Yazar "Mutlu, Mehmet Metin" seçeneğine göre listele

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    Comparing the performance of metaheuristics on the transit network frequency setting problem
    (Taylor & Francis Inc, 2024) Aksoy, İlyas Cihan; Mutlu, Mehmet Metin
    The Transit Network Frequency Setting Problem (TNFSP), an NP-Hard combinatorial optimization problem, has been frequently addressed in previous investigations, most of which employ metaheuristics. However, previous studies have not investigated to determine the best metaheuristic and the most effective parameter values for the TNFSP. This study aims to fill this research gap by comprehensively comparing five well-known metaheuristics, namely, Artificial Bee Colony, Differential Evolution, Firefly Algorithm, Genetic Algorithm, and Particle Swarm Optimization, on the TNFSP. The best parameter configurations for employed metaheuristics are determined using the brute-force method. The comparative results not only highlight the significance of metaheuristic selection in addressing the TNFSP but also reveal the importance of choosing the most suitable parameter values for these metaheuristics. Furthermore, the metaheuristics are performed on the transit networks obtained from previously published studies for comparison purposes. The results demonstrate that the metaheuristics achieve better frequency sets than the original frequency sets of the used transit networks.
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    Covid-19 transmission risk minimization at public transportation stops using differential evolution algorithm
    (Editorial Board Ejtir, 2021) Mutlu, Mehmet Metin; Aksoy, İlyas Cihan; Alver, Yalcin
    Public transportation vehicles, with their confined spaces and limited ventilation, are considered among the primary factors in the spread of COVID-19. As a measure to slow the spread of the virus during the pandemic, governments have applied passenger capacity restrictions to ensure physical distancing. On the other hand, the increase in the risk of disease transmission associated with passengers waiting together at stops is omitted. In this study, we consider the risk of disease transmission as a travel cost and formulate a risk minimization problem as a transit network frequency setting problem. We develop a bi-level optimization model minimizing the total infection risk occurring at stops, namely, the cumulative disease transmission risk cost. The Differential Evolution algorithm is employed to cope with the NP-hard bi-level transportation network design problem. We propose a novel objective function for the upper-level model, considering the infection risk cost based on passenger traffic at public transportation stops. A congested user-equilibrium transit assignment model is utilized to determine passenger movement. The proposed model is applied to a small-size hypothetical network, and a mid-size test network. Experimental studies provide evidence that the model can produce optimal solutions. Optimization results show significant improvements in the reduction of disease transmission risk compared to the optimizations depending on the traditional practice of transportation network planning based on user and operator costs. The proposed model provides risk cost reductions of 51% and 22% compared to the optimal solutions based on user cost minimization in the hypothetical network and Mandl's network, respectively.
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    Covid-19 transmission risk minimization at public transportation stops using differential evolution algorithm
    (TU Delft, 2021) Mutlu, Mehmet Metin; Aksoy, İlyas Cihan; Alver, Yalçın
    Public transportation vehicles, with their confined spaces and limited ventilation, are considered among the primary factors in the spread of COVID-19. As a measure to slow the spread of the virus during the pandemic, governments have applied passenger capacity restrictions to ensure physical distancing. On the other hand, the increase in the risk of disease transmission associated with passengers waiting together at stops is omitted. In this study, we consider the risk of disease transmission as a travel cost and formulate a risk minimization problem as a transit network frequency setting problem. We develop a bi-level optimization model minimizing the total infection risk occurring at stops, namely, the cumulative disease transmission risk cost. The Differential Evolution algorithm is employed to cope with the NP-hard bi-level transportation network design problem. We propose a novel objective function for the upper-level model, considering the infection risk cost based on passenger traffic at public transportation stops. A congested user-equilibrium transit assignment model is utilized to determine passenger movement. The proposed model is applied to a small-size hypothetical network, and a mid-size test network. Experimental studies provide evidence that the model can produce optimal solutions. Optimization results show significant improvements in the reduction of disease transmission risk compared to the optimizations depending on the traditional practice of transportation network planning based on user and operator costs. The proposed model provides risk cost reductions of 51% and 22% compared to the optimal solutions based on user cost minimization in the hypothetical network and Mandl’s network, respectively.
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    Transit frequency optimization in bi-modal networks using differential evolution algorithm
    (Turkish Chamber Civil Engineers, 2022) Mutlu, Mehmet Metin; Aksoy, İlyas Cihan; Alver, Yalçın
    This study proposes a bi-level optimization model for the transit frequency setting problem in bi-modal networks. The objective of the upper-level problem is to obtain a solution set of bus line frequencies that provide the minimum total travel cost of the car and bus users. Differential Evolution (DE) algorithm is employed in the upper-level model to determine the optimal headways for a given route structure. The lower-level model is a congested multi-modal user equilibrium assignment model, which considers the interactions of car and bus flows, for determining joint mode/route preferences of the network users, which considers the interactions of car and bus flows. The developed model is tested on Mandl's benchmark network to evaluate its performance and applicability. The comparative experiments demonstrate that the proposed model leads to reductions in transportation costs. Also, the result of numerous optimization runs shows that DE performs well in finding similar frequency sets in independent optimizations.
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    Urban road network maintenance scheduling using ant colony optimization
    (Gazi University, 2021) Aksoy, İlyas Cihan; Mutlu, Mehmet Metin; Alver, Yalçın
    In this study, an optimization model for road maintenance scheduling is proposed. The proposed model determines a maintenance schedule for a given set of links in an urban road network, that minimizes the total travel time during the maintenance period under a set of assumptions and a particular crew number constraint. It is assumed that all lanes of a road to be repaired are closed to traffic during the maintenance. Ant Colony Optimization (ACO), a suitable algorithm for discrete transportation problems, is employed in the proposed model. The model generates a set of roads to be closed for each maintenance day. The total travel time during the whole maintenance period is calculated using the deterministic user equilibrium assignment model. The proposed model is applied to a test network, and more efficient schedules in terms of total travel time are obtained compared to randomly generated schedules