A method and device for judging a highway maintenance construction organization mode

Abstract

The application provides a judgment method and device for a highway maintenance construction organization mode, relates to the technical field of traffic organization, and comprises the following steps: constructing a construction road section traffic bottleneck analysis model according to core parameters; solving the construction road section traffic bottleneck analysis model through a genetic algorithm to obtain single-lane traffic capacity; calculating a first ratio and a second ratio according to a road occupation construction period, a closed construction period, a target single-lane number, a target maintenance road occupation number, daily traffic volume and the single-lane traffic capacity; determining a construction organization mode type, a first ratio threshold and a per-kilometer daily traffic fee threshold according to the second ratio and a second ratio threshold; constructing a construction organization mode comparison table according to the target single-lane number, the target maintenance road occupation number, the per-kilometer daily traffic fee threshold, the first ratio threshold and the construction organization mode type; and selecting a final construction organization mode in the construction organization mode comparison table according to to-be-measured road section traffic data.

Description

Technical Field

[0001] This application relates to the field of traffic organization technology, and in particular to a method and apparatus for determining the organization mode of highway maintenance construction. Background Technology

[0002] The rational selection of highway maintenance and construction organization methods is key to ensuring road traffic efficiency and balancing maintenance and construction with road network operation needs during construction.

[0003] Traditional construction organization methods fail to comprehensively consider multi-dimensional factors such as traffic flow, vehicle type, and road alignment when calculating traffic capacity, resulting in inaccurate calculations of single-lane traffic capacity. The judgment of construction organization methods still relies mainly on manual experience, making it impossible to achieve unmanned quantitative judgment. Furthermore, the selection of construction organization methods does not consider toll costs, resulting in a single decision-making dimension and inaccurate selection of construction methods, which affects the effective implementation of highway maintenance projects. Summary of the Invention

[0004] To address the aforementioned problems, in a first aspect, the present invention provides a method for determining the organization method of highway maintenance construction, comprising:

[0005] Acquire maintenance and construction scenario data of the target highway, calibrate core parameters based on the maintenance and construction scenario data, construct a basic traffic flow graph model and a traffic capacity prediction model based on the core parameters, and establish a traffic bottleneck analysis model for the construction section based on the basic traffic flow graph model and the traffic capacity prediction model.

[0006] With the goal of minimizing the error between the estimated and actual cumulative congestion length, a genetic algorithm is used to solve the traffic bottleneck analysis model of the construction section to obtain the single-lane capacity.

[0007] Obtain the average daily traffic volume, the number of target one-way lanes, and the number of target lanes occupied for maintenance on the target expressway. Based on the number of target one-way lanes, the number of target lanes occupied for maintenance, the average daily traffic volume, and the single-lane capacity, determine congestion and calculate congestion duration to obtain the daily congestion duration for road construction.

[0008] Obtain the construction period for road occupancy and the construction period for road closure of the target highway. Use the ratio of the construction period for road occupancy to the construction period for road closure as the first ratio, and use the ratio of the daily congestion duration for road occupancy to the construction period for road closure as the second ratio.

[0009] Set a second ratio threshold, and determine the construction organization method type, the first ratio threshold, and the daily congestion duration threshold for road occupation construction based on the second ratio and the second ratio threshold. Determine the daily average traffic volume threshold based on the daily congestion duration threshold for road occupation construction.

[0010] Obtain the vehicle type composition ratio and toll standard of the target expressway, and calculate the daily average toll threshold per 100 kilometers based on the daily average traffic volume threshold, vehicle type composition ratio and toll standard.

[0011] A construction organization method comparison table is constructed based on the target number of one-way lanes, the target number of lanes occupied for maintenance, the daily average toll fee threshold per 100 kilometers, the first ratio threshold, and the construction organization method type. Traffic data of the road segment to be tested is obtained, and the final construction organization method is selected from the construction organization method comparison table based on the traffic data of the road segment to be tested.

[0012] In one embodiment:

[0013] The core parameters include free flow velocity, congestion density, model coefficients, shock wave propagation correction coefficients when congestion worsens, shock wave propagation correction coefficients when congestion dissipates, vehicle composition correction coefficients, and road alignment correction coefficients.

[0014] In one embodiment, the step of constructing a basic traffic flow graph model and a capacity prediction model based on core parameters includes:

[0015] Traffic flow density is used as the independent variable of the basic traffic flow graph model, and traffic flow is used as the dependent variable. Based on traffic flow, traffic flow density, free flow velocity, congestion density and model coefficients, a basic traffic flow graph model is constructed.

[0016] Traffic flow density, traffic volume, and cumulative congestion length are used as independent variables in the capacity prediction model, while capacity is used as the dependent variable.

[0017] Based on the unit time interval, the shock wave propagation correction coefficient when congestion intensifies, the shock wave propagation correction coefficient when congestion dissipates, the vehicle type composition correction coefficient, and the road alignment correction coefficient, combined with traffic flow density, traffic volume, and cumulative congestion length, a traffic capacity prediction model is constructed.

[0018] In one embodiment, the step of minimizing the error between the estimated and actual cumulative congestion length, and solving the traffic bottleneck analysis model of the construction section using a genetic algorithm to obtain the single-lane capacity, includes:

[0019] Obtain the historical single-lane capacity of the target highway at the construction bottleneck section, and use the historical single-lane capacity value as an individual to construct a population;

[0020] Obtain the historical traffic flow density and historical cumulative congestion length for all individuals;

[0021] By inputting historical traffic flow density into the traffic bottleneck analysis model of the construction section, the predicted cumulative congestion length and predicted traffic capacity are calculated.

[0022] The difference between the predicted cumulative congestion length and the historical cumulative congestion length is used as the error between the estimated and actual cumulative congestion length.

[0023] The individual with the smallest error is selected as the current best individual, and then the population is updated through crossover and mutation operations.

[0024] The population is iteratively updated to a preset number of iterations. The current best individual with the smallest error is taken as the final best individual, and the predicted traffic capacity corresponding to the final best individual is taken as the single-lane traffic capacity.

