method and system. This scheme collects the area and coordinates of road surface defects, divides the road network into several segments using road intersections as nodes, calculates the segment damage coefficient by combining the number of defects within the segment, the total area of ​​defects, and the longitudinal distribution dispersion, and then overlays two parameters—average traffic volume and time since the last maintenance—to obtain a weighted maintenance priority coefficient and rank the segments accordingly. This scheme also statistically analyzes the newly added defect area in each period and fits a standard curve of defect development for similar road segments to identify abnormal damage conditions.

[0004] Current pavement distress severity assessments only calculate the total area, quantity, and longitudinal distribution dispersion of distress, without considering the overlap between the distress's location in the lateral lane and the vehicle's wheel track zone. During vehicle travel, the wheel track zone experiences significantly higher frequency of load compaction than other lane areas. Distress within the wheel track zone expands much faster under repeated loads, resulting in a stronger impact on driving comfort and safety. For the same area and quantity of pavement distress, a concentration in the wheel track zone significantly increases the urgency of maintenance. However, the current assessment system does not incorporate this dimension, often underestimating the maintenance priority of these sections and failing to address them before rapid spread. This increases subsequent repair costs and raises safety hazards. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method and system for prioritizing pavement distress maintenance based on trend prediction. This solves the problem that existing maintenance priority assessments do not incorporate the lateral positional correlation between distress and wheel track, and cannot predict the transmission impact of secondary distresses, resulting in insufficient sorting accuracy.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for prioritizing pavement distress maintenance based on trend prediction, comprising: S1. Establish a road network lane calculation grid, divide each lane into continuous calculation segments along the longitudinal direction, mark the lane affiliation and spatial location of each segment, and establish the spatial mapping relationship between adjacent lanes and longitudinal segments. S2. Based on the lane and longitudinal section location of the defect, simulate the lateral offset trajectory of the vehicle when passing through the defect area, and determine the proportion of vehicles that trigger lane change behavior when the defect is within the wheel track area. S3. Based on the proportion of vehicles changing lanes and the lateral offset trajectory, calculate the axle load increment of the affected section of the adjacent lane. Along the longitudinal direction away from the location of the defect, the load increment gradually decreases. S4. Retrieve historical damage records and pavement service parameters of the affected sections of adjacent lanes, and combine them with the calculated load increment distribution to predict the new area of ​​secondary damage in the next maintenance cycle. S5. Calculate the impact of vehicle trajectory deviation on the road segment's traffic capacity based on the proportion of vehicles changing lanes and the total length of the affected section. S6. Summarize the original damage values ​​of all defects within the road section, add the equivalent of secondary damage and the equivalent of traffic impact, normalize and then sum the weighted values ​​to obtain the comprehensive damage equivalent of the road section. S7. After standardizing the comprehensive damage equivalent, road segment traffic flow, and maintenance interval parameters, a weighted calculation is performed to obtain the maintenance priority coefficient for each road segment. The maintenance task list is generated by sorting the coefficients from high to low.

[0007] Furthermore, step S1 specifically includes: Import basic data on road grade, number of lanes, and lane width, and divide each lane into continuous calculation segments along the direction of travel at fixed lengths; Each calculation segment is assigned a unique spatial code, which includes the lane number and longitudinal sequence number; Mark the lateral position range of the standard wheel track for each lane. The wheel track is determined by shrinking inward at a fixed ratio based on the left and right boundaries of the lane. Establish the corresponding relationship between adjacent lanes on the left and right and longitudinal position segments to form a spatial mapping index.

[0008] Furthermore, step S2 specifically includes: Determine whether the lateral coverage width of the defect exceeds the wheel track area. If it does, increase the vehicle's average lateral offset by the excess ratio. The average lateral offset and lane change trigger ratio for vehicle avoidance are determined based on the ratio of the lateral width of the defect to the width of the wheel track. The influence range is calculated by extending the length forward and backward along the longitudinal direction, and the longitudinal extension length of the affected section is determined by the severity level of the disease. Output the vehicle offset trajectory data and the range of the affected adjacent lane sections for each defect.

[0009] Furthermore, step S3 specifically includes: The axle load frequency of the original standard wheel track is used as the benchmark value; The increase in the number of axle loads affected is calculated based on the proportion of vehicles changing lanes. The increase is equal to the baseline value multiplied by the proportion of vehicles changing lanes. Based on the lateral position distribution after vehicle offset, the additional axle load is distributed to the corresponding lateral area of ​​the adjacent lane; Along the longitudinal direction away from the location of the disease, the load increment decreases by a fixed attenuation ratio for each calculation section. After exceeding the preset number of affected sections, the load increment returns to zero.

[0010] Furthermore, step S4 specifically includes the following steps: Retrieve historical records of the increase in the area of ​​damage to adjacent lanes and data on the service life of the road surface; The natural growth rate of disease under the baseline load condition is extracted as the base value; The growth rate is calculated based on the ratio of the load increment to the reference load, and the growth rate is positively correlated with the load increment. The revised growth rate is obtained by multiplying the base growth rate by the growth multiplier. For sections where diseases already exist, the amplification coefficient resulting from the original disease density is added to obtain the final predicted area of ​​new secondary diseases.

[0011] Furthermore, step S5 specifically includes: Retrieve the design speed and historical average traffic flow data for the road segment; Calculate the average speed reduction caused by lane-changing behavior based on the proportion of vehicles changing lanes and the total length of the affected section; The amount of traffic flow loss per unit time is used as a quantitative indicator of traffic efficiency. By combining the historical average traffic volume of the road segment, the traffic loss is converted into a traffic impact equivalent with uniform dimensions.

