Browsing by Author "Almusawi, A."

Now showing 1 - 5 of 5

Citation - WoS: 2
Citation - Scopus: 2
Advanced Rheological Characterization of Asphalt Binders Modified With Eco-Friendly and Polymer-Based Additives Under Dynamic Loading
(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Almusawi, A.; Nasraldeen, S.T.N.
This study explores the rheological performance of bitumen modified with a synthetic polymer (styrene–butadiene–styrene, SBS) and two environmentally sustainable additives—animal bone ash (AB) and waste cooking oil (WCO)—to enhance durability and deformation resistance under dynamic loading. Frequency sweep and linear amplitude sweep (LAS) tests were conducted to evaluate viscoelastic and fatigue behavior. SBS at 5% showed the highest elasticity and fatigue life, making it optimal for heavily trafficked pavements. Among bio-waste additives, 6% AB provided the highest stiffness and rutting resistance in laboratory tests; however, 5% AB offered a better balance between structural integrity and cracking resistance, making it more suitable for general pavement applications. WCO-modified binders demonstrated improved flexibility, with 4% WCO achieving the best balance between elasticity and softening, ideal for low-load or temperate environments. These results highlight the potential of combining synthetic and bio-based waste materials to tailor bitumen properties for sustainable and climate-responsive pavement design. © 2025 by the authors.
Citation - Scopus: 5
Examining the Influence of Autonomous Vehicle Behaviors on Travel Times and Vehicle Arrivals: a Comparative Study Across Different Simulation Durations on the Kirkuk-Sulaymaniyah Highway
(Society of Automotive Engineers Turkey, 2024) Albdairi, M.; Almusawi, A.
This study delves into the effects of autonomous vehicle behaviors on travel times and vehicle arrivals along the Kirkuk-Sulaymaniyah Highway, employing simulations spanning 3600, 5400, and 7200 seconds. Across varied traffic volumes ranging from 350 to 950 vehicles and autonomous vehicle behaviors categorized as cautious, normal, aggressive, aggressive platoons, and a mix alongside human-driven vehicles, the research unveils significant findings. Results highlight substantial reductions in average travel times and heightened vehicle arrivals among autonomous vehicles, particularly those exhibiting aggressive behaviors, compared to their human-driven counterparts. Across all simulation scenarios, aggressive autonomous vehicles consistently demonstrate superior performance, showcasing potential efficiency gains through aggressive driving algorithms. Furthermore, with increasing traffic volume, the advantages of aggressive autonomous behaviors become more pronounced, suggesting their adaptability to congested conditions. However, safety implications and traffic flow dynamics warrant caution, especially in scenarios with high volumes and aggressive behaviors. These insights underscore the importance of further research and policy considerations to leverage the full potential of autonomous vehicles while ensuring safely and efficiency on highways. © 2024 Society of Automotive Engineers Turkey. All rights reserved.
A Linear Programming Approach To Carpooling: Enhancing Commute Efficiency at Federal University of Technology Minna
(Institute of Electrical and Electronics Engineers Inc., 2024) Abdulrahman, H.S.; Almusawi, A.; Bamisaye, R.T.; Qadri, S.S.S.M.; Dawood, K.
This study explores the development of a carpooling system specifically designed for the Federal University of Technology Minna staff, utilizing the Civil Engineering Department as a case study. Amidst the escalating concerns of environmental sustainability, traffic congestion, and the economic burdens of individual commuting, carpooling presents itself as a sustainable alternative. Employing a mixed-methods approach, this research integrates a comprehensive survey to assess staff attitudes towards carpooling with the development of a linear programming model aimed at optimizing vehicle routes and allocations. The findings from the survey indicate a significant willingness among the staff to engage in carpooling, motivated by the anticipated benefits such as cost savings and reduced commuting times. The linear programming model further validates the practicality of substantially lowering total travel distances and emissions when compared to solo commuting practices. This targeted investigation showcases the carpooling system's capability not only to enhance commute efficiency among university staff but also positions it as a replicable and sustainable model for other academic institutions. The study contributes valuable insights into the design and operationalization of effective carpooling strategies within the university landscape, proposing a scalable framework applicable to similar urban contexts. © 2024 IEEE.
Citation - Scopus: 3
Microscopic Insights Into Autonomous Vehicles' Impact on Travel Time and Vehicle Delay
(Institution of Engineering and Technology, 2023) Almusawi, A.; Albdairi, M.; Qadri, S.S.S.M.
The future of highway travel is being reshaped by autonomous vehicles (AVs). This microscopic study, conducted along a 9-kilometer highway in Ankara, Turkey, explores the dynamic relationship between AVs and travel time, as well as vehicle delay. Analyzing 17 scenarios with varying AV penetration rates (ranging from 25% to 100%) and diverse AV behaviors (cautious, normal, aggressive, and mixed) uncovered intriguing patterns. Cautious AVs, while promoting safety, introduced slightly slower travel times. In contrast, aggressive AVs prioritized efficiency and reduced travel times, striking a delicate balance between speed and safety. The introduction of mixed AV fleets demonstrated an exciting equilibrium, delivering competitive travel times and mitigating delays. Most notably, the presence of AVs in all configurations exhibited the potential to relieve congestion and enhance overall traffic flow. The findings offer a compelling microscopic perspective on the transformative potential of AVs in shaping the future of highway transportation. Understanding the complex dynamics of travel time and delay is critical for informed policy decisions and the evolution of urban mobility as autonomous vehicles (AVs) continue to improve. © The Institution of Engineering & Technology 2023.
Citation - Scopus: 3
Optimizing Traffic Signal Timing at Urban Intersections: a Simheuristic Approach Using Ga and Sumo
(Institute of Electrical and Electronics Engineers Inc., 2024) Qadri, S.S.S.M.; Almusawi, A.; Albdairi, M.; Esirgün, E.
This study introduces an innovative simheuristic framework that integrates the Simulation of Urban MObility (SUMO), a detailed microsimulation tool, with the Genetic Algorithm (GA), a robust optimization method, for optimizing traffic signal timing (TST) at signalized intersections. Specifically designed to be applied to typical four-leg intersection phase plans, this framework systematically determines the most effective green signal timings to enhance traffic flow efficiency and reduce environmental impact. By meticulously testing each potential TST solution generated by the GA, using SUMO to simulate its real-world impacts, the framework provides a thorough assessment of various signal timing strategies. Comparative analyses against established methodologies, such as the Particle Swarm Optimization (PSO) algorithm and Webster's traditional method, are conducted during peak traffic demand periods to evaluate the framework's effectiveness in managing congestion and emissions. Our results demonstrate that the proposed simheuristic approach significantly outperforms the benchmarks: it achieves a reduction in CO levels by 4.97% compared to PSO and 11.76% compared to Webster; NOx emissions are reduced by 2.5% and 3.94%, respectively; and PMx levels see a decrease of 3.83% and 6.58%. These improvements underscore the substantial benefits of the framework in both traffic flow efficiency and environmental sustainability, providing critical insights for traffic engineers and urban planners aiming to implement advanced TST strategies in complex urban settings. This study not only enhances understanding of dynamic traffic management but also supports sustainable urban development goals. © 2024 IEEE.