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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.

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.