[0012] Furthermore, step S6 specifically includes: The total area and total number of all defects within the road section are counted. Calculate the dispersion index of disease distribution in the longitudinal direction; The original damage value is obtained by multiplying the total area by the area weighting coefficient, the total quantity by the quantity weighting coefficient, and the longitudinal dispersion by the dispersion weighting coefficient. The primary damage value, secondary damage equivalent, and traffic impact equivalent are normalized to ensure that the three values ​​are within the same range.

[0013] Furthermore, in step S7, the comprehensive damage equivalent, road segment traffic flow, and maintenance interval time parameters are standardized and then weighted to obtain the maintenance priority coefficient for each road segment, including: The three parameters of comprehensive damage equivalent, average traffic flow, and time since last maintenance are standardized respectively. The weighting ratio of the three parameters is adjusted according to the road grade and pavement type; The maintenance priority coefficients for each road segment are obtained by multiplying the standardized parameter values ​​by their corresponding weighting coefficients and then summing them. For road sections with high secondary risks, mark the range of their transmission impact and the level of secondary risk.

[0014] Furthermore, in step S7, generating the maintenance task list includes: All road sections are sorted from highest to lowest according to their maintenance priority coefficient; For each road segment, indicate the suggested maintenance period, estimated operation time, and type of resource requirement; Simultaneously label the affected adjacent sections and the potential extent of road damage in secondary risk sections; Generate a structured task list and push it to the job scheduling module of the operation and maintenance management system.

[0015] This invention also provides a trend prediction-based pavement distress maintenance priority assessment system, comprising: The road network lane modeling unit is used to establish the road network lane calculation grid, divide each lane into continuous calculation segments along the longitudinal direction, mark the lane affiliation and spatial location of each segment, and establish the spatial mapping relationship between adjacent lanes and longitudinal segments. The defect avoidance trajectory simulation unit is used to simulate the lateral deviation trajectory of a vehicle when passing through the defect area based on the lane and longitudinal section location of the defect, and to determine the proportion of vehicles that trigger lane change behavior when the defect is within the wheel track area. The load transfer distribution calculation unit is used to calculate the axle load increment of the affected section of the adjacent lane based on the proportion of lane-changing vehicles and the lateral offset trajectory. The load increment gradually decreases along the longitudinal direction away from the location of the defect. The secondary disease prediction unit is used to retrieve historical disease records and pavement service parameters of the affected sections of adjacent lanes, and combine them with the calculated load increment distribution to predict the new area of ​​secondary diseases in the next maintenance cycle. The traffic impact quantification unit is used to calculate the degree of impact of vehicle trajectory deviation on the traffic capacity of the road segment based on the proportion of lane-changing vehicles and the total length of the affected section. The comprehensive damage equivalent aggregation unit is used to summarize the original damage values ​​of all defects in the road segment, add the secondary damage equivalent and traffic impact equivalent, and then normalize and weighted sum to obtain the comprehensive damage equivalent of the road segment. The maintenance priority generation unit is used to standardize and weight the comprehensive damage equivalent with road segment traffic flow and maintenance interval parameters to obtain the maintenance priority coefficient of each road segment, and generate a maintenance task list by sorting the coefficients from high to low.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention constructs lane-level gridded computing units and incorporates the lateral position dimension of wheel tracks to simulate the lateral offset trajectory and lane-changing behavior of vehicles avoiding defects. It quantifies the longitudinal attenuation distribution law of axle load transfer to adjacent lanes, predicts the scale of secondary defects in adjacent lanes under additional loads, and converts the traffic efficiency loss caused by vehicle lane changes into a uniform equivalent index. Based on this, it aggregates three types of values: primary damage, secondary damage, and traffic impact, and superimposes traffic flow and maintenance interval parameters to calculate the maintenance priority coefficient of each road segment and generate a task list. This corrects the shortcomings of the original assessment system, which only focuses on the area, quantity, and longitudinal dispersion of defects and does not consider the degree of overlap between defects and wheel tracks. It allows defects distributed in the wheel track area to obtain priority weights that match their actual damage impact, enabling treatment before the defects accelerate their expansion, controlling subsequent maintenance costs, reducing driving safety hazards, and identifying the risk of cascading damage in adjacent lanes in advance. It also coordinates the scope of work, improves the allocation efficiency of maintenance resources, and adapts to the daily operation and maintenance management needs of road networks of different sizes. Attached Figure Description

[0017] Figure 1 This is a flowchart of the maintenance priority assessment method of the present invention; Figure 2 This is the load transfer and secondary disease prediction logic diagram of the present invention; Figure 3 This is a diagram of the road surface distress maintenance priority assessment system of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Please see Figures 1-2This invention provides a method for prioritizing pavement distress maintenance based on trend prediction, comprising: S1, establishing a road network lane calculation grid, dividing each lane into continuous calculation segments along the longitudinal direction, marking the lane affiliation and spatial location of each segment, and establishing a spatial mapping relationship between adjacent lanes and longitudinal segments; S2, simulating the lateral offset trajectory of vehicles passing through the distress area based on the lane and longitudinal segment location of the distress, and determining the proportion of vehicles triggering lane-changing behavior when the distress is within the wheel track zone; S3, calculating the axle load increment of the affected segment in adjacent lanes based on the lane-changing vehicle proportion and the lateral offset trajectory, with the load increment decreasing segment by segment along the longitudinal direction away from the distress location; S 4. Retrieve historical damage records and pavement service parameters of the affected sections of adjacent lanes, and combine them with the calculated load increment distribution to predict the new area of ​​secondary damage in the next maintenance cycle; S5. Calculate the impact of vehicle trajectory deviation on the traffic capacity of the road section based on the proportion of lane-changing vehicles and the total length of the affected section; S6. Summarize the original damage values ​​of all defects in the road section, superimpose the equivalent of secondary damage and the equivalent of traffic impact, normalize and then weight and sum to obtain the comprehensive damage equivalent of the road section; S7. After standardizing the comprehensive damage equivalent with the road section traffic flow and maintenance interval parameters, weight and calculate to obtain the maintenance priority coefficient of each road section, and generate a maintenance task list by sorting the coefficients from high to low.

