As a result of rapid economic development, the length of high-grade highways is increasing annually in China. At the end of 2019, the total length of highways in China was 5.0125 million kilometres, and the length of highways classified to be second-grade and above was 672,200 kilometres, accounting for 13.4%, of which the highway length was 149,600 kilometres. The number of road traffic accidents and deaths in China attracts attention. In 2018, 244,937 road traffic accidents were reported, causing 63,194 deaths and 258,532 injuries, and direct property losses of CNY 1,384,559,000 [1]. There are still many difficulties in improving road traffic safety, and it is necessary to take measures to curb the high incidence rate of traffic accidents in China.
Based on such background, researchers use historical data analysis or conflict analysis methods to research traffic safety in terms of maintenance policy research, maintenance economics, maintenance processes and responsibilities, maintenance automation, etc., to extend the service life of highways, ensure the maintenance of highways, and upkeep desirable technical conditions of the facilities along the route. Therefore, the urgent questions that remain unanswered are that of how to comprehensively evaluate the potential safety hazards of the road from the perspective of road users and of how to propose corresponding safety maintenance measures.
This paper establishes a gray correlation model of traffic safety maintenance evaluation through the fuzzy grading evaluation method and analyses the damage characteristics of the subgrade and pavement, safety facilities, management facilities, channelling facilities, and greening facilities of high-grade highways. It is a means to predict maintenance and repair effects more accurately to ensure the safety satisfaction of road users and provide a reference for highway safety maintenance decision-making and safety service improvement.
At present, wide research has been conducted on highway maintenance safety. The American maintenance standards manual
The above research results were mainly concentrated on the maintenance policy research, economics, processes and responsibilities, automation, etc., and there were few studies on the traffic safety characteristics, especially on the correlation of the maintenance of traffic safety facilities. From the perspective of ensuring the safety of road users, this paper adopts the fuzzy hierarchical evaluation method, which is based on gray correlation degree to discuss the damage characteristics of the components of high-grade highways respectively, and establishes a traffic safety maintenance evaluation model to provide references for the classification of safety maintenance levels.
In this paper, Section 3 analyses common damages of highway components, selects evaluation indicators, and builds evaluation models. Section 4 verifies the objectivity of the evaluation indicators and the rationality of the evaluation model and divides safety maintenance levels at crossroads and road sections. The last section summarises this paper and provides relevant conclusions.
In this research, high-grade highways are divided into level crossroads, and road sections are to be evaluated separately. By analysing the components of the subgrade and pavement, safety facilities, management facilities, channelling facilities, and greening facilities his section provides evidence for the selection of evaluation indicators [18].
This part is mainly composed of road shoulders and pavements, among others. The common damages of road shoulders include poor surface flatness, improper width setting of the shoulders, and staggering. The common damages of road pavements are ruts and breakages on the road surface, insufficient slip resistance, and water accumulation.
This part is mainly composed of central separation zones, separation zones for motor-driven vehicle and non-motor-driven vehicles, roadside guardrails, crossroad marks, anti-glare facilities, sightline guidance facilities, etc. Common damages mainly include poor sight distance, missing parts, poor reflectivity, poor visibility, weakened retroreflectivity, etc.
This part mainly refers to traffic markings, signs, signal lights, street lights, etc. Common damages are severely worn facilities, insufficient anti-slip resistance, poor retroreflectivity, missing parts, or damaged, tilt, reduced visibility, unreasonable location settings, and insufficient or overloaded information.
Channelling is the use of diversion islands or marking lines to separate or control conflicting vehicle flow. The common damages are improper channelling, damages of channelling diversion islands, worn channelling marks, and poor visibility.
Greening refers to the planting of trees and flowers on both sides or in the middle of the road, aiming to improve the traffic environment. The major common damages are the inappropriate selection of greening tree species which negatively affect the viewing distance.
Combining the selection principle of evaluation indicators, this research uses a combination of qualitative analysis and quantitative calculation to break down the high-level highway, thereby forming an ordered hierarchical structure, as shown in Tables 1 and 2.
Evaluation indicator system of the high-grade highway section.
High-grade highway road section evaluation index | Subgrade and pavement |
Road shoulder safety intactness |
Management facilities |
Traffic marking visibility |
Safety facilities |
Central separation zone intactness |
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Greening facilities |
Rationality of greening plants |
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Evaluation indicator system of high-grade highway crossroad.
