Browsing by Author "Politi, R.R."

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Developing a Sustainable Traffic Management Framework Using Machine Learning Models for Fuel Consumption Minimization at Closely Spaced Intersections
(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Politi, R.R.; Tanyel, S.
Closely spaced intersections can be specified as special types of intersections with short-distance characteristics that are generally located in urban areas. This study aimed to develop a sustainable transportation framework of machine learning algorithms to predict and minimize fuel consumption as a measure of environmental impact at closely spaced intersections. In the theoretical framework, this study incorporates key traffic parameters such as left-turn-lane length, cycle time, distance between intersections, left-turn movement ratio, and traffic volume fluctuations to model fuel consumption. In this context, different scenarios were modeled and compared with SIDRA Intersection (version 6.1), which is a well-known traffic analysis and intersection modeling software, by using partial least square regression (PLSR), polynomial support vector machine (PSVM), and artificial neural network (ANN) models to conduct a comparative analysis of their applicability. The results demonstrated that the ANN model best captured fuel consumption variations across different key influencing factors. Among all models, cycle time showed the highest sensitivity, highlighting its critical impact; the optimization of left-turn-lane length and cycle time is performed using Particle Swarm Optimization (PSO) to minimize the impact of left-turns on fuel consumption. These enhancements promote more efficient and environmentally friendly traffic management. The integration of the predictive and optimized PSO-ANN model establishes a foundation for optimizing intersection performance. The findings indicate that an overall improvement of 8.9% in fuel consumption is achieved by evaluating the optimized parameters under varying traffic volumes. The proposed framework supports sustainable signalized intersection management by improving fuel efficiency and reducing environmental impact. © 2025 by the authors.
Citation - WoS: 1
Citation - Scopus: 1
Investigation of the Effect of Geometric Irregularities on Capacity of Traffic Circles by Using Partial Least Squares Regression Method
(Canadian science publishing, 2024) Avşar, Y.Ö.; Yıldırım, Z.B.; Politi, R.R.; Tanyel, S.
This paper examined the impact of geometric irregularities on the intersection capacity at traffic circles. A new empirical capacity relation was proposed to predict the capacity of the traffic circles as a function of geometric elements, exit and circulating flows. Within this scope, first, the relationship between the vehicles entering from the minor approach and the circulating flow from the turning movement in the traffic circle was examined based on the K-Means cluster analysis method. The analysis was created in accordance with an exponential relationship between entry and circulating flow. Second, two clusters were selected by the partial least squares regression method to improve the model’s effectiveness. Lastly, to validate the model, “leave-one-out” cross-validation was used to select the components that maximize the model’s predictive ability. The results show that geometric parameters of a traffic circle create different effects on capacity, especially in different circulating flow conditions. © 2024 The Author(s).