Browsing by Author "Kabarcik, Ahmet"

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Evaluation of Robust Evacuation Strategies for Resilient Urban Infrastructure Through Microscopic Traffic Simulation
(Univ Studi Trieste, Ist Studio Trasporti integrazione Econ Europea-Istiee, 2025) Kabarcık, Ahmet; Qadri, Syed Shah Sultan Mohiuddin; Qadri, Shah Sultan Mohiuddin; Athar, Ambreen Ilyas; Albdairi, Mustafa; Kabarcik, Ahmet; Endüstri Mühendisliği
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.
Optimization of Signalized Intersections: Analyzing Autonomous Vehicle Behaviors Through Data-Driven Simulations
(Springer Science and Business Media Deutschland GmbH, 2026) Qadri, Syed Shah Sultan Mohiuddin; Albdairi, Mustafa; Almusawi, Ali; Kabarcik, Ahmet; Abdulrahman, H. S.
Autonomous vehicles (AVs) present a transformative opportunity to enhance traffic flow, particularly at urban intersections where delays are most frequent. This study investigates how different AV driving behaviors and penetration rates affect traffic efficiency at signalized intersections. Using a microscopic simulation model in PTV VISSIM, the research centers on a four-way intersection in Balgat, Ankara. Five AV driving behaviors—cautious, normal, aggressive, platooning, and mixed—are modeled under various signal cycle lengths. The simulation’s accuracy was ensured through calibration and validation with real-world traffic data. The findings reveal that the integration of AVs can significantly improve traffic flow, with aggressive and platooning driving behaviors achieving the most notable reduction in vehicle delays, particularly at shorter cycle lengths (60–70 s). Increased AV penetration rates amplify these positive effects, reducing delays and queue lengths in all tested scenarios. In contrast, cautious AV behaviors led to more significant delays, highlighting the importance of intelligent AV driving strategies for optimizing traffic management. The results underscore that optimizing signal cycle lengths with AV integration can reduce congestion and improve urban traffic flow. While the study demonstrates the potential of AVs to enhance urban traffic management, it also stresses the need for real-world validation and the development of adaptive traffic signal systems capable of accommodating diverse driving behaviors. These insights offer urban planners and policymakers valuable guidance on integrating AVs into current infrastructure to create more resilient and efficient transportation networks. © 2025 Elsevier B.V., All rights reserved.