Browsing by Author "Codur, Muhammed Yasin"

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Accessibility of Vaccination Centers in COVID-19 Outbreak Control: A GIS-Based Multi-Criteria Decision Making Approach
(MDPI, 2021) Alemdar, Kadir Diler; Kaya, Omer; Codur, Muhammed Yasin; Campisi, Tiziana; Tesoriere, Giovanni
The most important protective measure in the pandemic process is a vaccine. The logistics and administration of the vaccine are as important as its production. The increasing diffusion of electronic devices containing geo-referenced information generates a large production of spatial data that are essential for risk management and impact mitigation, especially in the case of disasters and pandemics. Given that vaccines will be administered to the majority of people, it is inevitable to establish vaccination centres outside hospitals. Site selection of vaccination centres is a major challenge for the health sector in metropolitan cities due to the dense population and high number of daily cases. A poor site selection process can cause many problems for the health sector, workforce, health workers, and patients. To overcome this, a three-step solution approach is proposed: (i) determining eight criteria using from the experience of the advisory committee, (ii) calculating criterion weights using Analytic Hierarchy Process (AHP), and performing spatial analysis of criteria using Geographic Information System (GIS), (iii) assigning potential vaccination centres by obtaining a suitability map and determining service areas. A case study is performed for Bagcilar, Istanbul district, using the proposed methodology. The results show that the suitable areas are grouped in three different areas of the district. The proposed methodology provides an opportunity to execute a scientific and strategic vaccination programme and to create a map of suitable vaccination centres for the countries.
Analysing Traffic Accidents in Terms of Driver Violation Behaviour Types: Machine Learning and Sensitivity Analysis Approaches
(Wiley, 2025) Kuskapan, Emre; Codur, Muhammed Yasin; Dissanayake, Dilum
Traffic accidents have become a major concern for governments, organizations and individuals worldwide due to the material and moral losses they cause. It is possible to reduce this concern by taking into account the research conducted by relevant institutions and organizations in this field. The main objective of this study is to categorize traffic accidents according to driver violation types and analyse them using machine learning algorithms and feature sensitivity to identify the most influential variables in each category. For this purpose, traffic accident reports that occurred in Erzurum province in the last 1 year were used to categorize and classify driver violation behaviour types. Five different machine learning algorithms, namely k-nearest neighbour, support vector machines, naive Bayes, multilayer perception and random forest, were used to examine the success performance of the classification. Among these, 91% successful classification was obtained with the random forest algorithm. Based on the classification obtained from this algorithm, sensitivity analysis was used to reveal the variables that most affect each violation category. The results of the analysis revealed that driver age and vehicle type were the most influential variables for many types of violations. Thanks to this study, the problems were clearly identified by going into the details of driver violation behaviours. At the end of the study, measures to reduce driver violation behaviours were proposed. If the recommendations that can reduce driver behaviour are taken into consideration by transportation authorities and policy makers, traffic accidents can be significantly reduced.
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.
An Artificial Neural Network Model for Highway Accident Prediction: A Case Study of Erzurum, Turkey
(Svenciliste U Zagrebu, Fakultet Prometnih Znanosti, 2015) Codur, Muhammed Yasin; Tortum, Ahmet
This study presents an accident prediction model of Erzurum's Highways in Turkey using artificial neural network (ANN) approaches. There are many ANN models for predicting the number of accidents on highways that were developed using 8 years with 7,780 complete accident reports of historical data (2005-2012). The best ANN model was chosen for this task and the model parameters included years, highway sections, section length (km), annual average daily traffic (AADT), the degree of horizontal curvature, the degree of vertical curvature, traffic accidents with heavy vehicles (percentage), and traffic accidents that occurred in summer (percentage). In the ANN model development, the sigmoid activation function was employed with Levenberg-Marquardt algorithm. The performance of the developed ANN model was evaluated by mean square error (MSE), the root mean square error (RMSE), and the coefficient of determination (R-2). The model results indicate that the degree of vertical curvature is the most important parameter that affects the number of accidents on highways.