[0025] In one embodiment, the step of determining congestion and calculating congestion duration based on the target number of one-way lanes, the target number of lanes occupied for maintenance, the average daily traffic volume, and the single-lane capacity to obtain the daily congestion duration for road construction includes:

[0026] Based on the set average daily traffic volume and the traffic volume ratio of each time period, calculate the traffic volume of the construction bottleneck section at each hour.

[0027] Based on the target number of one-way lanes, the target number of lanes occupied for maintenance, and the single-lane capacity, the remaining lane capacity excluding road construction is calculated.

[0028] The period during which the hourly traffic volume of the bottleneck section of the construction is greater than the capacity of the remaining lanes excluding those occupied by construction is identified as a congested period. The congested periods are then summed up to obtain the daily congestion duration for the construction site.

[0029] In one embodiment, determining the construction organization method type, the first ratio threshold, and the daily congestion duration threshold for road construction based on the second ratio and the second ratio threshold, and determining the daily average traffic volume threshold based on the daily congestion duration threshold for road construction, includes:

[0030] A statistical table is constructed by using the first ratio for each group and the second ratio corresponding to the daily congestion duration of road construction. The rows of the statistical table are the first ratios, and the columns are the daily congestion duration of road construction.

[0031] All second ratios in the statistical table that are greater than or equal to the critical value of the second ratio are taken as candidate second ratios. The construction organization method type corresponding to the candidate second ratios is closed construction, which includes closed access and closed diversion.

[0032] All second ratios in the statistical table that are less than the critical value of the second ratio are taken as non-selectable second ratios. The construction organization method type corresponding to the non-selectable second ratios is road occupation construction.

[0033] The smallest candidate second ratio in each column of the statistics table is taken as the target second ratio, the first ratio corresponding to the target second ratio is taken as the first ratio threshold, and the daily congestion duration of road construction corresponding to the target second ratio is taken as the daily congestion duration threshold of road construction.

[0034] The average daily traffic volume corresponding to the daily congestion duration threshold for road construction will be used as the average daily traffic volume threshold.

[0035] In one embodiment, selecting the final construction organization method from the construction organization method comparison table based on the traffic data of the road segment to be tested includes:

[0036] Obtain the following data from the traffic data of the road segment to be tested: number of one-way lanes, number of lanes occupied by maintenance, average daily toll per 100 kilometers, and first ratio of the road segment to be tested;

[0037] In the construction organization method comparison table, the number of one-way lanes of the test section is matched with the target number of one-way lanes, the number of maintenance lanes occupied by the test section is matched with the target number of maintenance lanes, the average daily toll per 100 kilometers of the test section is ≤ the threshold of the average daily toll per 100 kilometers, and the first ratio of the test section is ≤ the threshold of the first ratio. The corresponding construction organization method type is taken as the final construction organization method.

[0038] Secondly, the present invention provides a device for determining the organization mode of highway maintenance construction, used to implement the method for determining the organization mode of highway maintenance construction, the device comprising:

[0039] The traffic bottleneck analysis model construction module for construction sections is used to acquire maintenance and construction scenario data of the target highway, calibrate core parameters based on the maintenance and construction scenario data, construct a basic traffic flow graph model and a capacity prediction model based on the core parameters, and establish a traffic bottleneck analysis model for construction sections based on the basic traffic flow graph model and the capacity prediction model.

[0040] The single-lane capacity acquisition module is used to obtain the single-lane capacity by solving the traffic bottleneck analysis model of the construction section through a genetic algorithm with the goal of minimizing the error between the estimated and actual values ​​of the cumulative congestion length.

[0041] The module for obtaining daily congestion duration during road construction is used to obtain the average daily traffic volume of the target highway, the number of target one-way lanes, and the number of target maintenance lanes. Based on the number of target one-way lanes, the number of target maintenance lanes, the average daily traffic volume, and the single-lane capacity, it performs congestion judgment and congestion duration calculation to obtain the daily congestion duration during road construction.

[0042] The ratio acquisition module is used to obtain the construction period of road occupancy and the construction period of road closure of the target highway. The ratio of the construction period of road occupancy to the construction period of road closure is used as the first ratio, and the ratio of the daily congestion time of road occupancy to the construction period of road closure is used as the second ratio.

[0043] The first threshold acquisition module is used to set the second ratio threshold, determine the construction organization method type, the first ratio threshold, and the daily congestion duration threshold for road occupation construction based on the second ratio and the second ratio threshold, and determine the daily average traffic volume threshold based on the daily congestion duration threshold for road occupation construction.

[0044] The second threshold acquisition module is used to acquire the vehicle composition ratio and highway toll standard of the target highway, and calculate the daily average toll threshold per 100 kilometers based on the daily average traffic volume threshold, vehicle composition ratio and highway toll standard.

[0045] The final construction organization method acquisition module is used to construct a construction organization method comparison table based on the target number of one-way lanes, the target number of lanes occupied for maintenance, the daily average toll fee threshold per 100 kilometers, the first ratio threshold, and the construction organization method type. It then obtains traffic data for the road segment to be tested and selects the final construction organization method from the construction organization method comparison table based on the traffic data of the road segment to be tested.

[0046] Thirdly, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for determining the highway maintenance construction organization method.

[0047] Fourthly, the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method for determining the organization mode of highway maintenance construction.

[0048] The present invention has the following beneficial effects:

[0049] 1. By calibrating multi-dimensional core parameters including traffic flow, vehicle type, and road alignment, a series of models such as basic traffic flow maps and capacity prediction are constructed. Combined with genetic algorithms, the capacity of a single lane is accurately calculated, transforming the impact of maintenance construction on traffic into quantitative calculations, providing high-precision single-lane capacity for construction organization decisions. By defining quantitative thresholds such as the first ratio, the second ratio, the daily congestion duration of road construction, the average daily traffic volume, and the average daily toll per 100 kilometers, and combining the critical value of the second ratio to classify construction organization methods and construct a standardized comparison table, construction organization decisions can take into account both traffic efficiency and road construction efficiency, realizing the quantitative selection of construction methods and improving the accuracy of construction method selection.