High-grade highway crossroad evaluation index | Subgrade and pavement |
Road shoulder safety intactness |
Management facilities |
Traffic marking visibility |
Safety facilities |
Central separation zone intactness |
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Management facilities |
Channelling facility intactness |
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Greening facilities |
Rationality of greening plants |
Considering the multiple levels and complexity of the evaluation object, this research uses the fuzzy hierarchy theory based on gray correlation to construct the evaluation model. This method combines the advantages of the three evaluation methods of gray correlation, hierarchy analysis, and fuzzy theory, which are conducive to solve the problems of complexity, ambiguity, and insufficient information in traffic safety evaluation, so as to obtain more accurate, scientific, and reasonable evaluation results. The specific steps are as follows:
The high-grade highway maintenance evaluation system is a complex system, with each subsystem depending, influencing, and impacting every other. In the safety evaluation process, if the mutual influence between the relevant subsystems is ignored, the evaluation results will not be comprehensive, objective, or reliable. The comprehensive evaluation system consists of three functional modules, namely the data acquisition module, the subsystem performance evaluation module, and the comprehensive evaluation module, as shown in Figure 1 below.
The comment set is made for each evaluation indicator according to a certain scale. In this research, in order to facilitate calculation and analysis, the vector comment set of each indicator corresponds to and is consistent with the system's safety level. The comment level is shown as
Summary of whitening weight function.
1 | Level 1 (90, 100) |
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2 | Level 2 (80, 90) |
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3 | Level 3 (70, 80) |
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4 | Level 4 (0, 70) |
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For the evaluation index
For the evaluation index
The evaluation index
Subjective weighting method and objective weighting method are commonly adopted to determine the weight value of each indicator, namely:
In the formula:
W is the relative weight vector of the original judgement matrix
P is solved by the minimum closeness, and the calculation formulas are shown in formulas (7) and (8).
According to the hierarchical relationship of highway safety maintenance and its subsystems given in Tables 1 and 2 above, the weight of each parameter is summarised and shown in Figures 2 and 3 below. There are 16 sub-indicators in the highway section and 23 sub-indicators in the plane crossroads.
In the actual evaluation process, if there are missing items to be evaluated, the weights are adjusted according to the following formula (9).
In the formula:
∑
Taking the correlation between the first-level evaluation indicator vector and the relatively most indicator vector as the evaluation indicator, the gray evaluation matrix of the first-level indicator is obtained.
Obtain the traffic safety service level
To facilitate analysis,
According to the principle that evaluating and analysing is based on the maximum degree of membership, the comments of corresponding max
Based on the objective and operable principle, and in reference to the road maintenance standards and literature, the 23 indicators selected for the crossroads safety maintenance evaluation system and the 16 indicators selected for the road section maintenance safety evaluation system are determined in detail. The scoring standards are shown in Table 4. The score of each indicator is deducted from the full score of 100 points, and the safety evaluation score of each indicator is obtained after deducting the lost points from 100 points.
Indicator deduction standard.
Road shoulder safety intactness | Road shoulder flatness, gutter cover intactness, and subgrade drainage | Deduct 0–5 | Deduct 5–10 | Deduct 10–20 |
Road pavement safety intactness | Pavement flatness and damage | Deduct 0–5 | Deduct 5–10 | Deduct 10–20 |
Central separation zone intactness | Central separation zone type, broken end treatment, and intactness | Deduct 0–5 | Deduct 5–10 | Deduct 10–20 |
Separation zone of motor-driven vehicle and non-motor-driven vehicle intactness | Damage, incline, excessively high kerbstones, etc. | Deduct 0–5 | Deduct 5–10 | Deduct 10–20 |
Roadside guardrail intactness | Guardrail structure intactness, end treatment, and appropriate setting | Deduct 0–5 | Deduct 5–15 | Deduct 15–30 |
Railway crossroad marking intactness | Defect, tilt, visibility, and reflectivity | Deduct 2 for tilting and deformation, deduct 5 for massing and damage, and deduct 5 for loss of reflectivity | ||
Anti-glare facilities intactness | Missing of anti-glare effect | Deduct 0–5 | Deduct 5–10 | Deduct 10–20 |
Reflecting mirror intactness | Improper position, improper angle, an inclination of a pillar, and damaged mirror surface | Deduct 5 for any of these situations | ||
Line-of-sight intactness | Defects, deformation, poor reflectivity, poor visibility, and blocked | Deduct 5 for the former, deduct 10 for the latter | ||
Road spike intactness | Missing or poor reflection | Deduct 2 | ||
Traffic marking visibility | Worn condition | Deduct 2 for minor cases, deduct 5 for severe cases | ||
Street light intactness | Insufficient brightness or damaged components | Deduct 3 | ||
Signal brightness | Fuzzy situation | Deduct 10 for minor cases, deduct 20 for severe cases | ||
Signal visibility | Obscured or confusing background | Deduct 15 | ||
Signal intactness | Damaged or incomplete components | Deduct 5 | ||
Traffic sign information | Overload or wrong information | Deduct 10 for overloaded information, deduct 15 for wrong information | ||
Traffic sign visibility | Blocked or too small font | Deduct 10 | ||
Traffic sign intactness | Panel deformation, cracks, corrosion, loose deformation of support, etc., no fragile design or flexible design | Deduct 5 for the former, deduct 10 for the latter | ||
Rationality of greening plants | Plant height does not meet requirements | Deduct 5–10 | ||
Line-of-sight sufficiency | Does not meet line-of-sight requirements | Deduct 15–20 | ||
Channelling facility intactness | Worn facilities or breakage | Deduct 5 for minor cases, deduct 10 for severe cases | ||
Channelling facility visibility | Poor | Deduct 5–15 | ||
Channelling rationality | Unreasonable and excessive channelling | Deduct 10–20 |
In order to measure the influence of the evaluator's subjective factors on the indicator score and ensure the impartiality, the objectivity of the evaluation data needs to be verified. The general model of the single factor problem is as follows. There is a factor
It is necessary to test the hypotheses that
Calculate the sample variance
Deduction of test indicators.