[0020] Specifically, the implementation process uses a gridded road network model as its spatial basis, and all calculations are carried out in calculation segments with uniform granularity. Basic road network data, historical pavement data, and traffic monitoring data are retrieved from their respective maintenance databases, cleaned, and spatially matched before being mapped to each calculation segment to ensure the accuracy of the input data matches the granularity of the calculation. The simulation of vehicle lateral deviation trajectories references statistical patterns of daily driving behavior. When a pavement defect intrudes into the wheel track area, the driver adjusts their direction to avoid bumps; the deviation increases with the width of the defect coverage. When the deviation exceeds the adjustable range within a single lane, the vehicle triggers a lane change, and the proportion of lane-changing vehicles directly reflects the degree of interference of the defect on traffic flow. The increment of axle load frequency gradually decreases along the longitudinal direction, matching the actual pattern of vehicles gradually returning to their original driving trajectory after leaving the defect area, preventing the load impact range from being infinitely amplified. The prediction of secondary defects establishes a correlation between load changes and pavement damage development, allowing for the early identification of potential damage risks in adjacent lanes and filling the gap in existing assessments that only focus on the current defect state. Quantifying the impact on traffic capacity transforms traffic efficiency losses into an equivalent index that can be superimposed on the degree of damage, allowing priority assessment to simultaneously cover both pavement structure condition and road traffic efficiency. After the comprehensive damage equivalent is used to normalize and aggregate multi-dimensional indicators, traffic flow and maintenance interval are incorporated as maintenance parameters. The final output maintenance priority ranking can directly guide the allocation of maintenance resources, prioritizing the treatment of road sections with wheel track defects and high secondary risks, thus avoiding increased maintenance costs and safety hazards caused by rapid expansion of defects. The entire calculation process is automated, and maintenance ranking results for the entire road network can be generated in batches after data input, adapting to the operation and maintenance management needs of road networks of different sizes.

[0021] In a specific embodiment, step S1 specifically includes: importing basic data on road grade, number of lanes, and lane width; dividing each lane into continuous calculation segments along the driving direction at a fixed length; assigning a unique spatial code to each calculation segment, the code including lane number and longitudinal sequence number; marking the lateral position range of the standard wheel track of each lane, the wheel track being determined by shrinking inward at a fixed ratio based on the left and right boundaries of the lane; establishing the corresponding association between adjacent lanes and longitudinal position segments to form a spatial mapping index.

[0022] Specifically, basic data such as road grade, number of lanes, and lane width are stored in the road network operation and maintenance database. This data originates from road completion and acceptance documents and daily operation and maintenance update records, and can be directly retrieved by road segment number. Fixed-length segment divisions ensure uniform calculation granularity across the entire road network, facilitating lateral comparisons between different road segments. Segment lengths can be set with reference to the accuracy of inspection data collection; for example, 10 meters can be used as a single segment length to match the sampling interval of routine automated inspections. Spatial coding uses a combined coding rule. Lane numbers are arranged sequentially from left to right, with the vertical sequence increasing from small to large along the driving direction. The spatial location of a segment can be directly located through coding, eliminating the need for additional coordinate data storage. The lateral range of the wheel track is calculated proportionally to the lane width, extending inwards by one-fifth of the lane width from the left and right boundaries. The central area is the standard wheel track, corresponding to the main area of ​​tire wear during normal vehicle operation and also the area where road surface defects develop most rapidly. The spatial mapping index uses the vertical sequence number as the matching key to associate and bind adjacent lane segments at the same vertical position. When calculating load transfer across lanes, the parameters of the corresponding segment can be quickly retrieved directly through the index, improving calculation efficiency.

[0023] In a specific embodiment, step S2 specifically includes: determining whether the lateral coverage width of the defect exceeds the wheel track area; if it does, increasing the average lateral offset of the vehicle by the excess ratio; determining the average lateral offset of the vehicle and the lane change trigger ratio based on the ratio of the lateral width of the defect to the width of the wheel track; calculating the impact range along the longitudinal direction forward and backward, and determining the longitudinal extension length of the affected section based on the severity level of the defect; and outputting the vehicle offset trajectory data corresponding to each defect and the range of the affected adjacent lane sections.