[0050] 2. This invention defines a first ratio and a second ratio, sets quantitative thresholds such as daily congestion duration, average daily traffic volume, and average daily toll per 100 kilometers for road construction, and classifies construction organization methods by combining the critical value of the second ratio. It also constructs a standardized comparison table of construction organization methods, establishing a quantifiable, replicable, and scalable judgment method. By simply matching the core data such as the number of one-way lanes, the number of lanes occupied for maintenance, the toll, and the first ratio of the road segment to be tested, the optimal solution can be quickly selected. This solves the problems of subjectivity and differentiation in traditional decision-making and improves the accuracy and rationality of construction method selection. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 This is a flowchart of a method according to an embodiment of the present invention;

[0053] Figure 2 This is a structural diagram of the device according to an embodiment of the present invention. Detailed Implementation

[0054] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0055] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to and includes any or all possible combinations of one or more of the listed items.

[0056] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0057] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0058] Existing construction organization methods often rely on engineers' subjective experience to select solutions, failing to establish quantitative judgment models that directly correlate with key factors such as dynamic traffic flow, specific construction techniques and durations, and potential toll losses. This qualitative decision-making model is prone to inaccurate solution selection: either being overly conservative, readily adopting a full-line closure and diversion scheme to avoid congestion risks, resulting in large-scale vehicle detours, causing unnecessary toll losses and pressure on the regional road network; or being overly aggressive, forcibly implementing long-term road closures for construction on sections with near-saturation traffic flow, causing severe and persistent traffic congestion, which ultimately harms overall efficiency.

[0059] The capacity of traffic bottlenecks created by road construction is a core parameter for predicting the duration and extent of congestion. Traditional methods typically use fixed empirical values, such as simply assuming a single-lane capacity of 500 standard vehicles per hour under construction conditions, failing to accurately calibrate based on specific road alignment, traffic flow composition, and historical construction congestion data. Because the values ​​of key parameters deviate significantly from actual operating conditions, predictions of the duration and severity of congestion potentially caused by road construction are severely distorted, failing to provide a reliable basis for alternative solution selection.

[0060] Effective decision-making requires a comprehensive consideration of multiple factors, including traffic flow thresholds, toll economic thresholds, and construction efficiency thresholds. Traditional methods often view these issues in isolation; for example, they fail to distinguish that the optimal solutions for high-cost (e.g., high truck proportion) and low-cost road sections should differ under the same traffic flow levels. This one-sided analysis cannot minimize overall social costs.

[0061] Therefore, there is an urgent need to develop a quantitative decision-making method that can integrate precise traffic flow analysis, key parameter calibration, and multi-threshold collaborative optimization calculation to support the scientific selection of traffic organization schemes for highway maintenance. This will enable the method to minimize social travel costs and reduce toll revenue losses while ensuring construction safety and progress, and improve the overall operational efficiency of the road network.

[0062] Reference Figure 1 This invention provides a method for determining the construction organization method of highway maintenance, including:

[0063] S1 acquires maintenance and construction scenario data of the target highway, calibrates core parameters based on the maintenance and construction scenario data, constructs a basic traffic flow graph model and a traffic capacity prediction model based on the core parameters, and establishes a traffic bottleneck analysis model for the construction section based on the basic traffic flow graph model and the traffic capacity prediction model.

[0064] In some embodiments, the core parameters include free-flow velocity, congestion density, model coefficients, shock wave propagation correction coefficients for increased congestion, shock wave propagation correction coefficients for dissipated congestion, vehicle composition correction coefficients, and road alignment correction coefficients.

[0065] In some embodiments, free flow velocity The calibration is based on the design speed of the target expressway, the road section grade, and the actual traffic data during off-peak hours before construction without traffic interference. During the measurement, an uninterrupted section more than 500m upstream and downstream of the construction section is selected, and the vehicle speed during off-peak hours is collected. Low-speed abnormal values ​​such as large trucks and broken-down vehicles are removed, and the 85th percentile value of the vehicle speed is taken as the calibration value.

[0066] Blocking density The unit is pcu / km. The calibration is based on the lane width, vehicle type composition, and minimum safe distance between vehicles on the target highway. It is the critical density where the flow rate is 0 in the basic traffic flow diagram model. During the measurement, the number of vehicles in a single lane under congested conditions in the construction section is collected. After converting various vehicle types into standard passenger car equivalents, the number of vehicles in a unit length lane is calculated. The conventional value for the one-way single-lane congestion density of highways is 120-180 pcu / km. If the lane width is less than 3.5m and the proportion of trucks exceeds 50%, the lower value of 120-140 pcu / km is used. If the lane width is the standard value of 3.75m and the proportion of small cars exceeds 80%, the higher value of 160-180 pcu / km is used.

[0067] Model coefficients It is the core fitting parameter of the basic traffic flow graph model. It has no fixed value range and is calibrated based on the historical traffic flow measurement data of the target road segment. The expression of the basic traffic flow graph model is fitted by nonlinear least squares method, and the value of the model coefficient is iteratively optimized to minimize the error between the traffic flow value calculated by the model and the actual measured flow value on site. The final value is determined to adapt to the actual traffic flow characteristics of the target road segment and achieve accurate mapping between density and flow.

[0068] Shock wave propagation correction factor during congestion exacerbation The core function is to correct the shock wave propagation speed during the congestion diffusion stage of bottleneck sections during maintenance and construction. The calibration is based on the traffic flow merging characteristics of the construction section, with a value range of 1.0-1.2. In practice, a value of 1.0 is used for pure construction bottleneck sections without additional traffic merging. In cases where there are continuous merging from ramps and multiple lanes merging into the remaining lanes for construction, a value of 1.1-1.2 is used depending on the scale of the merging traffic. The larger the merging traffic and the more frequent the merging, the closer the value is to 1.2. On-site measurement is required in conjunction with the traffic organization plan of the construction section.

