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Öğe Analysis of Traffic Accidents to Identify Factors Affecting Injury Severity with Fuzzy and Crisp Techniques(Springer, 2021) Yaman T.T.; Bilgiç E.; Esen M.F.Injury severity in motor vehicle traffic accidents is determined by a number of factors including driver, vehicle, and environment. Airbag deployment, vehicle speed, manner of collusion, atmospheric and light conditions, degree of ejection of occupant’s body from the crash, the use of equipment or other forces to remove occupants from the vehicle, model and type of vehicle have been considered as important risk factors affecting accident severity as well as driver-related conditions such as age, gender, seatbelt use, alcohol and drug involvement. In this study, we aim to identify important variables that contribute to injury severity in the traffic crashes. A contemporary dataset is obtained from National Highway Traffic Safety Administration’s (NHTSA) Fatality Analysis Reporting System (FARS). To identify accident severity groups, we performed different clustering algorithms including fuzzy clustering. We then assessed the important factors affecting injury severity by using classification and regression trees (CRT). The results indicate that the most important factor in defining injury severity is deployment of air-bag, followed by extrication, ejection occurrences, travel speed and alcohol involvement. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.Öğe A model-based statistical classification analysis for karamattepe arrowheads(Ubiquity Press, 2019) Yaman T.T.The Nif Excavation Project is carried out by Elif Tül Tulunayin the southeastern part of Nif Dağı (Mount Nif) located in the eastern province of İzmir, Western Anatolia, Turkey. Since 2006, a total of 483 arrowheads made of iron and bronze have been found in Karamattepe, a territory with important sectors of rich metal finds, especially arrowheads. In the excavation process, Daniş Baykan implemented the first typological study related to arrowheads in 2012. The main objective of the current study is to create an unbiased classification tool which will cover both metric and categorical characteristics for present and future samples gathered from the same region; as well as to resolve the lack of a common typological base by providing a comparison with similar –contemporaneous– arrowheads found in nearby regions. For this purpose, Multinomial Logistic Regression was selected; and in order to provide a more coherent prediction, the Multiple Imputation (MI) Method was employed to complete the missing data. The study aims to develop the interpretation of archaeological data, while setting a unique example of the implication of the stated methods for the specified data. Copyright: © 2019 The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See http://creativecommons.org/licenses/by/4.0/.Öğe Modeling Urban Traffic by Means of Traffic Density Data: Istanbul Case(Springer, 2021) Yaman T.T.; Sezer H.B.; Sezer E.The main goal of the proposed research is to perform a predictive modeling study on Istanbul’s traffic congestion estimation by using traffic density data. Istanbul Metropolitan Municipality (IMM) shared the data, which includes traffic density status according to 336 different routes in Istanbul, at the end of January 2020. Previous studies on the traffic problem in Istanbul have been limited due to a lack of data. Therefore, it is aimed to perform an initial study on predictive modeling for Istanbul’s traffic congestion forecast. As a preliminary result of the analysis, it is seen that the traffic density is low at 93% accuracy for all locations between 00:00–07:00 am. When the locations are examined for other hours, it is seen that there was no traffic density at some locations. In the planned study, intelligent modeling techniques will be performed with identifying out-of-routine situations in traffic flow. Advantages and disadvantages of predicted models will be discussed according to performance indicators such as RMSE and MAPE. The superior model will be selected according to these criteria and it is expected that preferred approach would be a starting point on future research for predictive forecast studies of Istanbul’s traffic congestion. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.Öğe Pythagorean Fuzzy Analytical Network Process (ANP) and Its Application to Warehouse Location Selection Problem(Institute of Electrical and Electronics Engineers Inc., 2020) Yaman T.T.Although new techniques are added to multicriteria decision-making (MCDM) techniques every day, fuzzy applications of current and proven methods also take a large place in the literature. The main subject of this study is to propose an extension of Pythagorean fuzzy sets (PFS), which are useful to overcome the uncertainty in multi-criteria decision processes, to the well-known Analytical Network Process (ANP) technique. For this purpose, an empirical application of the proposed method was carried out in defining criteria weights of the warehouse location selection problem in the medical sector. © 2020 Polish Information Processing Society - as it is since 2011.
