Forecasting the Accident Frequency and Risk Factors: A Case Study of Erzurum, Turkey

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 ANNꞌs method are predicted to be 11.22% until 2030

Chinese postman problem approach for a large-scale conventional rail network in Turkey

Svake se godine željezničke pruge periodički pregledavaju zbog ispitivanja stanja tračnica i osiguranja sigurnosti vlakova u Turskoj. Takvi se projekti moraju obaviti s nekoliko specijaliziranih lokomotiva i skupom opremom. Pronalaženje optimalnog puta takve lokomotive, vozeći na svim prugama, od bitne je važnosti zbog troškova i udaljenosti. U postojećoj praksi, određivanje rute tih lokomotiva uglavnom je manualno i oslanja se prvenstveno na znanje i procjenu stručnjaka. U ovom se radu za rješavanje problema usmjeravanja prometa inspekcijskom lokomotivom, predlaže "problem kineskog poštara" - Chinese Postman Problem (CPP). Cilj je minimizirati ukupnu prijeđenu udaljenost pronalaženjem najkraćeg pravca. Predloženi je model primijenjen na veliki problem u stvarnom svijetu. U usporedbi s postojećim stanjem predloženim se modelom smanjuje prijeđeni put za 20,76%.Every year, railways are inspected periodically to examine the status of rail tracks and ensure the safety of train operations of the railroad networks in Turkey. These inspection projects must be performed by several specialized machines with large and expensive equipment. Finding the optimum route of inspection machines that control through travelling all lines is critically important in terms of cost and distance. In current practice, determining the route of these machines is largerly manual and primarily relies on the knowledge and judgment of experts. This paper proposes Chinese Postman Problem (CPP) to solve the inspection machine routing problem. The objective is to minimize the total travel distance on the railroads by finding the shortest route. The proposed model is applied to a large-scale real world problem. Compared to the current practice the proposed approach significantly outperforms the reduced objective value by 20,76%

Study Using Machine Learning Approach for Novel Prediction Model of Liquid Limit

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 (R2), 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

Study Using Machine Learning Approach for Novel Prediction Model of Liquid Limit

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 (R2), 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