[0069] Shock wave propagation correction factor during congestion dissipation The core function is to correct the shock wave propagation speed during the congestion relief phase of bottleneck sections during maintenance and construction. The calibration is based on the discrete exit characteristics of traffic flow in the construction section and the recovery of traffic efficiency in the bottleneck section, with a value range of 0.7-0.9. In practice, a value of 0.9 is used when there is no interference when exiting the bottleneck section and the traffic flow is orderly and discrete. If there is lane switching interference, a high proportion of trucks resulting in slow exit speed, or construction occupying the road near the highway exit, a value of 0.7-0.8 is used. The stronger the discreteness of the traffic flow, the lower the value.

[0070] Vehicle composition correction factor The core function is to correct the impact of mixed traffic of different vehicle types on the traffic capacity of construction sections. The calibration basis is the measured proportion of vehicle types on the target highway; the value rule is that η=1 when the proportion of small cars is 100%, and η increases by 10% for every 10% increase in the proportion of trucks. Reduce by 0.05~0.08. If there are extra-large trucks or trailers, the reduction can be increased appropriately.

[0071] Road alignment correction factor The core function is to correct the impact of road alignment on vehicle speed and traffic capacity. The calibration is based on the actual road alignment of the target maintenance construction section; the value selection rule is that straight sections have no curves and no slopes. =1; For curved sections, the value is taken based on the curve radius: 0.7-0.8 for small radius curves (radius < 250m), 0.8-0.9 for medium radius curves (250m ≤ radius < 600m), and 0.9-0.95 for large radius curves (radius ≥ 600m). The longer the curve, the lower the value. For slope sections, the value is taken based on the longitudinal slope: 0.6-0.7 for steep slopes (longitudinal slope > 3%), and 0.7-0.85 for gentle slopes (1% ≤ longitudinal slope ≤ 3%). The value for uphill sections is lower than that for downhill sections with the same slope. If it is a combined curved and slope section, the product of the correction coefficients for the curved section and the slope section is taken as the final value.

[0072] In some embodiments, constructing the basic traffic flow graph model and the capacity prediction model based on the core parameters includes:

[0073] Traffic flow density is used as the independent variable of the basic traffic flow graph model, and traffic flow is used as the dependent variable. Based on traffic flow, traffic flow density, free flow velocity, congestion density and model coefficients, a basic traffic flow graph model is constructed.

[0074] Traffic flow density, traffic volume, and cumulative congestion length are used as independent variables in the capacity prediction model, while capacity is used as the dependent variable.

[0075] Based on the unit time interval, the shock wave propagation correction coefficient when congestion intensifies, the shock wave propagation correction coefficient when congestion dissipates, the vehicle type composition correction coefficient, and the road alignment correction coefficient, combined with traffic flow density, traffic volume, and cumulative congestion length, a traffic capacity prediction model is constructed.

[0076] In some embodiments, the expression for the basic traffic flow graph model Q(k) is:

[0077] ,

[0078] in, For free flow velocity; Blocking density; is the model coefficient; k is the traffic flow density; q is the traffic flow.

[0079] The expression for the capability prediction model is:

[0080] ,

[0081] ,

[0082] in, The time interval is set to 1 hour. For the first Hourly congestion length change value, positive value indicates congestion increase, negative value indicates congestion dissipation; For the first Hourly cumulative congestion length; For the first The equivalent flow rate of standard passenger cars per lane per hour upstream of the bottleneck section, in pcu / h; For traffic capacity; , They are respectively , The corresponding traffic flow density is expressed in pcu / km; This is a shock wave propagation correction factor for increased congestion, ranging from 1.0 to 1.2, with a larger value used when traffic flows continuously merge. This is the shock wave propagation correction factor when congestion dissipates, with a value of 0.7 to 0.9, and a smaller value is used when the traffic flow disperses and exits. This is a correction factor for vehicle type composition, when small cars account for 100%. For every 10% increase in the proportion of trucks... Reduced by 0.05~0.08; This is the road alignment correction factor for straight sections. curve segment ramp section ; Design single-lane capacity for bottleneck sections.

[0083] S2 aims to minimize the error between the estimated and actual cumulative congestion length. It uses a genetic algorithm to solve the traffic bottleneck analysis model of the construction section and obtain the single-lane capacity.

[0084] In some embodiments, the step of minimizing the error between the estimated and actual cumulative congestion length, and solving the traffic bottleneck analysis model of the construction section using a genetic algorithm to obtain the single-lane capacity, includes:

[0085] Obtain the historical single-lane capacity of the target highway at the construction bottleneck section, and use the historical single-lane capacity value as an individual to construct a population;

[0086] Obtain the historical traffic flow density and historical cumulative congestion length for all individuals;

[0087] By inputting historical traffic flow density into the traffic bottleneck analysis model of the construction section, the predicted cumulative congestion length and predicted traffic capacity are calculated.

[0088] The difference between the predicted cumulative congestion length and the historical cumulative congestion length is used as the error between the estimated and actual cumulative congestion length.

[0089] The individual with the smallest error is selected as the current best individual, and then the population is updated through crossover and mutation operations.

[0090] The population is iteratively updated to a preset number of iterations. The current best individual with the smallest error is taken as the final best individual, and the predicted traffic capacity corresponding to the final best individual is taken as the single-lane traffic capacity.

[0091] In some embodiments, historical single-lane capacity measurement data under the same road conditions and traffic characteristics for the past 1-3 years are first collected for the bottleneck section of the target highway construction. Each historical single-lane capacity measurement value is used as a single individual in the genetic algorithm, and the range of individual values ​​is set to 800-2200 pcu / h in combination with the conventional highway capacity. An initial population is constructed by randomly selecting historical measurement values ​​with a population size of 50-100 individuals to ensure that the population individuals cover the capacity values ​​under different traffic flow conditions and avoid deviations in iteration results due to single values.

