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To address road accident losses, there is a need to prioritize safety factors, especially in high-risk locations on the road network, toward assuring a sustainable transport system. This paper proposes an approach for quantitative risk assessments of safety factors in hazardous road locations and involves the integration of the Fuzzy logic model, the Analytic Hierarchy Process (FAHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). This new innovative method offers a way to prioritize and select safety factors associated with hazardous locations using a hierarchical structure. To demonstrate the applicability of this method, a case study was conducted in Egypt. The assessment process involved active participation by professionals through multiple expert meetings. This collaborative approach ensures that the assessment incorporates valuable real-world knowledge and experiences. It analyzed road safety hazardous conditions across various sections, including intersections, non-intersection sections, narrow bridge sections, and curve sections. The application of FAHP-TOPSIS enables the determination of weights for safety factors within each section, facilitating the evaluation of safety indices between them and ranking the safety hazard sections. The achieved analysis revealed that the hazard safety factor index is comparatively higher in curved sections compared to other types of sections. Light utility poles and road barriers significantly affected the hazard index. By utilizing this approach, governments may make informed decisions regarding the allocation of resources and the implementation of safety measures at hazardous road locations. [ABSTRACT FROM AUTHOR]

The relevance of the study is conditioned by the urgent need for the practical derivation of clear criteria for assessing the degree of influence of certain materials used in road construction on the state of the roadside environment directly during such works, including the overall environmental situation in the area. In this context, the main purpose of this study is to conduct an experimental investigation to determine the physical and mechanical properties and durability of some building materials used during road repair, to perform a comparative analysis of the results obtained to determine their optimal use in terms of influencing the state of the roadside environment during the repair of a highway section. The leading method in this study is a combination of a systematic analysis of the influence of various materials on the state of the roadside environment during the repair with experimental studies. The results obtained indicate the different influence of materials used in road repair works on the state of the roadside environment and the need to choose their optimal combination in the future when repairing the roadway. The results obtained and the practical conclusions formed on their basis will be of significant importance directly for the employees of the road repair services, who, by their occupation, solve the issues of using specific building materials when repairing and eliminating damage to the roadway, as well as for researchers who solve theoretical problems of choosing specific materials for carrying out repair works, from the standpoint of assessing their impact on the roadside environment. [ABSTRACT FROM AUTHOR]

This paper introduces a novel Integrated Interval Rough Pivot Pairwise Relative Criteria Importance Assessment (IRN PIPRECIA) model combined with Interval Rough Combined Compromise Solution (IRN CoCoSo), marking a significant advancement in sustainable traffic flow management for commercial vehicles. This innovative merger is a first in literature, methodologically enhancing the evaluation of road sections based on critical parameters including passenger car equivalent (PCE) 85%, AADT, road conditions, and accident data. Our model systematically fills the research gap in holistic traffic performance analysis, providing a unique tool for prioritizing road safety and efficiency. The key scientific contribution is the model's ability to integrate causal and consequential traffic factors into a single framework, offering a novel multi-criteria decision-making (MCDM) approach. Results, validated through various verification models, show the integrated model's effectiveness in real-world scenarios, confirming its robustness and stability. With strong engineering application potential, our work supports urban planners and traffic managers in making informed, sustainable decisions. The model extends beyond traditional traffic analysis, promising a shift towards more adaptive, data-driven infrastructure management. Future research will aim to refine the criteria basis and explore real-time decision-making through advanced MCDM applications. [ABSTRACT FROM AUTHOR]