Automatic Detection and Classification of Road Defects on a Global-Scale: Embedded System
(Elsevier Sci Ltd, 2025) Kaya, Omer; Codur, Muhammed Yasin
Road networks are created with different pavement designs. In general, flexible pavement is preferred as the most used superstructure type in the world. Road networks with this superstructure experience deterioration for some reasons. These deteriorations take different forms over time and create road defects. The process of identifying defects is very important for the efficiency of the road pavement and traffic components. In this study, the process of automatic detection and classification of road defects occurring in flexible superstructure road networks was carried out with an intelligent system. Road images obtained from eight different countries and 10 different road defects were taken into consideration in the study. YOLOv5 object detection model was used in the detection process of defects. The training processes were completed by creating five different variants and different combinations and YOLOv5l provided the best performance. mAP and F1 Score values were determined as 0.526 and 0.540, respectively. In addition, a global-scale automatic road defect detection system has been developed via new data set consisting of different countries. The developed system is a prototype and has the ability to detect and classify defects occurring in road networks in real-time. To the best of our knowledge, this is the first study to detect the most road defect classes in real time. The created system was tested on a 4.5 km long university campus network as a case study. D00-517, D10-507, D20-50, D30-2, D40-48, D50-18, D60-343, D7025 and D80-9 were detected in real-time by the embedded system. It is clear that the system will be a guide for road network managers by obtaining location information of the identified defects. Detecting, classifying and locating road defects with the developed system will accelerate the maintenance and repair process of road networks and also extend their service life. Road safety and comfort of traffic components using the road network will also be increased. As a result, an example of vehicle-infrastructure (V2I) communication, which is a form of intelligent transportation systems application, is presented in this article.
Automatic Detection of Pedestrian Crosswalk with Faster R-CNN and Yolov7
(MDPI, 2023) Kaya, Omer; Codur, Muhammed Yasin; Mustafaraj, Enea
Autonomous vehicles have gained popularity in recent years, but they are still not compatible with other vulnerable components of the traffic system, including pedestrians, bicyclists, motorcyclists, and occupants of smaller vehicles such as passenger cars. This incompatibility leads to reduced system performance and undermines traffic safety and comfort. To address this issue, the authors considered pedestrian crosswalks where vehicles, pedestrians, and micro-mobility vehicles collide at right angles in an urban road network. These road sections are areas where vulnerable people encounter vehicles perpendicularly. In order to prevent accidents in these areas, it is planned to introduce a warning system for vehicles and pedestrians. This procedure consists of multi-stage activities by sending warnings to drivers, disabled individuals, and pedestrians with phone addiction simultaneously. This collective autonomy is expected to reduce the number of accidents drastically. The aim of this paper is the automatic detection of a pedestrian crosswalk in an urban road network, designed from both pedestrian and vehicle perspectives. Faster R-CNN (R101-FPN and X101-FPN) and YOLOv7 network models were used in the analytical process of a dataset collected by the authors. Based on the detection performance comparison between both models, YOLOv7 accuracy was 98.6%, while the accuracy for Faster R-CNN was 98.29%. For the detection of different types of pedestrian crossings, YOLOv7 gave better prediction results than Faster R-CNN, although quite similar results were obtained.