[0092] Based on the historical single-lane capacity of each individual in the initial population, historical traffic flow measurement data of the same time and the same road segment are retrieved to accurately match the historical traffic flow density and historical cumulative congestion length of each individual.

[0093] Historical traffic flow density, combined with previously calibrated free-flow velocity, congestion density, and various correction coefficients, is uniformly input into the traffic bottleneck analysis model for the construction section. According to the model's built-in traffic flow basic diagram formula and capacity prediction formula, the historical traffic flow density corresponding to each individual is substituted one by one to calculate the predicted cumulative congestion length and predicted capacity for each individual.

[0094] Using absolute error as a quantitative indicator, the difference between the predicted cumulative congestion length and the historical cumulative congestion length for each individual is calculated. The smaller the difference, the closer the model calculation result for that individual is to the actual traffic conditions. When there is no error, the value is 0.

[0095] Iterate through the error values ​​of all individuals in the initial population, determine the individual with the smallest error value as the current best individual, and record its corresponding predicted passage ability;

[0096] A single-point crossover method is used to update individuals within the population. The crossover probability is set to 0.7-0.9. Two different individuals within the population are randomly selected, and some genes are exchanged at a preset crossover position to generate two new individuals, which replace the inefficient individuals in the original population.

[0097] The new population after crossover was mutated using a random mutation method with a mutation probability set at 0.01-0.05. The mobility values ​​of some individuals were randomly selected and adjusted within a range of ±50 pcu / h to generate mutated individuals, which were then added to the population to complete one population iteration update.

[0098] The genetic algorithm is set to iterate 50-100 times. The above process is repeated to update the population iteratively. After each iteration, the current best individual and its corresponding error value are recorded. When the number of iterations reaches the preset value, the iteration is terminated. The final best individual with the smallest error value in all iterations is selected, and the predicted traffic capacity corresponding to this individual is used as the calibration value of the single-lane traffic capacity of the bottleneck section of the target highway construction.

[0099] S3 obtains the target highway's average daily traffic volume, target number of one-way lanes, and target number of lanes occupied for maintenance. Based on the target number of one-way lanes, target number of lanes occupied for maintenance, average daily traffic volume, and single-lane capacity, it performs congestion assessment and calculates congestion duration to obtain the daily congestion duration for road construction.

[0100] In some embodiments, the step of determining congestion and calculating congestion duration based on the target number of one-way lanes, the target number of lanes occupied for maintenance, the average daily traffic volume, and the single-lane capacity to obtain the daily congestion duration for road construction includes:

[0101] Based on the set average daily traffic volume and the traffic volume ratio of each time period, calculate the traffic volume of the construction bottleneck section at each hour.

[0102] Based on the target number of one-way lanes, the target number of lanes occupied for maintenance, and the single-lane capacity, the remaining lane capacity excluding road construction is calculated.

[0103] The period during which the hourly traffic volume of the bottleneck section of the construction is greater than the capacity of the remaining lanes excluding those occupied by construction is identified as a congested period. The congested periods are then summed up to obtain the daily congestion duration for the construction site.

[0104] In some embodiments, based on experience, the average daily traffic volume is first set to 5000, 6000, ..., 17000 pcu / d. The traffic volume ratios for different time periods of the target highway are shown in Table 1. The traffic volume ratios for each time period are multiplied by the corresponding time period of the average daily traffic volume to obtain the hourly traffic volume of the construction bottleneck section.

[0105] Table 1. Traffic Flow Ratio for Different Time Periods

[0106]

[0107] The difference between the target number of one-way lanes and the target number of lanes occupied for maintenance is taken as the number of remaining lanes. The remaining number of lanes is multiplied by the single-lane capacity to obtain the capacity of the remaining lanes excluding road construction. According to the above fitting results, the capacity of the remaining lanes excluding road construction is 600 pcu / h. The hourly congestion period is determined, and the daily congestion duration of road construction under different daily average traffic volume is calculated, as shown in Table 2.

[0108] Table 2. Daily Congestion Duration Due to Road Construction Under Different Average Daily Traffic Volumes

[0109]

[0110] S4 obtains the construction period for road occupancy and the construction period for road closure of the target expressway, and uses the ratio of the construction period for road occupancy to the construction period for road closure as the first ratio, and the ratio of the daily congestion duration for road occupancy to the construction period for road closure as the second ratio.

[0111] In some embodiments, two types of construction period raw data are extracted from the target highway construction filing documents, traffic management department construction permit files, and on-site construction logs; the road occupation construction period refers only to the effective construction time of the road section that occupies lanes but is not completely closed during the construction period, such as half-width road occupation or single-lane road occupation construction; the road closure construction period refers to the effective construction time of the target road section that is completely closed during the construction period.

[0112] S5 sets a second ratio threshold, and determines the construction organization method type, the first ratio threshold, and the daily congestion duration threshold for road occupation construction based on the second ratio and the second ratio threshold. It also determines the daily average traffic volume threshold based on the daily congestion duration threshold for road occupation construction.

[0113] In some embodiments, determining the construction organization method type, the first ratio threshold, and the daily congestion duration threshold for road construction based on the second ratio and the second ratio threshold, and determining the daily average traffic volume threshold based on the daily congestion duration threshold for road construction, includes:

[0114] A statistical table is constructed by using the first ratio for each group and the second ratio corresponding to the daily congestion duration of road construction. The rows of the statistical table are the first ratios, and the columns are the daily congestion duration of road construction.

[0115] All second ratios in the statistical table that are greater than or equal to the critical value of the second ratio are taken as candidate second ratios. The construction organization method type corresponding to the candidate second ratios is closed construction, which includes closed access and closed diversion.

[0116] All second ratios in the statistical table that are less than the critical value of the second ratio are taken as non-selectable second ratios. The construction organization method type corresponding to the non-selectable second ratios is road occupation construction.

[0117] The smallest candidate second ratio in each column of the statistics table is taken as the target second ratio, the first ratio corresponding to the target second ratio is taken as the first ratio threshold, and the daily congestion duration of road construction corresponding to the target second ratio is taken as the daily congestion duration threshold of road construction.