[0024] Specifically, the lateral coverage width of defects is extracted from the defect contour data identified during inspections. This inspection data, collected by an automated inspection vehicle, is processed through image recognition to obtain the location, size, type, and severity level of each defect, which is then matched to the corresponding calculation segment using spatial coding. The vehicle's lateral offset trajectory employs a sinusoidal smooth transition model, with the defect's central section as the peak offset point, gradually decreasing towards both sides along a sinusoidal curve to a zero offset state. This trajectory change conforms to the smoothness characteristics of actual driving. When the defect is completely within the wheel track area, the vehicle performs a small avoidance maneuver within its lane. The average lateral offset amplitude is linearly positively correlated with the ratio of the defect's lateral width to the wheel track width, with the maximum offset not exceeding the distance from one side of the wheel track boundary to the lane boundary. When the defect's lateral width exceeds the wheel track area, the adjustable lateral space within the lane is insufficient for complete avoidance, and the vehicle directly triggers a lane change. The lane change trigger ratio increases synchronously with the percentage of the defect's width exceeding the wheel track. The specific functional relationship between offset amplitude and lane change width was calibrated using real-vehicle driving observation data. When the ratio of the lane change width to the wheel track width is 0.3, 0.6, and 1.0, the corresponding average offset amplitude within the lane is 0.2, 0.4, and 0.6 times the wheel track width, respectively. When the ratio of the lane change width to the wheel track width reaches 0.2, the baseline value for lane-changing vehicles is set at 0.3. For every 0.1 increase in the excess ratio, the lane-changing ratio increases by 0.15. The correspondence between offset amplitude and lane-changing ratio is statistically calibrated based on a large amount of driving behavior observation data, covering typical scenarios with different lane change levels and lane widths. The severity of lane change is classified into three levels according to industry standards: mild, moderate, and severe. The higher the level, the longer the longitudinal extension of the affected section. For example, a mild lane change extends longitudinally by 2 calculation sections, and a severe lane change extends longitudinally by 5 calculation sections. The affected area is symmetrically distributed forward and backward along the center of the lane change. The output offset trajectory data includes the lateral offset of the vehicle at each longitudinal position, and the range of the affected adjacent lane sections clearly defines the spatial boundary of load transfer, providing input parameters for subsequent axle load increment calculation.

[0025] In a specific embodiment, step S3 specifically includes: using the axle load frequency of the original standard wheel track as a baseline value; calculating the incremental number of affected axle loads based on the proportion of lane-changing vehicles, the increment being equal to the baseline value multiplied by the proportion of lane-changing vehicles; allocating the additional axle load to the corresponding lateral area of ​​the adjacent lane based on the lateral position distribution after vehicle offset; along the longitudinal direction away from the location of the defect, the load increment decreases by a fixed attenuation ratio after passing through each calculation segment; after exceeding the preset number of affected segments, the load increment returns to zero.

[0026] Specifically, the baseline axle load frequency of the standard wheel track zone is calculated from road segment traffic monitoring data. The traffic monitoring database stores the daily average traffic flow and axle load spectrum data of the road segment. The total number of standard axle load actions within the maintenance cycle is calculated based on the maintenance cycle duration, and then converted into a baseline value according to the lateral distribution ratio of the wheel track zone. This calculation method is constructed with reference to the load distribution pattern after the lateral shift of traffic flow. The additional load caused by vehicle lane changes reaches its peak at the center of the defect. During the departure process, the vehicle trajectory gradually returns to the original lane, so the load increment decreases exponentially with longitudinal distance. In the calculation, the baseline axle load frequency is first multiplied by the proportion of lane-changing vehicles to obtain the maximum axle load increment in the defect center section. Then, using the longitudinal attenuation coefficient as the base and the number of distance segments between the calculation section and the defect center as the exponent, the attenuated increment at the corresponding position is obtained. The corresponding calculation relationship is as follows: ; in For the first The increment of axle load cycles in each calculation section The reference axle load frequency for the standard wheel track belt. The proportion of vehicles changing lanes. The longitudinal attenuation coefficient is... This refers to the segment number corresponding to the disease center. To calculate the number of segments that are the distance between a section and the center of the disease.

[0027] The longitudinal attenuation coefficient is determined through regression analysis of measured traffic flow data for road segments. Lateral offset distributions of vehicles at different longitudinal positions are collected, and the attenuation pattern is fitted; for example, a value of 0.65 can be used. The preset number of affected segments can be set to 5. When the distance to the center of the defect exceeds 5 segments, the load increment returns to zero, matching the actual situation of vehicles gradually returning to their original driving trajectories. Taking a pothole defect on a main road in a city as an example, the baseline axle load frequency for the corresponding segment is 12,000 times per maintenance cycle, the lane-changing vehicle ratio is 0.25, and the defect center is located in calculation segment 32. Therefore, the axle load increment in segment 33, which is adjacent to the defect center, is 1,950 times, and in segment 34, it is 1,267.5 times, gradually decreasing with increasing distance. The lateral distribution of the additional axle load follows the concentrated distribution pattern of the wheel track zone, adopting a normal distribution form and concentrating on the center area of ​​the wheel track after the adjacent lane offset. The distribution bandwidth is consistent with the standard wheel track zone width and will not spread evenly across the entire lane width. The lateral area for receiving loads in adjacent lanes is defined by extending half the standard wheel track width to each side, centered on the original lane's wheel track after lateral offset. For multi-lane roads with three or more lanes, defects in the middle lane only transfer the load to the adjacent lanes on either side, without considering secondary transmission across lanes. The bidirectional effects are calculated independently and then superimposed onto the corresponding adjacent lanes to ensure that the calculation results closely match the actual load distribution.

[0028] In a specific embodiment, step S4 includes: retrieving historical records of the increase in the area of ​​defects in the affected sections of adjacent lanes and data on the service life of the road surface; extracting the natural growth rate of defects under the baseline load condition as the base value; calculating the growth multiple based on the ratio of the load increment to the baseline load, wherein the growth multiple is positively correlated with the load increment; multiplying the base growth rate by the growth multiple to obtain the corrected growth rate; and for sections with existing defects, adding the increase coefficient brought about by the original defect density to obtain the final predicted area of ​​new secondary defects.

