Browsing by Author "Albdairi, M."

Now showing 1 - 6 of 6

Citation - Scopus: 0
Evaluation of Robust Evacuation Strategies for Resilient Urban Infrastructure Through Microscopic Traffic Simulation
(Institute for Transport Studies in the European Economic Integration, 2025) Qadri, S.S.S.M.; Athar, A.I.; Albdairi, M.; Kabarcik, A.
Natural disasters are a global threat, highlighting the urgent need for effective disaster management systems worldwide. Many countries, both developed and developing, are not adequately prepared, emphasizing the importance of governmental action. Key to disaster management is the creation of specialized disaster management units that develop and implement rapid response plans for potential risks. A crucial aspect of disaster management is evacuation-the process of moving vulnerable populations to safer areas. However, evacuations face challenges such as timely alert issuance, traffic congestion, resident reluctance to evacuate, and potential damage to transportation infrastructure. These challenges can be mitigated through comprehensive evacuation plans that ensure smooth relocation to shelters. This paper addresses these issues by developing and evaluating traffic routing conditions in an evacuation study area using the microscopic simulator SUMO. It examines two algorithms, Dijkstra and A-star (A*), which optimize vehicle routes under different network conditions. By focusing on criteria such as Minimum Travel Time and Maximum Number of Evacuations (clearance time), the research aims to improve disaster response and resilience. The objective is to enhance evacuation procedures, thereby strengthening disaster management and ensuring the safety of affected populations. Results show that the A* algorithm outperforms Dijkstra, reducing travel times by up to 18% and network clearance times by up to 6.8% under optimal conditions. The Manhattan-based network design further enhances evacuation efficiency, reducing average waiting time by up to 35% compared to the actual map. © 2025 Institute for Transport Studies in the European Economic Integration. All rights reserved.
Citation - Scopus: 3
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.
Citation - Scopus: 2
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: 0
A Novel Design of a Quadratic Koch Fractal Nano Antenna for Thz Application
(American Scientific Publishing Group (ASPG), 2025) Shareef, A.N.; Shaalan, A.B.; Naeem, H.S.; Albdairi, M.
The study, called "A Novel Design of a Quadratic Koch Fractal Nanoantenna," aims to create and study a brand-new microstrip nanoantenna that works in the THz range, specifically between 100 and 130 THz, and can handle a wide range of optical communication frequencies. We examine two unique geometries, specifically the quadratic Koch fractal patch (QKF) and the complementary quadratic Koch fractal patch (CQKF), utilizing two different dielectric materials as substrates. We employ silicon (Si) dielectric material because of its high dielectric constant (11.9), while we use the silicon dioxide (SiO2) dielectric material because of its dielectric constant (4). The feeding method employed to stimulate these nanoantennas has been waveguide feed at a frequency of 50 Ω.We have employed a software simulator, available for purchase as CST STUDIO SUITE, to achieve the established objectives for assessing the performance of each proposed nanoantenna. © 2025, American Scientific Publishing Group (ASPG). All rights reserved.
Citation - Scopus: 0
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.
Citation - Scopus: 2
Robust Classification for Sub Brain Tumors by Using an Ant Colony Algorithm With a Neural Network
(Innovative Information Science and Technology Research Group, 2024) Faris, R.A.; Mosa, Q.; Albdairi, M.
A brain tumor is responsible for the highest number of fatalities across the globe. Identifying and diagnosing the tumor correctly at an early stage can significantly improve the chances of survival. Classifying a brain tumor can be aided by factors like type, texture, and location. In this research, we propose a robust technique for detecting sub-brain tumors using an ant colony algorithm coupled with a neural network. To achieve this, we employ an ant colony optimization algorithm (ACO) to eliminate extraneous features extracted from the image, enabling us to find the most effective representation of the image. This, in turn, assists the Neural Network (NN) in the process of classification. Our system involves a series of five steps. Initially, we perform cropping processing as the initial step to eliminate unnecessary background from the original MRI images. This enhances the overall quality of the images, thereby improving the performance of the classification method. In the next step, we conduct image preprocessing to enhance image quality, making it easier for the feature extractor to accurately extract features. The third step involves employing a feature extractor for each image. In the fourth step, we utilize the ant colony optimization algorithm (ACO) to identify the most suitable representation of the image, which further aids the NN in classification. In the fifth and final step, we utilize an NN method to classify the vector obtained from the fourth step (optimization method) to determine the subtype of the brain tumor (normal, glioma, meningioma, and pituitary). Our model's performance is evaluated using the publicly available BT-large-4c dataset, and it surpasses current state-of-the-art methods with exceptional accuracy, attaining a rate of 87.7%. The effectiveness of our approach is particularly evident in maintaining accurate classifications within MRI input images. © 2024, Innovative Information Science and Technology Research Group. All rights reserved.