[0118] The average daily traffic volume corresponding to the daily congestion duration threshold for road construction will be used as the average daily traffic volume threshold.

[0119] In some embodiments, a statistical table of the first ratio and the second ratio corresponding to the daily congestion duration of road construction is shown in Table 3.

[0120] Table 3. Statistical table of the second ratio

[0121]

[0122] The second ratio threshold is set to 0.5, and the formula for determining the type of construction organization method is:

[0123]

[0124] Where 0 represents road occupancy construction, and non-zero values ​​represent road closure construction; 'a' is the first ratio; and 'T' is the daily congestion duration due to road occupancy construction. This is the critical value for the second ratio.

[0125] As shown in Table 3, the portion of the second ratio that is greater than or equal to the critical value of 0.5 is considered a candidate second ratio. If it is greater than 0.5, it indicates that the traffic congestion caused by the traditional road occupation construction method is severe, and road occupation construction should be abandoned. The construction organization method type should prioritize closed construction schemes, including closed access or closed diversion. The portion of the second ratio that is less than the critical value of 0.5 is not a candidate second ratio, and the construction organization method type should prioritize road occupation construction schemes.

[0126] Road occupancy construction refers to the occupation of only a portion of the lanes required for maintenance during construction, while the remaining lanes remain open as usual. Vehicles do not need to change their original routes and can travel along the original unclosed lanes. Road closure and borrowing refers to the closure of all lanes in the direction of the construction section, with temporary traffic control measures used to borrow lanes in the opposite direction, forming a two-way single-lane or multi-lane traffic pattern. Vehicles do not need to leave the highway to detour. Road closure and diversion refers to the complete closure of all lanes in the construction section, prohibiting vehicles from traveling on that section, and guiding vehicles to exit from the upstream highway exit and detour to their destination via national and provincial highways, other highways, and other external road networks.

[0127] It can be seen that when the daily congestion duration for road construction is less than 2 hours, the overall congestion is relatively mild when using the traditional road construction method, and road construction can be used regardless of the ratio of the road construction period to the closure period. When the daily congestion duration for road construction is 8 hours, the overall congestion is severe when using the traditional road construction method, and the closure and diversion construction method must be used regardless of the ratio of the road construction period to the closure period. Correspondingly, the selected daily congestion duration thresholds for road construction are, for example, 3 hours below a first ratio of 4, 5 hours below a first ratio of 2.5, and 6 hours below a first ratio of 2.

[0128] Select the daily average traffic volume corresponding to the daily congestion duration threshold for road construction, such as 9000, 10000, and 11000 in Table 2, as the daily average traffic volume threshold.

[0129] S6 obtains the vehicle type composition ratio and highway toll standard of the target highway, and calculates the daily average toll threshold per 100 kilometers based on the daily average traffic volume threshold, vehicle type composition ratio and highway toll standard.

[0130] In some embodiments, the passenger and freight vehicle traffic volumes are deduced based on the vehicle type composition ratio. The passenger and freight vehicle traffic volumes are then multiplied by the highway toll standard to obtain the average daily toll per 100 kilometers. Table 4 shows the average daily toll per 100 kilometers for passenger vehicle 1, passenger vehicle 2, passenger vehicle 3, passenger vehicle 4, freight vehicle 1, freight vehicle 2, freight vehicle 3, freight vehicle 4, freight vehicle 5, and freight vehicle 6. By rounding down the average daily toll per 100 kilometers corresponding to the daily traffic volume thresholds of 9,000, 10,000, and 11,000, the corresponding average daily toll per 100 kilometers thresholds are obtained as 400,000, 450,000, and 500,000, respectively.

[0131] Table 4. Average daily toll per 100 kilometers (10,000 yuan)

[0132]

[0133] S7 constructs a construction organization method comparison table based on the target number of one-way lanes, the target number of lanes occupied for maintenance, the daily average toll fee threshold per 100 kilometers, the first ratio threshold, and the construction organization method type. It then obtains traffic data for the road segment to be tested and selects the final construction organization method from the construction organization method comparison table based on the traffic data of the road segment to be tested.

[0134] In some embodiments, selecting the final construction organization method from the construction organization method comparison table based on the traffic data of the road segment to be tested includes:

[0135] Obtain the following data from the traffic data of the road segment to be tested: number of one-way lanes, number of lanes occupied by maintenance, average daily toll per 100 kilometers, and first ratio of the road segment to be tested;

[0136] In the construction organization method comparison table, the number of one-way lanes of the test section is matched with the target number of one-way lanes, the number of maintenance lanes occupied by the test section is matched with the target number of maintenance lanes, the average daily toll per 100 kilometers of the test section is ≤ the threshold of the average daily toll per 100 kilometers, and the first ratio of the test section is ≤ the threshold of the first ratio. The corresponding construction organization method type is taken as the final construction organization method.

[0137] In some embodiments, the comparison table of construction organization methods is shown in Table 5.

[0138] Table 5 Comparison of Construction Organization Methods

[0139]

[0140] Finally, based on Table 5, the optimal final construction organization method is selected according to the four core parameters in the traffic data of the road section to be tested: the number of one-way lanes in the road section to be tested, the number of lanes occupied by maintenance in the road section to be tested, the average daily toll per 100 kilometers in the road section to be tested, and the first ratio of the road section to be tested.

[0141] Reference Figure 2 This invention provides a device 20 for determining the organization method of highway maintenance construction, used to implement a method for determining the organization method of highway maintenance construction. The device includes:

[0142] The construction module 21 for the traffic bottleneck analysis model of the construction section is used to obtain maintenance and construction scenario data of the target highway, calibrate the core parameters based on the maintenance and construction scenario data, construct the basic traffic flow graph model and the capacity prediction model based on the core parameters, and establish the traffic bottleneck analysis model of the construction section based on the basic traffic flow graph model and the capacity prediction model.