[0029] Specifically, historical damage data for affected sections of adjacent lanes is stored in the road inspection database. Damage data collected by annual automated inspections is archived and stored by section. Each section must retain records of damage area, type, and location for at least three consecutive maintenance cycles to ensure the statistical reliability of the growth rate fitting. Road service life data is retrieved from the road network operation and maintenance basic database to assist in judging the degree of road aging. For newly built roads or sections with less than three cycles of historical data, the benchmark growth rate of adjacent road sections of the same level, road type, and traffic load level is used as a substitute value, which is then adjusted in conjunction with the service life of the current road section before being used in the calculation. The natural growth rate of damage under the benchmark load condition is obtained by fitting historical damage data from multiple consecutive cycles. During the fitting process, historical cycle data with stable axle load levels are selected, abnormal periods of temporary fluctuations due to heavy traffic are eliminated, and the basic contributions of non-load factors such as environmental temperature and humidity and material aging are separated. These are then superimposed with the load-driven contributions under the standard axle load level to jointly constitute the benchmark natural growth rate, reflecting the rate of damage development under the combined effects of environmental factors and normal loads. The rate of pavement distress development is directly related to the number of axle load applications. Based on this, a calculation relationship for secondary distress growth is constructed. The axle load increment obtained in the previous steps is the key input parameter for this calculation; the larger the load increment, the higher the distress growth rate. The calculation first uses the base growth rate to obtain the natural growth area for the baseline period. Then, the growth rate is calculated using the ratio of the load increment to the baseline load. After adding the accelerating effect of the existing distress density, the final predicted new area is obtained. The corresponding calculation relationship is as follows: ; in The predicted increase in area due to secondary diseases. This represents the area of ​​natural disease growth within the baseline period. This is the load influence factor. This represents the increment of axle load cycles for the corresponding section. As the reference axle load frequency, This is the coefficient for increasing the density of existing diseases.

[0030] The load influence coefficient was calibrated using long-term observation data of similar road surfaces, matching the rate of damage growth under different axle load levels to obtain the corresponding proportional relationship. The original damage density increase coefficient was set based on the statistical regularity of historical damage data; the higher the proportion of existing damage area in a section, the greater the probability of new damage, and the coefficient value increased accordingly. Using the scenario parameters of the aforementioned pothole damage, the corresponding section's base period natural growth area is 1.8 square meters, the load influence coefficient is 1.1, the axle load increment is 1950 cycles, the base axle load is 12000 cycles, and the original damage density increase coefficient is 1.05. Calculations show that the predicted new area of ​​secondary damage is approximately 2.23 square meters. The calculated new area of ​​secondary damage can be converted into secondary damage equivalent, maintaining the same dimensions as the primary damage value.

[0031] In a specific embodiment, step S5 specifically includes: retrieving the design speed and historical average traffic flow data of the road segment; calculating the average speed reduction caused by the lane-changing behavior based on the proportion of vehicles changing lanes and the total length of the affected section; using the amount of traffic flow loss per unit time as a quantitative indicator of traffic efficiency; and converting the amount of traffic flow loss into a traffic impact equivalent with uniform dimensions by combining the historical average traffic flow of the road segment.

[0032] Specifically, the design speed of the road segment is retrieved from the road network operation and maintenance database, and historical average traffic flow data is extracted from the traffic monitoring database, using the average monthly traffic flow of the past twelve months as the calculation benchmark. During lane changes, vehicles may decelerate or weave in, interfering with the normal driving of adjacent lanes. The higher the proportion of vehicles changing lanes and the longer the affected section, the more significant the traffic flow disturbance and the greater the speed reduction. The calculation of the average speed reduction is based on the traffic flow fluctuation mechanism; the traffic flow disturbance caused by lane changes propagates longitudinally, ultimately reflected in a decrease in the average speed of the road segment. The reduction is positively correlated with both the proportion of lane changes and the affected length. The traffic flow loss is the difference between the normal traffic flow and the affected traffic flow, reflecting the loss of traffic benefits caused by the damage. The traffic impact equivalent is converted using the extreme value normalization method. Using the maximum traffic flow loss of roads of the same level within the road network as a benchmark, the traffic flow loss of each road segment is mapped to a range of 0 to 1, maintaining the same numerical range as the damage equivalent. The comprehensive damage equivalent is a dimensionless relative assessment index, used only for horizontal ranking and comparison of maintenance priorities between road sections. It does not represent the sum of absolute physical quantities. Each index achieves weighted fusion of relative importance through preset weights, supporting the quantitative ranking of maintenance decisions. This allows for the weighted superposition of two types of indicators with different attributes, ensuring the rationality of the comprehensive assessment.

[0033] In a specific embodiment, step S6 specifically includes: calculating the total area and total number of all defects within the road segment; calculating the dispersion index of the longitudinal distribution of defects; multiplying the total area by the area weighting coefficient, the total number by the number weighting coefficient, and the longitudinal dispersion by the dispersion weighting coefficient, and then summing them to obtain the primary damage value; and normalizing the primary damage value, the secondary damage equivalent, and the traffic impact equivalent so that the three values ​​are in the same range.

