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 Practitioners and decision-makers often face difficulties in selecting and prioritizing effective strategies to address challenges to sustainable urban transportation development. Although there has been considerable research conducted on the subject, the Tanzanian context, which is greatly affected by social and environmental problems, has received inadequate attention. Therefore, this study intends to bridge this gap by pinpointing the obstacles to sustainable urban transportation and proposing the most appropriate strategies to tackle them. The study proposes seven strategies and determines five criteria to prioritize them. To accomplish this, the study proposes a novel Fermatean fuzzy-based intelligent decision support model to assess the criteria weights and prioritizes strategies based on the weighted criteria. The study validates the proposed methodology by conducting a sensitivity analysis, which indicates that restricting car use (A5), improving sector coordination (A1), and conducting extensive research on transportation issues (A7) are the top three strategies for promoting sustainable urban transportation. The study’s findings hold significant value in providing urban transportation planners with helpful guidance to develop optimization techniques that can improve transportation systems.

In recent years, major regional transport infrastructure programmes have been adopted throughout the African continent. However, it has been noticed that while some programmes seem to be more effective, others have shown opposite outcomes. On this point, understanding the difference in the execution outcomes of these programmes is important for stakeholders. In this study, an improved fuzzy Step-Wise Weight Assessment Ratio Analysis based on the fuzzy Bonferroni aggregator is integrated with fuzzy Measurement of Alternatives and Ranking according to COmpromise Solution to assess the most critical factors affecting the performance of the African regional infrastructure programmes and identify the best regional transportation infrastructure programme based on its performance. Four regional transportation infrastructure programmes are considered. To rank these programmes, five criteria are defined. The results show that “leadership” and “availability of finance” are the most critical factors while the fourth alternative “East African road network project” is the best alternative among the four possible programmes. The robustness of the proposed methodology is validated through a three-phase sensitivity analysis. The results confirmed the stability and applicability of the proposed methodology.