Effects of Transportation Parameters on Climatic Elements Between Urban and Rural Areas: A Case Study of Erzurum
(Parlar Scientific Publications (p S P), 2019) Codur, Muhammed Yasin; Toy, Suleyman
Roads make it possible for urban people to access public services and materials they need. Enlargement of road surfaces impacts climatic elements in urban areas since they heat excessively air depending heavily on the absorption rate of solar radiation due to lower albedo. Erzurum is an important city in Turley since it is the largest city of East Anatolia Region of the country. Harsh continental climatic characteristics are prevalent and majorly service sector employs more than half of people in the city. The aim of the study is to evaluate the change in mean yearly long term temperature (1960-2016) and rainfall (1929-2016) values obtained from the station operated and maintained regularly by the Turkish State Meteorological Service (MGM) at the city airport about 7 km bird eye from the city center by considering some urban parameters, human population, road and building surfaces and the number of motor vehicles. It was found that human population has been decreasing incessantly in the city until today, but the number of motor vehicles and the total surface area of buildings and the roads is in an increasing trend. In terms of climatic elements (temperature and rainfall), it is seen that mean yearly temperatures are in increasing trend after especially 1992 and rainfall is in a general decreasing trend in especially recent years. Such an apparent change in climatic elements in the city is dependent on a dense and quick transformation trend in the city, which is the reflection of the socioeconomic development policies adopted by central government over at least the last ten years based on land use/valuation or rant. In the example of Erzurum, urban climate is getting warmer and drier which is a condition that must urgently be taken under control for human health and better living environment.
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.
Estimating the Bitumen Ratio to Be Used in Highway Asphalt Concrete by Machine Learning
(Riga Technical Univ-RTU, 2024) Codur, Muhammed Yasin; Kasil, Halis Bahadir; Kuskapan, Emre
Hot mix asphalt, which is frequently used in road pavements, contains bitumen in certain proportions. This bitumen ratio varies according to the layers in the road pavements. The bitumen ratio in each pavement is usually estimated by the Marshall design method. However, this method is costly as well as time-consuming. In this study, the Naive Bayes method, which is a machine learning algorithm, was used to estimate the bitumen ratio practically. In the study, a total of 102 asphalt concrete designs were examined, which were taken from the wearing course, binder course, and asphalt concrete base course and stone mastic asphalt wearing course layers. Each road pavement layer was divided into three different classes according to the bitumen ratios and the algorithm was trained with machine learning. Then the bitumen ratio was estimated for each data set. As a result of this process, the bitumen ratios of the layers were estimated with an accuracy between 75% and 90%. In this study, it was revealed that the bitumen ratio in the road pavement layers could be estimated practically and economically.
An Estimation of Transport Energy Demand in Turkey Via Artificial Neural Networks
(Svenciliste U Zagrebu, Fakultet Prometnih Znanosti, 2019) Codur, Muhammed Yasin; Unal, Ahmet
The transportation sector accounts for nearly 19% of total energy consumption in Turkey, where energy demand increases rapidly depending on the economic and human population growth and the increasing number of motor vehicles. Hence, the estimation of future energy demand is of great importance to design, plan and use the transportation systems more efficiently, for which a reliable quantitative estimation is of primary concern. However, the estimation of transport energy demand is a complex task, since various model parameters are interacting with each other. In this study, artificial neural networks were used to estimate the energy demand in transportation sector in Turkey. Gross domestic product, oil prices, population, vehicle-km, ton-km and passenger-km were selected as parameters by considering the data for the period from 1975 to 2016. Seven models in total were created and analyzed. The best yielding model with the parameters of oil price, population and motor vehicle-km was determined to have the lowest error and the highest R-2 values. This model was selected to estimate transport energy demand for the years 2020, 2023, 2025 and 2030.
Evaluation of Air Quality Index by Spatial Analysis Depending on Vehicle Traffic During the Covid-19 Outbreak in Turkey
(MDPI, 2021) Alemdar, Kadir Diler; Kaya, Omer; Canale, Antonino; Codur, Muhammed Yasin; Campisi, Tiziana
As in other countries of the world, the Turkish government is implementing many preventive partial and total lockdown practices against the virus's infectious effect. When the first virus case has been detected, the public authorities have taken some restriction to reduce people and traffic mobility, which has also turned into some positive affect in air quality. To this end, the paper aims to examine how this pandemic affects traffic mobility and air quality in Istanbul. The pandemic does not only have a human health impact. This study also investigates the social and environmental effects. In our analysis, we observe, visualize, compare and discuss the impact of the post- and pre-lockdown on Istanbul's traffic mobility and air quality. To do so, a geographic information system (GIS)-based approach is proposed. Various spatial analyses are performed in GIS with the statistical data used; thus, the environmental effects of the pandemic can be better observed. We test the hypothesis that this has reduced traffic mobility and improved air quality using traffic density cluster set and air monitoring stations (five air pollutant parameters) data for five months. The results shows that there are positive changes in terms of both traffic mobility and air quality, especially in April-May. PM10, SO2, CO, NO2 and NOx parameter values improved by 21.21%, 16.55%, 18.82%, 28.62% and 39.99%, respectively. In addition, there was a 7% increase in the average traffic speed. In order for the changes to be permanent, it is recommended to integrate e-mobility and sharing systems into the current transportation network.
