Browsing by Author "Gil-Marin, Jhan Kevin"

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    33
    (33, 33) Moreno-Palacio, Diana P.; Gonzalez-Calderon, Carlos A.; Posada-Henao, John Jairo; Lopez-Ospina, Hector; Gil-Marin, Jhan Kevin; 33
    This paper presents an entropy-based transit tour synthesis (TTS) using fuzzy logic (FL) based on entropy maximization (EM). The objective is to obtain the most probable transit (bus) tour flow distribution in the network based on traffic counts. These models consider fixed parameters and constraints. The costs, traffic counts, and demand for buses vary depending on different aspects (e.g., congestion), which are not captured in detail in the models. Then, as the FL can be included in modeling that variability, it allows obtaining solutions where some or all the constraints do not entirely satisfy their expected value, but are close to it, due to the flexibility this method provides to the model. This optimization problem was transformed into a bi-objective problem when the optimization variables were the membership and entropy. The performance of the proposed formulation was assessed in the Sioux Falls Network. We created an indicator (?) that measures the distance between the model’s obtained solution and the requested value or target value. It was calculated for both production and volume constraints. The indicator allowed us to observe that the flexible problem (FL Mode) had smaller ? values than the ones obtained in the No FL models. These results prove that the inclusion of the FL and EM approaches to estimate bus tour flow, applying the synthesis method (traffic counts), improves the quality of the tour estimation.
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    (33, 33) Moreno-Palacio, Diana P.; Gonzalez-Calderon, Carlos A.; López-Ospina, Héctor; Gil-Marin, Jhan Kevin; Posada-Henao, John Jairo; 33
    This paper presents an improved entropy-based freight tour synthesis (FTS) using fuzzy logic (FL). One approach used in formulating FTS models is entropy maximization, which aims to obtain the most probable freight (trucks) tour flow distribution in a network based on traffic counts. These models consider fixed parameters and constraints. However, the variations in costs, traffic counts, and truck demands depending on human behavior, are not always captured in detail in such models. FL can include such variabilities in its modeling. The flexibility FL provides to the model allows to obtain solutions where some or all the constraints do not entirely satisfy—but are close to—their expected values. Moreover, the modeling approach used based on FL theory is the membership function, specifically the triangular membership function, which is defined by three points corresponding to the vertices. This optimization problem was transformed into a bi-objective problem when the optimization variables are the membership and the entropy. The performance of the proposed formulation was assessed in the Sioux Falls network. To solve the problem, the model was run in General Algebraic Modeling System (GAMS), applying the ? approach, where ? value (?? [0, 1] with steps of 0.01) represents the level of accomplishment that at least one of the constraints (but can be more) gets. The results show that the entropy value decreased as the accomplishment level increased, and this behavior indicates a Pareto frontier, which proves that the optimization problem is bi-objective.
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    (33, 33) Moreno-Palacio, Diana P.; Gonzalez-Calderon, Carlos A.; Lopez-Ospina, Hector; Gil-Marin, Jhan Kevin; Posada-Henao, John Jairo; 33
    The freight system’s complexity and significant impact on urban areas necessitate carefully considering sustainable transportation options. The proposed freight transit tour synthesis (FTTS) model, using fuzzy logic and entropy maximization, analyzes freight and transit systems as a multiclass category, exploring scenarios where buses and trucks share infrastructure. The experiments demonstrate that capacity and maximum cost significantly influence the solutions obtained using fuzzy parameters, with ?-values indicating the best solution. Results may vary depending on available data, highlighting the need to explore solutions for different capacity levels if exceeded. The impact of the maximum cost constraint on tour flows is significant, emphasizing the importance of considering cost in optimizing tour flows. The model’s robustness is evident across various subjective value of time (SVT) scenarios. The application of the FTTS model offers a novel approach to estimating tour flows, incorporating traffic counts and fuzzy parameters for immediate, relevant results. The model's multiclass formulation accurately represents real-world traffic conditions, considering congestion in traffic assignments. Overall, the FTTS model holds promise for optimizing tour flows and shared infrastructure between freight and transit systems, aiding decision-makers in urban transportation planning and resource allocation, ultimately leading to improved traffic management and infrastructure usage efficiency.