[0143] The single-lane capacity acquisition module 22 is used to solve the traffic bottleneck analysis model of the construction section through a genetic algorithm with the goal of minimizing the error between the estimated value and the actual value of the cumulative congestion length, so as to obtain the single-lane capacity.

[0144] The module 23 for obtaining the daily congestion duration of road construction is used to obtain the average daily traffic volume of the target expressway, the number of target one-way lanes and the number of target maintenance lanes. Based on the number of target one-way lanes, the number of target maintenance lanes, the average daily traffic volume and the single-lane capacity, it makes congestion judgment and calculates the congestion duration to obtain the daily congestion duration of road construction.

[0145] The ratio acquisition module 24 is used to acquire the construction period of road occupation and the construction period of road closure of the target expressway, and to take the ratio of the construction period of road occupation to the construction period of road closure as the first ratio, and the ratio of the daily congestion time of road occupation to the construction period of road closure as the second ratio.

[0146] The first threshold acquisition module 25 is used to set the second ratio threshold, determine the construction organization method type, the first ratio threshold and the daily congestion duration threshold for road occupation construction based on the second ratio and the second ratio threshold, and determine the daily average traffic volume threshold based on the daily congestion duration threshold for road occupation construction.

[0147] The second threshold acquisition module 26 is used to acquire the vehicle composition ratio and highway toll standard of the target highway, and calculate the daily average toll threshold per 100 kilometers based on the daily average traffic volume threshold, vehicle composition ratio and highway toll standard.

[0148] The final construction organization method acquisition module 27 is used to construct a construction organization method comparison table based on the target number of one-way lanes, the target number of lanes occupied for maintenance, the daily average toll fee threshold per 100 kilometers, the first ratio threshold, and the construction organization method type, to obtain traffic data of the road segment to be tested, and to select the final construction organization method from the construction organization method comparison table based on the traffic data of the road segment to be tested.

[0149] This application provides an electronic device, including a processor and a memory; the memory stores a computer program, wherein the computer program, when executed by the processor, implements a method for determining the highway maintenance construction organization method of any of the above schemes.

[0150] Specifically, the processor may include, for example, a general-purpose microprocessor, an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor may also include onboard memory for caching purposes. The processor may be a single processing unit or multiple processing units for performing different actions of the method flow according to embodiments of this application.

[0151] Memory can be any medium capable of containing, storing, transmitting, propagating, or transmitting instructions. For example, memory can include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, instruments, or propagation media. Specific examples of memory include: magnetic storage devices such as magnetic tape or hard disk drives (HDDs); optical storage devices such as optical discs (CD-ROMs); and also random access memory (RAM) or flash memory; and / or wired / wireless communication links.

[0152] This application also provides a computer-readable medium storing a computer program thereon, which, when executed by a processor, implements a method for determining the highway maintenance construction organization method of any of the above-described schemes. This computer-readable medium may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The aforementioned computer-readable medium carries one or more programs, which, when executed, implement the methods described in the embodiments of this application.

[0153] According to embodiments of this application, a computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wired, optical fiber, radio frequency signals, etc., or any suitable combination thereof.

[0154] Those skilled in the art will understand that the features described in the various embodiments and / or claims of this application can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in this application. In particular, the features described in the various embodiments and / or claims of this application can be combined and / or combined in various ways without departing from the spirit and teachings of this application. All such combinations and / or combinations fall within the scope of this application. Therefore, the scope of this application should not be limited to the above embodiments, but should be defined not only by the appended claims, but also by their equivalents. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for determining the construction organization mode of highway maintenance, characterized in that, include: Acquire maintenance and construction scenario data of the target highway, and calibrate core parameters based on the maintenance and construction scenario data. The core parameters include free flow velocity, congestion density, model coefficient, shock wave propagation correction coefficient when congestion intensifies, shock wave propagation correction coefficient when congestion dissipates, vehicle composition correction coefficient, and road alignment correction coefficient. Traffic flow density is used as the independent variable of the basic traffic flow graph model, and traffic flow is used as the dependent variable. Based on traffic flow, traffic flow density, free flow velocity, congestion density and model coefficients, a basic traffic flow graph model is constructed. Traffic flow density, traffic volume, and cumulative congestion length are used as independent variables in the capacity prediction model, while capacity is used as the dependent variable. Based on the unit time interval, the shock wave propagation correction coefficient when congestion intensifies, the shock wave propagation correction coefficient when congestion dissipates, the vehicle type composition correction coefficient, and the road alignment correction coefficient, combined with traffic flow density, traffic volume, and cumulative congestion length, a capacity prediction model is constructed. Based on the basic traffic flow graph model and the traffic capacity prediction model, a traffic bottleneck analysis model for construction sections is established. With the goal of minimizing the error between the estimated and actual cumulative congestion length, a genetic algorithm is used to solve the traffic bottleneck analysis model of the construction section to obtain the single-lane capacity. Obtain the average daily traffic volume, the number of target one-way lanes, and the number of target lanes occupied for maintenance on the target expressway. Based on the number of target one-way lanes, the number of target lanes occupied for maintenance, the average daily traffic volume, and the single-lane capacity, determine congestion and calculate congestion duration to obtain the daily congestion duration for road construction. Obtain the construction period for road occupancy and the construction period for road closure of the target highway. Use the ratio of the construction period for road occupancy to the construction period for road closure as the first ratio, and use the ratio of the daily congestion duration for road occupancy to the construction period for road closure as the second ratio. Set a second ratio threshold, and construct a statistical table by using the first ratio and the second ratio corresponding to the daily congestion duration of road construction for each group. The rows of the statistical table are the first ratios, and the columns of the statistical table are the daily congestion duration of road construction. All second ratios in the statistical table that are greater than or equal to the critical value of the second ratio are taken as candidate second ratios. The construction organization method type corresponding to the candidate second ratios is closed construction, which includes closed access and closed diversion. All second ratios in the statistical table that are less than the critical value of the second ratio are taken as non-selectable second ratios. The construction organization method type corresponding to the non-selectable second ratios is road occupation construction. The smallest candidate second ratio in each column of the statistics table is taken as the target second ratio, the first ratio corresponding to the target second ratio is taken as the first ratio threshold, and the daily congestion duration of road construction corresponding to the target second ratio is taken as the daily congestion duration threshold of road construction. The average daily traffic volume corresponding to the daily congestion duration threshold for road construction will be used as the average daily traffic volume threshold. Obtain the vehicle type composition ratio and toll standard of the target expressway, and calculate the daily average toll threshold per 100 kilometers based on the daily average traffic volume threshold, vehicle type composition ratio and toll standard. A construction organization method comparison table is constructed based on the target number of one-way lanes, the target number of lanes occupied for maintenance, the daily average toll fee threshold per 100 kilometers, the first ratio threshold, and the construction organization method type. Traffic data of the road segment to be tested is obtained, and the final construction organization method is selected from the construction organization method comparison table based on the traffic data of the road segment to be tested.

