Browsing by Author "Codur, Merve Kayaci"

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Arc Routing Problem Approach for Reducing Exhaust Gas Emission in Road Transportation: A Case Study of Erzurum
(Parlar Scientific Publications (p S P), 2019) Codur, Merve Kayaci; Yilmaz, Mustafa; Codur, Muhammed Yasin
One of the sectors that have the greatest impact on greenhouse gas emissions around the world is the road transportation sector. For this reason, the studies carried out for the logistic activities of the enterprises and entrepreneurs in a way that will cause the least harm to the environment are accelerating day by day. Some processes are routinely carried out on the road to ensure both transportation safety and environmental regulation. One of these processes is the snow plowing process, which is frequently performed in winter months. There are several heavy-duty vehicles that using for snow plowing operations on the roads, and these vehicles are seriously releasing exhaust gases. Therefore, in this study, the measures reducing the adverse effects of exhaust emissions on the environment arising from road vehicles were analyzed by arc routing problem approach. As one of these measures, transportation planning of the vehicles traveling on the road network and the completion of the process by vehicles in the shortest distance is expected. The optimum routes of vehicles traveling on the roads are critically important in terms of cost, distance and environmental effects. In this study, multiple vehicle variants of k-Chinese Postman Problem (k-CPP), which is one of the most frequently used approaches of arc routing problems, are addressed. A new type called Balanced k-Chinese Postman Problem (Bk-CPP) that balances the workload among vehicles and that has an important role for real-world applications is developed. A bi-objective integer-programming model is presented. There are two objectives; to minimize the total distance covered, and to balance the workload in terms of distance traveled among vehicles as much as possible. The proposed Bk-CPP model is applied to a network of a part of Ataturk University campus in Turkey for snow plowing operations. Additionally, well-known arc routing test instances that are widely used in the literature are solved to demonstrate the effectiveness and applicability of the proposed Bk-CPP model. The results show that the optimum routes significantly outperform to reduce the amount of exhaust gas emissions.
The Development of Decarbonisation Strategies: A Three-Step Methodology for the Suitable Analysis of Current EVCS Locations Applied to Istanbul, Turkey
(MDPI, 2021) Kaya, Omer; Alemdar, Kadir Diler; Campisi, Tiziana; Tortum, Ahmet; Codur, Merve Kayaci
One of the solutions to reduce environmental emissions is related to the deployment of electric vehicles (EVs) with sustainable energy. In order to be able to increase the number of electric vehicles in circulation, it is important to implement optimal planning and design of the infrastructure, with particular reference to areas equipped with charging stations. The suitable analysis of the location of current electric vehicle charging stations (EVCSs) is the central theme of this document. The research focused on the actual location of the charging stations of five major EVCS companies in the province by selecting Istanbul as the study area. The study was conducted through a three-step approach and specifically (i) the application of the analytical hierarchy process (AHP) method for creating the weights of the 6 main and 18 secondary criteria that influence the location of EVCSs; (ii) a geospatial analysis using GIS considering each criterion and developing the suitability map for the locations of EVCSs, and (iii) application of the technique for order preference by similarity to ideal solution (TOPSIS) to evaluate the location performance of current EVCSs. The results show that the ratio between the most suitable and unsuitable areas for the location of EVCSs in Istanbul and the study area is about 5% and 4%, respectively. The results achieved means of improving sustainable urban planning and laying the basis for an assessment of other areas where EVCSs could be placed.
Enhancing Energy Management in Railway Transportation: A High-Accuracy Prediction Approach Using Ensemble Machine Learning
(Wiley, 2026) Kuskapan, Emre; Codur, Muhammed Yasin; Codur, Merve Kayaci; Dissanayake, Dilum
Predicting energy consumption helps countries make strategic decisions in many critical areas such as energy management, economic development, energy security, environmental sustainability and infrastructure investments. Therefore, accurate and reliable energy consumption predictions are vital to ensure the sustainability and prosperity of countries. This study aims to contribute to the proper planning of transportation policies and energy management by successfully predicting T & uuml;rkiye's railway energy consumption. In this direction, energy prediction values were obtained from 18 different machine learning methods using the country's railway line length, number of passengers, freight amount and energy consumption values from 1977 to 2024. To further strengthen the results obtained with these methods, bagging, boosting, stacking and blending ensemble learning methods were utilized. With the improvements, the R-squared value was increased up to 0.9667 and energy predicting was achieved with very high accuracy. Based on the results obtained from this study, it is possible to provide investment planning more efficiently. In addition, the implementation of energy management strategies, infrastructure planning and sustainable energy policies will be provided more efficiently as a result of obtaining more successful results by using ensemble machine learning methods instead of traditional machine learning methods for energy consumption predictions in different sectors.