In this study, a new combined framework based on the methods of Multiple-criteria decision-making and mathematical planning was provided in order to select the Road Sections (RS) to carry out Maintenance and Repair Operation (MRO) in order to maximise the reliability of using roads. At first, using Multiple-criteria decision-making of the Best-Worst Method (BWM) and Grey Relational Analysis (GRA) methods, the weight of each RS for each MRO was defined, which can be considered as the amount of improvement in every section for the implementation of each MRO. Then, using a mathematical model, the optimal decision was specified to implement MRO for each RS. According to the calculation results, the fracture level on the road with the weight of 0.159 is the most important criterion and the lack of light with the weight of 0.074 is the least important criterion for selecting the type of MRO. Also, using the results of the mathematical model creates a confidence level of more than 93 percent in using all the section of road, which shows 14.8 percent improvement compared to the current situation. Also, the robust model results have also shown that as the level of uncertainty level rises along with the increase in the robust value, the road reliability decreases. [ABSTRACT FROM AUTHOR]

Many studies have examined the effect of roads on landscape fragmentation. Yet they rarely considered local characteristics of the road and road buffer widths. Therefore, this study that took place in the Qingzang Highway (QH) examined the variations in road buffers and road sections of landscape fragmentation. The QH was divided into 32 sections with 23 buffer areas. Based on the indicators of landscape fragmentation from 1980 to 2018, we found significant spatial heterogeneity between sections and buffers. Generally, landscape fragmentation decreased with increasing buffer distance to the QH. For different sections, the coefficients of variation between buffers were rather high and significantly different. Therefore, fixed-width buffers may overestimate or underestimate the spatial scope and influence intensity of a road. The impacts of road sections around provincial capitals, prefecture-level cities and main counties on landscape fragmentation were relatively extensive and formed clusters of highly fragmented areas. Geodetector results indicated that natural and anthropogenic factors, such as altitude, climate, distance to major settlements and socioeconomic conditions, could well explain the spatiotemporal characteristics of landscape fragmentation. Altitude, precipitation and the distance to major settlements had higher explanatory power for landscape fragmentation in permafrost regions, whereas slope and socioeconomic condition had higher explanatory power for non-permafrost regions in Xizang Autonomous Region.

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Traffic management is a significantly difficult and demanding task. It is necessary to know the main parameters of road networks in order to adequately meet traffic management requirements. Through this paper, an integrated fuzzy model for ranking road sections based on four inputs and four outputs was developed. The goal was to determine the safety degree of the observed road sections by the methodology developed. The greatest contribution of the paper is reflected in the development of the improved fuzzy step-wise weight assessment ratio analysis (IMF SWARA) method and integration with the fuzzy measurement alternatives and ranking according to the compromise solution (fuzzy MARCOS) method. First, the data envelopment analysis (DEA) model was applied, showing that three road sections have a high traffic risk. After that, IMF SWARA was applied to determine the values of the weight coefficients of the criteria, and the fuzzy MARCOS method was used for the final ranking of the sections. The obtained results were verified through a three-phase sensitivity analysis with an emphasis on forming 40 new scenarios in which input values were simulated. The stability of the model was proven in all phases of sensitivity analysis.

The conventional gray predication model GM (1, 1) cannot accurately analyze the dynamic traffic index information of complex and scattered road sections because it may cause relatively large error and performs not well in stability. In order to solve this problem, a dynamic traffic safety grade evaluation model for road sections based on gray fixed weight clustering is designed. In this method, In this method, the gray clustering evaluation method is adopted for gray clustering to complex and scattered traffic safety grade evaluation indexes, and the gray fixed weight clustering method is adopted to weight each clustering index in advance; the clustering weight of each index is set by a fuzzy consistent matrix, on which the fixed weight coefficient of the index is calculated and the clustering vector is constructed; the cluster coefficients and cluster vectors are combined to obtain the clustering indexes of traffic safety evaluation; then a BP neural network dynamic traffic safety grade evaluation model for road sections is constructed according to the indexes, so as to accurately evaluate the dynamic traffic safety grade of road sections. The experiment results show that the designed model method can effectively evaluate the dynamic traffic safety grade of 31 road sections in areas with a high probability of traffic congestion with small evaluation error and high stability, so it meets the design requirements. [ABSTRACT FROM AUTHOR]