[0034] Specifically, the total area and number of all defects within a road segment are obtained by statistically analyzing all defect data matched to that segment, directly reflecting the overall scale of defects in the segment. The dispersion of the longitudinal distribution of defects is calculated using the distribution variance of the defect segments; the more dispersed the defect distribution, the higher the dispersion, and the stronger the sustained impact on traffic. The weighting coefficients of the three indicators can be adjusted according to the road's functional positioning. Roads oriented towards heavy traffic can have their area weight increased, while urban roads oriented towards passenger cars can have their dispersion weight appropriately increased. The weighting values ​​are determined through expert surveys and calibration using historical operation and maintenance data. Normalization uses the extreme value normalization method, with the maximum value of the corresponding indicator for roads of the same level within the road network as the denominator and each indicator value as the numerator, mapping all values ​​to the interval between 0 and 1 to eliminate calculation bias caused by dimensional differences. The secondary damage equivalent only counts the scale of defects newly added to adjacent lanes due to this load transfer. Before calculation, existing defects already included in the original damage value of the corresponding segment are pre-removed to avoid duplicate statistics of existing defects. After the three indicators are normalized, they can be weighted and summed according to preset weights to obtain the comprehensive damage equivalent of the road segment. This indicator is a dimensionless relative ranking indicator that integrates the relative importance of three dimensions: current damage status, future expansion risk and traffic impact, and comprehensively reflects the current damage status, potential secondary risks and traffic impact of the road segment.

[0035] In a specific embodiment, step S7 involves standardizing the comprehensive damage equivalent, traffic flow, and maintenance interval parameters, and then weighting them to obtain the maintenance priority coefficient for each road segment. This includes: standardizing the comprehensive damage equivalent, average traffic flow, and time since the last maintenance; adjusting the weight distribution ratio of the three parameters according to the road grade and pavement type; multiplying the standardized parameter values ​​by their corresponding weight coefficients and summing them to obtain the maintenance priority coefficient for each road segment; and marking the transmission impact range and secondary risk level for road segments with high secondary risks.

[0036] Specifically, the standardization of the three parameters uses the mean and standard deviation of parameters from roads of the same grade to eliminate fundamental differences between roads of different grades and ensure fairness in priority ranking. The weighting ratio is adjusted according to road grade; higher-grade arterial roads have a higher weighting for traffic flow, emphasizing the importance of traffic efficiency in maintenance decisions; lower-grade secondary roads have a higher weighting for comprehensive damage equivalent, focusing on the repair of pavement structure. The specific weighting ratio is set according to the road operation and maintenance management objectives and can be adjusted according to maintenance strategies. High secondary risk sections refer to sections where the predicted scale of secondary damage exceeds a preset threshold, which can be set based on the total maintenance resources and risk control requirements. The affected adjacent lane sections are marked with the transmission impact range. Secondary risk levels are divided into different levels according to the predicted new area, facilitating maintenance personnel to intuitively identify cascading damage risks and comprehensively consider the treatment needs of adjacent sections when formulating maintenance plans, avoiding repeated damage caused by incomplete single operations.

[0037] In a specific embodiment, step S7, generating the maintenance task list includes: sorting all road segments from high to low according to the maintenance priority coefficient; marking the suggested maintenance period, estimated operation time and resource demand type for each road segment; simultaneously marking the affected adjacent sections and potential disease expansion scale of secondary risk road segments; generating a structured task list and pushing it to the operation and maintenance management system's operation scheduling module.

[0038] Specifically, the sorted list clearly reflects the urgency of maintenance for each road segment, with higher priority segments scheduled for earlier maintenance work. It is recommended that maintenance times be determined based on historical low traffic volumes for each segment; for example, urban roads should prioritize nighttime work, while highways should prioritize off-peak hours to minimize disruption to normal traffic. Estimated work duration is calculated based on the scale of the damage and the efficiency of the corresponding maintenance process. Different damage treatment processes correspond to different resource requirements, such as crack filling, pothole repair, and milling and repaving. The inclusion of secondary risk markers helps dispatchers coordinate the scope of work, addressing adjacent segments with potential cascading effects simultaneously, thus improving the efficiency of maintenance funds. The structured task list uses a standard data format and can directly interface with the existing operation and maintenance management system's work scheduling module, achieving seamless data transfer without requiring manual re-entry.

[0039] Please see Figure 3This invention also provides a road surface distress maintenance priority assessment system based on trend prediction, comprising: a road network lane modeling unit for establishing a road network lane calculation grid, dividing each lane into continuous calculation segments along the longitudinal direction, marking the lane affiliation and spatial location of each segment, and establishing a spatial mapping relationship between adjacent lanes and longitudinal segments; a distress avoidance trajectory simulation unit for simulating the lateral offset trajectory of vehicles passing through the distress area based on the lane and longitudinal segment location of the distress, and determining the proportion of vehicles triggering lane-changing behavior when the distress is within the wheel track zone; a load transfer distribution calculation unit for calculating the axle load increment of the affected segment of adjacent lanes based on the lane-changing vehicle proportion and the lateral offset trajectory, with the load increment decreasing segment by segment along the longitudinal direction away from the distress location; and secondary distresses. The prediction unit retrieves historical damage records and pavement service parameters of the affected sections of adjacent lanes, and predicts the new area of ​​secondary damage in the next maintenance cycle by combining the calculated load increment distribution. The traffic impact quantification unit calculates the impact of vehicle trajectory deviation on the traffic capacity of the road section based on the proportion of lane-changing vehicles and the total length of the affected section. The comprehensive damage equivalent aggregation unit summarizes the original damage values ​​of all defects in the road section, superimposes the secondary damage equivalent and traffic impact equivalent, and obtains the comprehensive damage equivalent of the road section by normalization and weighted summation. The maintenance priority generation unit standardizes the comprehensive damage equivalent and road section traffic flow and maintenance interval parameters, calculates the weighted summation, obtains the maintenance priority coefficient of each road section, and generates a maintenance task list by sorting the coefficients from high to low.