Environmental Effects of Driver Distraction at Traffic Lights: Mobile Phone Use
(MDPI, 2023) Alemdar, Kadir Diler; Codur, Merve Kayaci; Codur, Muhammed Yasin; Uysal, Furkan
The transportation demands of people are increasing day by day depending on the population, and the number of vehicles in traffic is causing various problems. To meet the energy needs of vehicles, there is a huge burden on countries in terms of fossil fuels. In addition, the use of fossil fuels in vehicles has a serious impact on environmental pollution. Various studies have been carried out to prevent unnecessary fuel consumption and emissions. Behavior of drivers, who are important components of traffic, are carefully examined in the context of this subject. Driver distraction causes various environmental problems as well as traffic safety issues. In this study, the negative situations that arise as a result of drivers waiting at traffic lights dealing with their mobile phones are discussed. Roadside observations are made for drivers at considered intersections in Erzurum Province, Turkey. As a result of these observations, delays at selected intersections due to mobile phone use are calculated. Unnecessary fuel consumption and emissions due to delays are also analyzed. An annual fuel consumption of approximately 177.025 L and emissions of 0.294 (kg) NOX and 251.68 (kg) CO2 occur at only selected intersections. In addition, a second roadside observation is made in order to analyze driver behavior and the most preferred type of mobile phone usage is determined. It is seen that drivers mostly exhibit the "Talking" and "Touchscreen" action classes. Considering the economic conditions and environmental pollution sensitivities of countries, attempts have been made to raise awareness about fuel consumption and emissions at traffic lights.
Exploring the Urban Form and Compactness: A Case Study of Multan, Pakistan
(MDPI, 2022) Nadeem, Muhammad; Khaliq, Nayab; Akhtar, Naseem; Al-Rashid, Muhammad Ahmad; Asim, Muhammad; Codur, Merve Kayaci; Baig, Farrukh
Sustainable development has become an immense challenge, one further complicated by rapid population growth in developing countries. Therefore, analyzing the existing compactness of urban areas is essential for guiding future urban development. Most of the previous research on urban compactness has been conducted in developed countries, whereas limited research has been conducted on urban compactness in developing countries. This study fills this research gap and contributes to the current body of knowledge by offering empirical evidence of compactness measurement based on the existing urban form using Multan city as its context. Multan is a metropolitan city in the growing phase, so measuring its compactness for the promotion of sustainable development is crucial. For this research study, various indicators are adopted from the literature, such as land cover changes, density, land use, road network, congestion index, walkability index, and shape performance index, in order to evaluate compactness. The above-mentioned indicators were analyzed using ArcMap and ERDAS IMAGINE software. This study concludes that Multan city presently lies between compactness and dispersion. To achieve full compactness, highly dense vertical development with a better public transport network should be encouraged. In addition, the prevailing building regulations should be revised to increase the floor area ratio, and incentives should be devised for developers to promote vertical infill development. Moreover, there is an emerging need to formulate and implement compact city policies. By retaining the compact character of Multan city, sustainable development will be promoted. Ultimately, this research study would be a valuable resource for urban planners, decision-makers, and relevant authorities in proposing future compactness policies for sustainable development. This research can be applied to other cities with similar demographic characteristics, population, area, geographical conditions, and structure to that of Multan.
Forecasting the Accident Frequency and Risk Factors: A Case Study of Erzurum, Turkey
(Univ Osijek, Tech Fac, 2022) Sahraei, Mohammad Ali; Codur, Merve Kayaci; Codur, Muhammed Yasin; Tortum, Ahmet
Nowadays, life is intimately associated with transportation, generating several issues on it. Numerous works are available concerning accident prediction techniques depending on independent road and traffic features, while the mix parameters including time, geometry, traffic flow, and weather conditions are still rarely ever taken into consideration. This study aims to predict future accident frequency and the risk factors of traffic accidents. It utilizes the Generalized Linear Model (GLM) and Artificial Neural Networks (ANN) approaches to process and predict traffic data efficiently based on 21500 records of traffic accidents that occurred in Erzurum in Turkey from 2005 to 2019. The results of the comparative evaluation demonstrated that the ANN model outperformed the GLM model. The study revealed that the most effective variable was the number of horizontal curves. The annual average growth rates of accident occurrences based on the ANN.s method are predicted to be 11.22% until 2030.
Pedestrian Safety at Signalized Intersections: Spatial and Machine Learning Approaches
(Elsevier Sci Ltd, 2022) Kuskapan, Emre; Sahraei, Mohammad Ali; Codur, Merve Kayaci; Codur, Muhammed Yasin
Introduction: The major goal of the present research is to determine hotspot areas by the generation of a geospatial model and develop a model associated with pedestrian-vehicle crash injuries (severe, moderate, slight) at signalized intersections in Erzurum, Turkey.& nbsp;Methodology: This study used the comprehensive algorithm in Artificial Neural Network (ANN). Data from 197 crashes injury (2015-2019) at 57 intersections depending on the mix of variables such as driver, road and vehicle characteristics, and environment data were collected.& nbsp;Results: Within the four candidate models, the first one including pedestrian density, level of education, traffic congestion, type of vehicle, presence of bus stop, age, and gender had the lowest RMSE and MAE values and the greatest R-2 value. Lastly, sensitivity analyses were conducted to evaluate the impact of independent parameters.& nbsp;Conclusions: The importance of the study lies in the expected outcomes to assist the experts to address the pedestrian-vehicle crash risk factors by conducting appropriate countermeasures for facilities management/improvement.
