Politi, Ruti
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Politi, Ruti R.
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Email Address
ruti.politi@ieu.edu.tr
Main Affiliation
05.03. Civil Engineering
Status
Current Staff
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Sustainable Development Goals
SDG data is not available
Documents
5
Citations
32
h-index
2
Documents
5
Citations
26
Scholarly Output
5
Articles
5
Views / Downloads
29/609
Supervised MSc Theses
0
Supervised PhD Theses
0
WoS Citation Count
2
Scopus Citation Count
3
WoS h-index
1
Scopus h-index
1
Patents
0
Projects
0
WoS Citations per Publication
0.40
Scopus Citations per Publication
0.60
Open Access Source
3
Supervised Theses
0
| Journal | Count |
|---|---|
| Canadian Journal of Civil Engineering | 1 |
| IEEE Access | 1 |
| Sustainability | 1 |
| Sustainability (Switzerland) | 1 |
| Transportation Letters-The International Journal of Transportation Research | 1 |
Current Page: 1 / 1
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5 results
Scholarly Output Search Results
Now showing 1 - 5 of 5
Article Developing a Sustainable Traffic Management Framework Using Machine Learning Models for Fuel Consumption Minimization at Closely Spaced Intersections(Mdpi, 2025) Politi, Ruti R.; Tanyel, SerhanClosely 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.Article Citation - WoS: 1Citation - Scopus: 1Investigation 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).Article 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.Article Prediction of Signalized Intersection Delays Using Genetic Programming, XGBoost, and Clustering Approach(Taylor & Francis Ltd, 2026) Politi, RutiPrecise estimation of average control vehicle delay is a critical parameter for the efficient operation and optimization of signalized intersections. This paper presents predictive models for estimating delays at signalized intersections that are developed by using Genetic Programming (GP) and Extreme Gradient Boosting (XGBoost). The combination of GP's ability to derive interpretable mathematical expressions and XGBoost's reliability in handling nonlinear relationships provides a comprehensive framework for accurate delay prediction. In order to capture data-driven patterns, the dataset was separated using k-means clustering and grouped-based segmentation. Both six k-means clusters and four different groups were compared with traditional delay models (HCM, and Ak & ccedil;elik). The results indicate that the models demonstrated good performance to estimate delays at signalized intersections under varying saturation degrees, reflecting different traffic volumes. These findings suggest that machine learning-based delay estimation models can significantly advance both theoretical research and practical applications in traffic management for signalized intersections.Article Citation - WoS: 1Citation - Scopus: 2Minimizing Delay at Closely Spaced Signalized Intersections Through Green Time Ratio Optimization: a Hybrid Approach With K-Means Clustering and Genetic Algorithms(IEEE-inst Electrical Electronics Engineers inc, 2025) Politi, Ruti R.; Tanyel, SerhanClosely spaced intersections play a key role in traffic flow management. This study aims to model different traffic related parameters to minimize the delay of a closely spaced intersection by optimizing the green time ratio with the help of the genetic algorithm. The factors influencing optimization were selected as the distance between two adjacent intersections, cycle time, degree of saturation, green time ratio, volume, and the queue length-to-distance ratio, which is considered the Index parameter. The dataset was calibrated, validated, and used to simulate the analysis of traffic scenarios using SIDRA Intersection. To provide a clearer analysis, the k-means clustering algorithm was applied to divide the distances into three clusters. Among these clusters, the distance range between 110 and 160 meters is identified as the transition zone. The optimal green time ratio to minimize delay value for closely spaced intersection clusters was determined within a range of 0.58 to 0.69. To ensure a more comprehensive analysis, these values are used to examine their impact on delays. For this reason, the scenarios were restructured again with SIDRA using the newly optimized values to evaluate whether there is any reduction in the traffic-related parameters. The delay values and their temporal fluctuations showed significant improvements with this hybrid approach. The optimized green time ratios reduced delay, degree of saturation, and CO2 emissions by 8.95%, 8%, and 4.72% at the downstream intersection, and by 6.86%, 6.16%, and 7.09% at the upstream intersection, respectively.
