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In a world with growing environmental and energy concerns, effective energy use is crucial. Since our country largely imports natural gas, efficient usage is vital for the national economy. Accurate consumption estimates will enhance investment efficiency, optimize gas purchase agreements, and minimize economic losses from excess supply, thereby balancing future supply and demand. In this study, natural gas consumption data of Kayseri province on a monthly basis between the years 2015-2022, obtained from Republic of Türkiye Energy Market Regulatory Authority (EPDK), was used, and a consumption estimate was made for the year 2023. Auto Regressive Integrated Moving Average (ARIMA -Box-Jenkins), Gray Prediction and Artificial Neural Networks (ANNs) methods, which are widely accepted in the literature, were used as forecasting methods. The methods used were compared in terms of Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and R² error performance measures, and it was seen that the multiplicative decomposition based grey GM( 1,1) model, which gave the most successful result with an R² value of 0.926, was suitable for estimation. With the appropriate multiplicative decomposition based GM(1,1) model, monthly natural gas consumption estimation of Kayseri province was made for the year 2023. [ABSTRACT FROM AUTHOR]

The secondhand vehicle market in Türkiye has always been active. In the secondhand vehicle market, information such as brand, model, and fuel type can be analyzed, and this information can be made useful. Prices vary according to the various features of the vehicles. Machine learning techniques can predict prices and help users set prices when selling or buying vehicles. Price prediction falls under regression. Since the number of secondhand vehicles is quite high, big data systems are used. Apache Spark and its machine learning library are quite useful for this. Linear regression, decision tree regression, random forest regression, GBT regression, and isotonic regression algorithms are used for price prediction. The random forest algorithm achieved the highest success for the price prediction with 21435.09 RMSE and 0.887 R2 values. As a result of the statistical tests performed to check the significant difference between the RMSE and R2 values obtained with the random forest algorithm and other algorithms, it is concluded that the results obtained with the random forest algorithm are statistically better than other algorithms. The random forest algorithm gives better results because the algorithm performs training over multiple decision trees, its flexibility, and strong hyperparameters. [ABSTRACT FROM AUTHOR]

Although railway systems have been widely used in public transportation for several years, crew scheduling research focuses on airline operations. Since the crew costs constitute the critical part of the operational costs in railway public transportation, the crew scheduling problem gains more importance. Crew scheduling aims to assign all trips to duty packages in a timeline while satisfying several labour working rules, company requirements, and operational restrictions. Because of the complexity of real-world conditions, crew scheduling problems are NP-complete. In this study, the crew scheduling problem was addressed, and a constraint programming based column generation algorithm was proposed as a solution approach. A column generation algorithm was employed to solve problems having a considerable number of variables. In this study, the master problem was designed as a set partitioning problem that aimed to assign all trips to duty while minimizing the cost. In the subproblem, new column(s) satisfying all constraints were generated and added to the master problem to reduce the cost. To analyse the performance of the proposed approach, an integer programming model of the problem was developed. An extensive computational study is presented to compare the results of these two modelling approaches via different real-life problem instances. [ABSTRACT FROM AUTHOR]

Increasing the quality of service is more important than ever for companies today. At the same time, the factors affecting service quality in the service sector vary according to the type and structure of the service. In the urban public transport sector, the situation is not different from other service sectors. EN 13816 Standard of Service Quality Management in Passenger Transport was constituted to develop a quality approach in the urban public transport sector in 2002. NF 281 Passenger Transportation Standard, which was created with the aim of defining the criteria of public transport service and certificate at the point of suitability, has been using EN 13816 as a guide resource since 2003. According to the EN 13816 standard, two types of service quality measurements are carried out: customer oriented and service provider oriented. Service provider-oriented service quality measurement is called as performance measurement. In this study, the design of service provider-oriented service quality measurement model, in other words the design of performance measurement model for the T1 line of Kayseri Transportation Inc. Light Rail System was discussed. With the design of performance measurement model, service commitments, service commitments measurement and evaluation system, service level computation methods, service commitments management elements have been developed. These improvements have been made taking into account the requirements of the NF 281 standard. [ABSTRACT FROM AUTHOR]