[0040] During system operation, the road network lane modeling unit first receives basic road network data, constructs the lane calculation grid, divides continuous calculation segments and generates spatial codes, establishes spatial mapping indexes for adjacent lanes, and outputs a gridded road network model, which is then synchronously transmitted to the defect avoidance trajectory simulation unit and the load transfer distribution calculation unit. The defect avoidance trajectory simulation unit receives defect location and morphology data obtained from road inspections, matches it to the corresponding calculation segments, simulates the lateral offset process of vehicles passing through defect areas based on a sinusoidal smooth transition model, determines the avoidance mode based on the lateral positional relationship between the defect and the wheel track, calculates the lane change trigger ratio and offset trajectory parameters, and transmits the results to the load transfer distribution calculation unit and the traffic impact quantification unit, respectively. The load transfer distribution calculation unit, based on the lane change ratio and offset trajectory data, calculates the axle load increment in each affected segment of adjacent lanes, performs attenuation calculations along the longitudinal direction, obtains the load increment distribution results for the entire road segment, and transmits them to the secondary defect prediction unit. The secondary damage prediction unit retrieves historical damage and pavement service data, incorporates load increment distribution, predicts the new area of ​​secondary damage in the next cycle, and outputs the secondary damage quantification results to the comprehensive damage equivalent aggregation unit. The traffic impact quantification unit calculates the speed reduction and traffic flow loss based on lane change parameters and impact range, converts them into a unified-dimensional traffic impact equivalent, and transmits it to the comprehensive damage equivalent aggregation unit. The comprehensive damage equivalent aggregation unit statistically analyzes the original damage values, pre-matches the original damage stock in the corresponding secondary damage sections and performs deduplication, then superimposes the secondary damage equivalent and traffic impact equivalent, completes normalization and weighted calculation, and outputs the comprehensive damage equivalent of the road segment to the maintenance priority generation unit. The maintenance priority generation unit incorporates traffic flow and maintenance interval parameters, performs standardization and weighted calculation to obtain the maintenance priority coefficient for each road segment, sorts them by coefficient, generates a structured maintenance task list, and pushes it to the external operation and maintenance management system.

[0041] In summary, this invention constructs lane-level gridded computing units and incorporates the lateral position dimension of the wheel track zone to simulate the lateral offset trajectory and lane-changing behavior of vehicles avoiding defects. It quantifies the longitudinal attenuation distribution law of axle load transfer to adjacent lanes, predicts the scale of secondary defects in adjacent lanes under additional loads, and converts the traffic efficiency loss caused by vehicle lane changes into a uniform equivalent index. Based on this, it aggregates three types of values: primary damage, secondary damage, and traffic impact, and superimposes traffic flow and maintenance interval parameters to calculate the maintenance priority coefficient of each road segment and generate a task list. This corrects the shortcomings of the original evaluation system, which only focuses on the area, quantity, and longitudinal dispersion of defects and does not consider the degree of overlap between defects and wheel tracks. It allows defects distributed in the wheel track zone area to obtain priority weights that match their actual damage impact, enabling treatment before the defects accelerate their expansion, controlling subsequent maintenance costs, reducing driving safety hazards, and identifying the risk of cascading damage in adjacent lanes in advance. It also coordinates the scope of work, improves the allocation efficiency of maintenance resources, and adapts to the daily operation and maintenance management needs of road networks of different sizes.

[0042] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0043] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for prioritizing pavement distress maintenance based on trend prediction, characterized in that, include: S1. Establish a road network lane calculation grid, divide each lane into continuous calculation segments along the longitudinal direction, mark the lane affiliation and spatial location of each segment, and establish the spatial mapping relationship between adjacent lanes and longitudinal segments. S2. Based on the lane and longitudinal section location of the defect, simulate the lateral offset trajectory of the vehicle when passing through the defect area, and determine the proportion of vehicles that trigger lane change behavior when the defect is within the wheel track area. S3. Based on the proportion of vehicles changing lanes and the lateral offset trajectory, calculate the axle load increment of the affected section of the adjacent lane. Along the longitudinal direction away from the location of the defect, the load increment gradually decreases. S4. Retrieve historical damage records and pavement service parameters of the affected sections of adjacent lanes, and combine them with the calculated load increment distribution to predict the new area of ​​secondary damage in the next maintenance cycle. S5. Calculate the impact of vehicle trajectory deviation on the road segment's traffic capacity based on the proportion of vehicles changing lanes and the total length of the affected section. S6. Summarize the original damage values ​​of all defects within the road section, add the equivalent of secondary damage and the equivalent of traffic impact, normalize and then sum the weighted values ​​to obtain the comprehensive damage equivalent of the road section. S7. After standardizing the comprehensive damage equivalent, road segment traffic flow, and maintenance interval parameters, a weighted calculation is performed to obtain the maintenance priority coefficient for each road segment. The maintenance task list is generated by sorting the coefficients from high to low.

2. The method for prioritizing pavement distress maintenance based on trend prediction as described in claim 1, characterized in that, The S1 step specifically includes: Import basic data on road grade, number of lanes, and lane width, and divide each lane into continuous calculation segments along the direction of travel at fixed lengths; Each calculation segment is assigned a unique spatial code, which includes the lane number and longitudinal sequence number; Mark the lateral position range of the standard wheel track for each lane. The wheel track is determined by shrinking inward at a fixed ratio based on the left and right boundaries of the lane. Establish the corresponding relationship between adjacent lanes on the left and right and longitudinal position segments to form a spatial mapping index.

3. The method for prioritizing pavement distress maintenance based on trend prediction as described in claim 1, characterized in that, The S2 step specifically includes: Determine whether the lateral coverage width of the defect exceeds the wheel track area. If it does, increase the vehicle's average lateral offset by the excess ratio. The average lateral offset and lane change trigger ratio for vehicle avoidance are determined based on the ratio of the lateral width of the defect to the width of the wheel track. The influence range is calculated by extending the length forward and backward along the longitudinal direction, and the longitudinal extension length of the affected section is determined by the severity level of the disease. Output the vehicle offset trajectory data and the range of the affected adjacent lane sections for each defect.