2. The method for determining the construction organization method for highway maintenance according to claim 1, characterized in that, The objective is to minimize the error between the estimated and actual cumulative congestion length. A genetic algorithm is used to solve the traffic bottleneck analysis model of the construction section to obtain the single-lane capacity, including: Obtain the historical single-lane capacity of the target highway at the construction bottleneck section, and use the historical single-lane capacity value as an individual to construct a population; Obtain the historical traffic flow density and historical cumulative congestion length for all individuals; By inputting historical traffic flow density into the traffic bottleneck analysis model of the construction section, the predicted cumulative congestion length and predicted traffic capacity are calculated. The difference between the predicted cumulative congestion length and the historical cumulative congestion length is used as the error between the estimated and actual cumulative congestion length. The individual with the smallest error is selected as the current best individual, and then the population is updated through crossover and mutation operations. The population is iteratively updated to a preset number of iterations. The current best individual with the smallest error is taken as the final best individual, and the predicted traffic capacity corresponding to the final best individual is taken as the single-lane traffic capacity.

3. The method for determining the construction organization method for highway maintenance according to claim 1, characterized in that, The method of determining congestion and calculating congestion duration based on the target number of one-way lanes, the target number of lanes occupied for maintenance, the average daily traffic volume, and the single-lane capacity yields the daily congestion duration for road construction, including: Based on the set average daily traffic volume and the traffic volume ratio of each time period, calculate the traffic volume of the construction bottleneck section at each hour. Based on the target number of one-way lanes, the target number of lanes occupied for maintenance, and the single-lane capacity, the remaining lane capacity excluding road construction is calculated. The period during which the hourly traffic volume of the bottleneck section of the construction is greater than the capacity of the remaining lanes excluding those occupied by construction is identified as a congested period. The congested periods are then summed up to obtain the daily congestion duration for the construction site.

4. The method for determining the construction organization method for highway maintenance according to claim 1, characterized in that, The step of selecting the final construction organization method from the construction organization method comparison table based on the traffic data of the road section to be tested includes: Obtain the following data from the traffic data of the road segment to be tested: number of one-way lanes, number of lanes occupied by maintenance, average daily toll per 100 kilometers, and first ratio of the road segment to be tested; In the construction organization method comparison table, the number of one-way lanes of the test section is matched with the target number of one-way lanes, the number of maintenance lanes occupied by the test section is matched with the target number of maintenance lanes, the average daily toll per 100 kilometers of the test section is ≤ the threshold of the average daily toll per 100 kilometers, and the first ratio of the test section is ≤ the threshold of the first ratio. The corresponding construction organization method type is taken as the final construction organization method.

5. A device for determining the organization method of highway maintenance construction, used to implement the method for determining the organization method of highway maintenance construction as described in any one of claims 1 to 4, characterized in that, The device includes: The traffic bottleneck analysis model construction module for construction sections is used to acquire maintenance and construction scenario data of the target highway, calibrate core parameters based on the maintenance and construction scenario data, construct a basic traffic flow graph model and a capacity prediction model based on the core parameters, and establish a traffic bottleneck analysis model for construction sections based on the basic traffic flow graph model and the capacity prediction model. The single-lane capacity acquisition module is used to obtain the single-lane capacity by solving the traffic bottleneck analysis model of the construction section through a genetic algorithm with the goal of minimizing the error between the estimated and actual values ​​of the cumulative congestion length. The module for obtaining daily congestion duration during road construction is used to obtain the average daily traffic volume of the target highway, the number of target one-way lanes, and the number of target maintenance lanes. Based on the number of target one-way lanes, the number of target maintenance lanes, the average daily traffic volume, and the single-lane capacity, it performs congestion judgment and congestion duration calculation to obtain the daily congestion duration during road construction. The ratio acquisition module is used to obtain the construction period of road occupancy and the construction period of road closure of the target highway. The ratio of the construction period of road occupancy to the construction period of road closure is used as the first ratio, and the ratio of the daily congestion time of road occupancy to the construction period of road closure is used as the second ratio. The first threshold acquisition module is used to set the second ratio threshold, determine the construction organization method type, the first ratio threshold, and the daily congestion duration threshold for road occupation construction based on the second ratio and the second ratio threshold, and determine the daily average traffic volume threshold based on the daily congestion duration threshold for road occupation construction. The second threshold acquisition module is used to acquire the vehicle composition ratio and highway toll standard of the target highway, and calculate the daily average toll threshold per 100 kilometers based on the daily average traffic volume threshold, vehicle composition ratio and highway toll standard. The final construction organization method acquisition module is used to construct a construction organization method comparison table based on the target number of one-way lanes, the target number of lanes occupied for maintenance, the daily average toll fee threshold per 100 kilometers, the first ratio threshold, and the construction organization method type. It then obtains traffic data for the road segment to be tested and selects the final construction organization method from the construction organization method comparison table based on the traffic data of the road segment to be tested.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method for determining the highway maintenance construction organization method as described in any one of claims 1 to 4.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for determining the highway maintenance construction organization method as described in any one of claims 1 to 4.

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