Browsing by Author "Silgu, Mehmet Ali"

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Citation - WoS: 6
Citation - Scopus: 6
Multi-Objective Optimization Framework for Trade-Off Among Pedestrian Delays and Vehicular Emissions at Signal-Controlled Intersections
(Springer Heidelberg, 2024) Silgu, Mehmet Ali; Göncü, Sadullah; Akyol, Görkem
Traffic congestion has several adverse effects on urban traffic networks. Increased travel times of vehicles, with the addition of excessive greenhouse emissions, can be listed as harmful effects. To address these issues, transportation engineers aim to reduce private car usage, reduce travel times through different control strategies, and mitigate harmful effects on urban networks. In this study, we introduce an innovative approach to optimizing traffic signal control settings. This methodology takes into account both pedestrian delays and vehicular emissions. Non-dominated sorting genetic algorithm-II and Multi-objective Artificial Bee Colony algorithms are adopted to solve the multi-objective optimization problem. The vehicular emissions are modeled through the MOVES3 emission model and integrated into the utilized microsimulation environment. Initially, the proposed framework is tested on a hypothetical test network, followed by a real-world case study. Results indicate a significant improvement in pedestrian delays and lower emissions.
Citation - Scopus: 1
A Bi-Objective Traffic Signal Optimization Model for Mixed Traffic Concerning Pedestrian Delays
(Elsevier B.V., 2024) Akyol, Görkem; Çelikoğlu, Hilmi Berk; Silgu, Mehmet Ali; Goncu, Sadullah
Urban traffic networks suffer in numerous ways from traffic congestion. Some of these adverse effects are increased travel times of cars, buses, bicycle users, pedestrians etc., with the addition of excessive greenhouse gas emissions. Transportation engineers and policy makers try to improve the quality of urban transportation systems by developing projects to enhance the pedestrian experience, reduce private car usage, reduce total time spent in the network through different control strategies, and diminish the detrimental effects. In this context, this study takes Connected and Automated Vehicles (CAVs) and pedestrians into account at signal-controlled intersections. A novel intersection signal control optimization methodology that incorporates pedestrian delays and vehicular emissions from CAVs is presented. Non-dominated sorting genetic algorithm-II is utilized to solve the multiobjective optimization problem. For the emission calculations, the MOVES3 emission model is utilized. The proposed framework is tested on real-world case study. Simulation experiments showed major improvements in pedestrian delays and lower emissions. © 2024 The Authors. Published by ELSEVIER B.V.