This research introduces the Urban Traffic Mobility Optimization Model (UTMOM), a data-driven methodology for analyzing two distinctive urban traffic datasets through the integration of data mining and mathematical modeling. Designed to decode the complexities of urban mobility patterns, UTMOM meticulously evaluates daily traffic dynamics with a focus on reducing discrepancies and underscoring variations in traffic intensity, particularly during peak times. Our findings unveil pivotal insights into the differences across datasets, providing a substantial contribution to the realms of traffic management and urban planning. UTMOM delves into the intricacies of traffic flow variations, emphasizing the critical importance of comprehending fluctuations in traffic volume across diverse times and locations. By incorporating detailed graphical representations and statistical validations, including ANOVA analysis, our study delivers a comprehensive evaluation of UTMOM’s precision in reflecting real-world traffic scenarios. These insights affirm the value of data-informed strategies in optimizing traffic flow and alleviating congestion. Positioned as a valuable asset for traffic engineers, data scientists, and urban planners, UTMOM advocates for advanced modeling techniques to improve urban mobility. Beyond enriching academic discourse on traffic analysis, UTMOM offers actionable intelligence for enhancing the efficiency and sustainability of urban transportation systems. Through this in-depth investigation, our aim is to catalyze the development of innovative solutions to traffic challenges, steering towards smoother and more sustainable urban environments.

Supply chain management covers suppliers, manufacturers, distributors, and retailers linked by financial and information frameworks that require analysis, monitoring, and improvement of the performance regularly. This study presents a hybrid model of strategical and operational metrics to support managers through a clear and risk-free decision making process based on performance. Four methods which are Analytic Network Process (ANP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Decision-Making Trial and Evaluation Laboratory (DEMATEL) are proposed as an integrated approach. All performance metrics are taken from the latest version of Supply Chain Operations Reference (SCOR) model for standardisation. A real-world construction management example is applied as case study and results of application show that the proposed model is superior to the traditional models in terms of its improved decision quality. Cash Cycle Time, Return on Working Capital and Perfect Order Fulfilment were the most important performance metrics.

Asphalt is known as a self-healing material due to its temperature-related flow behavior. When damage occurs in asphalt concrete, bitumen can flow into cracks with heat and provide recovering by filling the cracks. Much of the current literature on self-healing pays particular attention to artificially heat asphalt concrete by several methods, including microwave heating. Although there is a growing body of literature that recognizes using steel fibers in microwave healing asphalt, there are no data on the use of aluminum fibers to improve microwave healing. This paper evaluates the microwave healing potential of asphalt concrete that contains metal wastes such as aluminum and steel shavings. Besides, the use of basalt and limestone aggregate (BA and LA) in asphalt concrete were evaluated. To achieve this, firstly, microwave absorption capabilities of BA and LA were determined. In the second step of the study, porous asphalt concrete specimens were prepared, and they were damaged by the indirect tensile (IDT) strength test at low temperatures. After that, damaged specimens were healed via microwave heating, and they were damaged again by the IDT strength test. In the end, the healing index of the specimens was determined as the proportion of healed specimen’s strength to the original. It has been found that BA is almost three times more microwave absorber material than LA, and asphalt specimens containing BA showed better healing performance. It has also been demonstrated for the first time that aluminum shavings might be a better option than steel shavings to accelerate microwave healing.

This paper classifies a case study on railway clusters allowing analysis of competitiveness performance criteria including the innovation capability of industries via different models. Data are collected from the Turkish railway industrial cluster; where 130 companies are located. Porters’ industries Life Cycle Evolution, Diamond, Five Forces Models and SWOT Analysis are applied to explore which criteria influence at most on the cluster companies’ competitiveness performances and innovation capability based on operational conditions. Porter’s Life Cycle analysis showed that the cluster is between introduction and growth phase. This phase has typical characteristics such as high investment demand on R&D, lack of qualified staff which could be compared to the performance criteria evaluated from the other three models. Another aim was to research the consistency and difference between the applied different dimension scaled models. Delivery time of the suppliers, qualified staff, capital range of the companies and physical enlargement area of the production plants were found to be the most important criteria in operational view of the competitiveness. Another result is that the applied tools are producing similar and consistent results independently from their dimension scales.

The fact that the transport costs directly affect the countries' economies all around the world and that the transport costs increase depending on the energy resources steered the countries into developing combined transport strategies that will decrease the transport costs. In this study, an assessment is made for the selection of locations of the logistics centers which will contribute to decreasing the transport costs, develop logistics sector strategies in Turkey, and thereby enable to identify the strategies for the fastest, safest, and the most economic transport utilizing integration of modes of transport with each other. Nonetheless, an integrated logistics model which includes freight capacity and selection of the optimum location is proposed for the efficient and useful logistics center. In this logistics model, a freight model, which is a criterion for optimal location selection, has been obtained by using regression analysis. According to this freight model, freight carrying capacity is determined for each alternative region. Optimum location selection has been made utilizing AHP (Analytic Hierarchy Process)-VIKOR (Vise Kriterijumska Optimizacija I Kompromisno Resenje) method. We aim to contribute to the literature by making systematically estimating variable carrying capacities and by being decisive in the logistics center location selection. The proposed logistics model has been applied for a logistics center to be established, and effective and valid results have been obtained.