Yazar "Calisici, Mustafa" seçeneğine göre listele

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    Öğe
    An Energy Analysis of Road Transportation in Turkey
    (World Scientific And Engineering Acad And Soc, 2009) Cansiz, Omer F.; Cubuk, M. Kursat; Calisici, Mustafa
    Due to comparatively high access to personal cars and passenger carriers for either intracity or intercity travel, the road transport is the most preferred and used one among the other types of transportation in Turkey. Although, a variety of statistics is available related to usage of roadways, there has not enough attention been paid yet to energy efficiency of using roadways. However, being aware of the importance of the energy efficiency is essential for planning future transportation investments particularly for the countries like Turkey which have limited sources to allocate, especially considering recent increases in energy costs. In this work, an energy efficiency of using road transportation in Turkey is studied by taking into account of freight and passenger movements separately. Data covering the period from 1988 to 2005 are analyzed. It is observed that although the usage of road transport continuously increases, the energy intensity continuously decreases as well. This indicates a declining tendency in the energy usage per unit, in spite of the increase in the road usage. Simply, it reflects an increase in the energy efficiency. In a similar fashion, the energy intensity for the passenger movement shows a declining tendency as well, regardless of increasing passenger movements. It is also observed that those values are significantly low with regard to values in developed countries. This study can shed light on current status and can give a new direction for future planning and policies in highways transportation of Turkey.
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    Öğe
    Use of Artificial Neural Network to Estimate Number of Persons Fatally Injured in Motor Vehicle Accidents
    (World Scientific And Engineering Acad And Soc, 2009) Cansiz, Omer F.; Calisici, Mustafa; Miroglu, M. Melik
    The paper demonstrates an artificial intelligence method known as the Artificial Neural Network (ANN) approach based on supervised neural networks to estimate the number of persons fatally injured in motor vehicle accidents. In order to analyze a data set related to fatal accidents, the Artificial Neural Network Estimating Fatal Accident (ANNEFA) model is developed by using social and traffic-related variables, population and motor-vehicle registrations. To obtain the best form of ANNEFA, various ANN models having different transfer functions, different number of neurons and different train algorithms are designed. The ANNEFA model formed with fourteen neurons, tansig transfer function and Levenberg-Marquardt training algorithm provides the best fit to training and test data. Fluctuations in variables used in historical data are reflected in results of the ANNEFA model. Estimates of the ANNEFA model compared with the results of Revised Smeed Equation (RSE) reconstituted from Smeed Equation in accord with the USA Data Set. Thus, this study provides a benchmark for predicting fatality in motor-vehicle accidents in the form of a numerical and graphical comparison between results of the ANNEFA and RSE. The results indicate that the ANN model is a proper approach in predicting fatalities in motor-vehicle crashes.
  • [ N/A ]
    Öğe
    Use of artificial neural network to estimate number of persons fatally injured in motor vehicle accidents
    (2009) Cansiz, Omer F.; Calisici, Mustafa; Miroglu, M. Melik
    The paper demonstrates an artificial intelligence method known as the Artificial Neural Network (ANN) approach based on supervised neural networks to estimate the number of persons fatally injured in motor vehicle accidents. In order to analyze a data set related to fatal accidents, the Artificial Neural Network Estimating Fatal Accident (ANNEFA) model is developed by using social and traffic-related variables, population and motorvehicle registrations. To obtain the best form of ANNEFA, various ANN models having different transfer functions, different number of neurons and different train algorithms are designed. The ANNEFA model formed with fourteen neurons, tansig transfer function and Levenberg-Marquardt training algorithm provides the best fit to training and test data. Fluctuations in variables used in historical data are reflected in results of the ANNEFA model. Estimates of the ANNEFA model compared with the results of Revised Smeed Equation (RSE) reconstituted from Smeed Equation in accord with the USA Data Set. Thus, this study provides a benchmark for predicting fatality in motor-vehicle accidents in the form of a numerical and graphical comparison between results of the ANNEFA and RSE. The results indicate that the ANN model is a proper approach in predicting fatalities in motor-vehicle crashes.