Evaluation of the Effect on the Layer Thickness of Different Layer Designs
(Scientific Research Center Ltd Belgrade, 2016) Unal, Ahmet; Codur, Muhammed Yasin; Ozcan, Merve Gulfer
Road pavement design is carried out by estimating the expected traffic density during the project. According to these estimations material properties of the layer and layer thickness are determined. Pavement project should be carried out so as not to allow the formation of cracks and large deformation beyond permissible limits. Road pavement consists of gravel with asphalt concrete and base layer thickness. In case of undesired deformations pavement cracks occur in the pavement design and implementation of the scientific method. In this study layer thickness calculation and selection of material properties belonging to the layers used in Erzurum South Ring Road that was built in respect to American Association of State Highway and Transport Officials (AASHTO-86) were analysed. Other pavement designs that could be alternative to the same road were investigated in terms of layer thickness and economic analyses.
Evaluation of Traffic Accidents Happening in Recent Years in Turkey
(Scientific Research Center Ltd Belgrade, 2016) Codur, Muhammed Yasin; Unal, Ahmet; Atalay, Ahmet
Throughout the world, in traffic accidents many people get injured or die. Even though the developed countries are able to fmd a partial solution to this problem, such a case continues to be a major problem in undeveloped countries. The number of traffic accidents in Turkey is increasing depending on the increase in the number of motor-vehicles. However, there may be different reasons for the increment of the number of traffic accidents. Countries are seeking a variety of methods to reduce the frequency of traffic accidents. One of the effective methods to reduce the casualty number of traffic accidents is the construction of divided highways. In this study, common traffic accidents happening in Turkey were evaluated and their causes and measures to be taken to reduce their frequency are investigated.
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.
Geographical Information Systems Aided Traffic Accident Analysis Case Study: City of Erzurum North Ring Road
(Scientific Research Center Ltd Belgrade, 2014) Codur, Muhammed Yasin; Atalay, Ahmet; Tortum, Ahmet; Dogru, Ahmet Osgur
The population growth in developing World, technological development and urbanization is directly connected with the systems of transportation. Although transportation sector offers pretty much alternatives nowadays, people heavily prefer to highway transportation mode in the east of Turkey. The studies which have been done for providing road safety decrease traffic accidents. Geographical Information System (GIS) technology has been a popular tool for accident data and analysis of black spots in highways. Many traffic agencies have been using GIS for accident analysis. Accident analysis studies aim at the identification of high rate accident locations and safety deficient areas on the highways. A case study, using GIS aided traffic accident analysis for The North Ring Road of Erzurum/Turkey were developed by using the historical data, between 2005 and 2012. In conclusion, this study focused on the practicability of the GIS with the maps for traffic safety analysis.
Impact of COVID-19 on Public Transportation Usage and Ambient Air Quality in Turkey
(Sveuciliste U Zagrebu, Fakultet Prometnih Znanosti, 2021) Sahraei, Mohammad Ali; Kuskapan, Emre; Codur, Muhammed Yasin
COVID-19 caused by the SARS-CoV-2 virus is a global health concern due to the quick spread of the disease. In Turkey, the first confirmed COVID-19 case and death occurred on 11 and 15 March 2020, respectively. There is a lack of research on the impact of COVID-19 on public transportation mobility and the Air Quality Index (AQI) around the world. The objective of this research is to consider the impact of COVID-19 on public transportation usage and consequently the AQI level in Turkey. Data collection for the analysis of public transportation usage and the air quality status during pre-lockdown and lockdown was carried out using the public transportation applications Moovit and World's Air Pollution. The results demonstrated that during the lockdown in Ankara and Istanbul, public transportation usage dramatically decreased by more than 80% by the end of March and did not change significantly until the end of May. As regards air quality, the results confirmed that air quality improved significantly during the lockdown. For Ankara and Istanbul, the improvement was estimated at about 9% and 47%, respectively.