4. The method for prioritizing pavement distress maintenance based on trend prediction as described in claim 1, characterized in that, The S3 step specifically includes: The axle load frequency of the original standard wheel track is used as the benchmark value; The increase in the number of axle loads affected is calculated based on the proportion of vehicles changing lanes. The increase is equal to the baseline value multiplied by the proportion of vehicles changing lanes. Based on the lateral position distribution after vehicle offset, the additional axle load is distributed to the corresponding lateral area of ​​the adjacent lane; Along the longitudinal direction away from the location of the disease, the load increment decreases by a fixed attenuation ratio for each calculation section. After exceeding the preset number of affected sections, the load increment returns to zero.

5. The method for prioritizing pavement distress maintenance based on trend prediction as described in claim 1, characterized in that, The specific steps of step S4 include: Retrieve historical records of the increase in the area of ​​damage to adjacent lanes and data on the service life of the road surface; The natural growth rate of disease under the baseline load condition is extracted as the base value; The growth rate is calculated based on the ratio of the load increment to the reference load, and the growth rate is positively correlated with the load increment. The revised growth rate is obtained by multiplying the base growth rate by the growth multiplier. For sections where diseases already exist, the amplification coefficient resulting from the original disease density is added to obtain the final predicted area of ​​new secondary diseases.

6. The method for prioritizing pavement distress maintenance based on trend prediction as described in claim 1, characterized in that, The S5 step specifically includes: Retrieve the design speed and historical average traffic flow data for the road segment; Calculate the average speed reduction caused by lane-changing behavior based on the proportion of vehicles changing lanes and the total length of the affected section; The amount of traffic flow loss per unit time is used as a quantitative indicator of traffic efficiency. By combining the historical average traffic volume of the road segment, the traffic loss is converted into a traffic impact equivalent with uniform dimensions.

7. The method for prioritizing pavement distress maintenance based on trend prediction as described in claim 1, characterized in that, Step S6 specifically includes: The total area and total number of all defects within the road section are counted. Calculate the dispersion index of disease distribution in the longitudinal direction; The original damage value is obtained by multiplying the total area by the area weighting coefficient, the total quantity by the quantity weighting coefficient, and the longitudinal dispersion by the dispersion weighting coefficient. The primary damage value, secondary damage equivalent, and traffic impact equivalent are normalized to ensure that the three values ​​are within the same range.

8. The method for prioritizing pavement distress maintenance based on trend prediction as described in claim 1, characterized in that, In step S7, the comprehensive damage equivalent, road segment traffic flow, and maintenance interval time parameters are standardized and then weighted to obtain the maintenance priority coefficient for each road segment, including: The three parameters of comprehensive damage equivalent, average traffic flow, and time since last maintenance are standardized respectively. The weighting ratio of the three parameters is adjusted according to the road grade and pavement type; The maintenance priority coefficients for each road segment are obtained by multiplying the standardized parameter values ​​by their corresponding weighting coefficients and then summing them. For road sections with high secondary risks, mark the range of their transmission impact and the level of secondary risk.

9. The method for prioritizing pavement distress maintenance based on trend prediction as described in claim 1, characterized in that, In step S7, generating the maintenance task list includes: All road sections are sorted from highest to lowest according to their maintenance priority coefficient; For each road segment, indicate the suggested maintenance period, estimated operation time, and type of resource requirement; Simultaneously label the affected adjacent sections and the potential extent of road damage in secondary risk sections; Generate a structured task list and push it to the job scheduling module of the operation and maintenance management system.

10. A pavement distress maintenance priority assessment system based on trend prediction, used to execute the pavement distress maintenance priority assessment method based on trend prediction as described in any one of claims 1 to 9, characterized in that, include: The road network lane modeling unit is used to establish the road network lane calculation grid, divide each lane into continuous calculation segments along the longitudinal direction, mark the lane affiliation and spatial location of each segment, and establish the spatial mapping relationship between adjacent lanes and longitudinal segments. The defect avoidance trajectory simulation unit is used to simulate the lateral deviation trajectory of a vehicle when passing through the defect area based on the lane and longitudinal section location of the defect, and to determine the proportion of vehicles that trigger lane change behavior when the defect is within the wheel track area. The load transfer distribution calculation unit is used to calculate the axle load increment of the affected section of the adjacent lane based on the proportion of lane-changing vehicles and the lateral offset trajectory. The load increment gradually decreases along the longitudinal direction away from the location of the defect. The secondary disease prediction unit is used to retrieve historical disease records and pavement service parameters of the affected sections of adjacent lanes, and combine them with the calculated load increment distribution to predict the new area of ​​secondary diseases in the next maintenance cycle. The traffic impact quantification unit is used to calculate the degree of impact of vehicle trajectory deviation on the traffic capacity of the road segment based on the proportion of lane-changing vehicles and the total length of the affected section. The comprehensive damage equivalent aggregation unit is used to summarize the original damage values ​​of all defects in the road segment, add the secondary damage equivalent and traffic impact equivalent, and then normalize and weighted sum to obtain the comprehensive damage equivalent of the road segment. The maintenance priority generation unit is used to standardize and weight the comprehensive damage equivalent with road segment traffic flow and maintenance interval parameters to obtain the maintenance priority coefficient of each road segment, and generate a maintenance task list by sorting the coefficients from high to low.

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