Rationality of channelling | Driver | 9 | 5 | 6 | 4 | 5 | 6 |
Maintenance personnel | 5 | 9 | 7 | 6 | 4 | 4 | |
Researcher | 8 | 6 | 4 | 5 | 4 | 5 |
The research conducts a one-way analysis of variance on the data in Table 5, assuming that for a given
Common methods for model rationality verification include subjective evaluation, fitting test, residual plot method, regression analysis, etc. In this research, the score of the plane crossroads section and road section used the subjective evaluation method, while the linear correlation between the model's score and the subjective score used regression analysis. The rationality of the model is verified by the results.
The test section is selected first, which mainly includes two parts, namely model calculation and subjective scoring. Among them, the model calculation is to access the evaluation indicator scores of the selected test sections, and to score each evaluation object and comprehensive evaluation object according to the above safety maintenance evaluation model; the subjective scoring is to organise a batch of experienced experts in safety maintenance evaluation to make a subjective and quantitative evaluation of the safety maintenance quality of the test section, for which the specific deduction criteria are shown in Table 6.
Interval partition of grade.
Road section | Grade 1 | 87–100 | Crossroad | Grade 1 | 89–100 |
Grade 2 | 85–87 | Grade 2 | 87–89 | ||
Grade 3 | 83–85 | Grade 3 | 84–87 | ||
Grade 4 | 0–83 | Grade 4 | 0–80 |
In order to verify the effectiveness of the model, the expert scoring panel offers each indicator's score and the total score on the road section every 2 km on the S321 test highway. Validation data of rationality of road section evaluation model is shown in Figures 4 and 5 below.
By analysing the correlation between Figures 4 and 5, it can be seen that the absolute error of most validation data is within 5 points, as shown in Figures 6–10. The result is indicating that the model has strong rationality,
In order to make the scoring results of the above model reflect the safety and service status of the high-level highways of the evaluation object in a more visual manner, this research uses the cumulative frequency curve method to divide the safety service level. The research selects 50 sections and 40 crossroads of high-grade highways in Jiangsu Province for data collection, using the model to score and accumulating frequencies in an order from low to high. Points with a cumulative frequency of 0 ∼ 15% are selected as the classification criteria for grade 4; points with a cumulative frequency of 15–50% as the classification criteria for grade 3; points with a cumulative frequency of 50–85% as the classification criteria for grade 2; and points with a cumulative frequency of 85–100% as the classification criteria grade level 1. The results are shown in Table 6.
In order to accurately predict the effect of maintenance and repair and improve the safety satisfaction of road users, this paper analyses the damage characteristics of the subgrade and pavement, safety facilities, management facilities, greening facilities, and channelling facilities of high-grade highways, establishing a traffic safety maintenance evaluation model and drawing the following conclusions:
The evaluation indicator system for plane crossroad and road section safety maintenance is established from the five aspects of subgrade and pavement, traffic safety facilities, management facilities, channelling, and roadside greening. According to the constituent characteristics of each evaluation indicator, a high-grade highway safety maintenance evaluation indicator scoring standard is established. A high-grade highway traffic safety maintenance evaluation model is established using the fuzzy hierarchical evaluation method of gray correlation degree. The cumulative frequency curve method is used to divide the safety level of high-grade highways, and the standard method and interval for each safety maintenance level are provided.
This research gives the safety level of each road component according to its safety status, which points out the weak links of road components in a visual manner, leading to more target decisions of highway safety maintenance and providing a reference for highway safety maintenance. The determination of indicators in this study also has certain limitations. In the follow-up study, we will further expand the scope of highway distribution, increase the regional factors of severe weather, and incorporate bridge and culvert structures and other components, so as to improve the indicator system.