Improving the Management of Operations of the E-Scooter Services in Sicily: A First Step of a Descriptive Statistical Survey
(Univ Zilina, 2023) Campisi, Tiziana; Vianello, Chiara; Kuskapan, Emre; Codur, Muhammed Yasin; De Cet, Giulia
The recent pandemic has changed the modal choices of users in the urban context, highlighting in the last two years an increase in the diffusion of electric scooters, which is not homogeneous in the Italian context. At the beginning of 2020, the first e-scooter services were launched in Sicily, with the city of Palermo leading the way. A survey was therefore conducted in Sicily involving approximately 550 regular users of e-scooter services. The descriptive statistical analysis undertaken compared three different periods pre, during and post pandemic, with particular attention to the gender and age gap and different trends in the diffusion of multimodality. The results provide not only some suggestions for the improvement of services by managers but some suggestions to local administrators for implementation of democratic and sustainable planning steps, as well.
Increasing the Visibility of Traffic Signs in Foggy Weather
(Parlar Scientific Publications (p S P), 2019) Codur, Muhammed Yasin; Kaplan, Nur Huseyin
Due to the growth in human population and number of vehicles, the traffic accidents on highways are drastically increased in both developed and developing countries, in recent years. In traffic accidents, the rate of accidents caused by bad weather has an important place. Among adverse weather conditions, driving under foggy conditions is one of the potentially dangerous activity. However, few studies have been reported the effect of foggy weathers in highway traffic accidents. In recent years, the driver support systems, in which several cameras and sensors are used to warn or help the drivers. In this study, it is suggested to use a real time defogging algorithm to preprocess the camera data before reflected to the driver support system screen. By this way, the traffic accidents occured under foggy conditions is expected to be reduced.
Investigation of Physical and Chemical Properties of Bitumen Modified with Waste Vegetable Oil and Waste Agricultural Ash for Use in Flexible Pavements
(MDPI, 2023) Colak, Muhammed Ali; Zorlu, Elif; Codur, Muhammed Yasin; Bas, Fatih Irfan; Yalcin, Ozgen; Kuskapan, Emre
The rapid growth of the world population and the rapid diversification of consumption habits due to technological advancements have increased waste production. An investigation of the effects of biomass products, such as waste vegetable oil and waste agricultural ash, on bitumen's physical and chemical properties was conducted in this study. By recycling biomass products, this study aimed to improve the performance and stability of bituminous hot mixtures, optimize the number of additives, and create more economical designs. Using the Taguchi method, 0%, 2%, 4% by weight of waste vegetable oil and 0%, 3%, and 6% by weight of waste agricultural ash were added to 70/100 penetration pure bitumen with an orthogonal array of L9. For 10, 20, and 30 min, modified bitumen samples were prepared at 170 degrees C, 180 degrees C, and 190 degrees C with a constant mixing speed of 3000 RPM. The samples were tested for penetration, softening point, flash point, rolling thin film oven (RTFOT), FTIR, and Marshall Design stability and flow. Based on the obtained performance statistics, 95% confidence levels were assigned to the predictions. The stability and softening point values decreased as the oil content increased, while flash and penetration values increased. With increasing ash content, stability, flash, and softening point values increased, and penetration values decreased. Compared to oil and ash additives, mixing temperature and time had relatively little effect on the modification process. Overall, the optimum parameter levels were 4% for oil, 0% for ash, 170 degrees C for temperature, and 10 min for time.