Prediction of Transportation Energy Demand by Novel Hybrid Meta-Heuristic ANN
(Pergamon-Elsevier Science Ltd, 2022) Sahraei, Mohammad Ali; Codur, Merve Kayaci
Road automobiles are deemed one of the major resources of energy consumption throughout cities. To realize and design sustainable urban transport, it is essential to comprehend as well as evaluate interactions among a set of elements, which form transport impacts and behaviors. The goal of the current research was to propose a hybrid algorithm, Artificial Neural Network (ANN)-Genetic Algorithm (ANN GA), ANN-Simulated Annealing (ANN-SA), and Particle Swarm Optimization (ANN-PSO) to better optimize the coefficients for predicting the energy demand based on the several predictor variables (1975 e2019) i.e., GDP, year, vehicle-km, population, oil price, passenger-km, and ton-km in Turkey. Eleven combinations of all predictor variables were selected and then compared with real data. The outcomes exposed that the proposed ANN-PSO technique based on the GDP, population, ton-km outperforms the other two models. It is anticipated that this research can be useful for developing extremely productive and applicable planning regarding transportation energy policies.(c) 2022 Elsevier Ltd. All rights reserved.
Study Using Machine Learning Approach for Novel Prediction Model of Liquid Limit
(MDPI, 2022) Nawaz, Muhammad Naqeeb; Qamar, Sana Ullah; Alshameri, Badee; Karam, Steve; Codur, Merve Kayaci; Nawaz, Muhammad Muneeb; Azab, Marc
The liquid limit (LL) is considered the most fundamental parameter in soil mechanics for the design and analysis of geotechnical systems. According to the literature, the LL is governed by different particle sizes such as sand content (S), clay content (C), and silt content (M). However, conventional methods do not incorporate the effect of all the influencing factors because traditional methods utilize material passing through a # 40 sieve for LL determination (LL40), which may contain a substantial number of coarse particles. Therefore, recent advancements suggest that the LL must be determined using material passing from a # 200 sieve. However, determining the liquid limit using # 200 sieve material, referred to as LL200 in the laboratory, is a time-consuming and difficult task. In this regard, artificial-intelligence-based techniques are considered the most reliable and robust solutions to such issues. Previous studies have adopted experimental routes to determine LL200 and no such attempt has been made to propose empirical correlation for LL200 determination based on influencing factors such as S, C, M, and LL40. Therefore, this study presents a novel prediction model for the liquid limit based on soil particle sizes smaller than 0.075 mm (# 200 sieve) using gene expression programming (GEP). Laboratory experimental data were utilized to develop a prediction model. The results indicate that the proposed model satisfies all the acceptance requirements of artificial-intelligence-based prediction models in terms of statistical checks such as the correlation coefficient (R-2), root-mean-square error (RMSE), mean absolute error (MAE), and relatively squared error (RSE) with minimal error. Sensitivity and parametric studies were also conducted to assess the importance of the individual parameters involved in developing the model. It was observed that LL40 is the most significant parameter, followed by C, M, and S, with sensitivity values of 0.99, 0.93, 0.88, and 0.78, respectively. The model can be utilized in the field with more robustness and has practical applications due to its simple and deterministic nature.
Z-Numbers MCDM Approach for Personnel Selection at Institutions of Higher Education for Transportation
(MDPI, 2024) Gottwald, Dalibor; Chocholac, Jan; Codur, Merve Kayaci; Cubranic-Dobrodolac, Marjana; Yazir, Kubra
Personnel evaluation and selection is an essential part of modern business. Appropriate candidate selection can significantly contribute to companies in terms of increased profit, good culture, reputation, reduced costs, etc. This paper addresses the personnel evaluation and selection problem at the University of Pardubice, Faculty of Transport Engineering (UPCE). Since this is a typical ranking alternative problem where multiple criteria affect the decision, the Z-numbers-based Alternative Ranking Order Method Accounting for the two-step Normalization (AROMAN) is applied. Four Ph.D. candidates are assessed, and the most appropriate is selected to be employed by the UPCE. The Z-numbers fuzzy AROMAN method ranks Ph.D. candidate number four as the most appropriate alternative. To investigate the stability and sensitivity of the Z-numbers fuzzy AROMAN method, the values of parameters beta and lambda used in the mathematical calculations of the method were changed. The results of sensitivity analysis revealed that the obtained solution is stable. To confirm the robustness of the proposed approach, a comparative analysis is performed. Simple Additive Weighting (SAW), Weighted Product Model (WPM), and Z-number fuzzy TOPSIS were applied. Besides, we applied the fuzzy inferior ratio method as well. The results confirm the high robustness of the proposed Z-numbers fuzzy AROMAN method.