Yazar "Cansiz, Omer F." seçeneğine göre listele

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    Crash test simulation of a modified thrie-beam high containment level guardrail under NCHRP Report 350 TL 4-12 conditions
    (Inderscience Enterprises Ltd, 2006) Cansiz, Omer F.; Atahan, Ali O.
    This paper describes details of a computer simulation study performed on a modified thrie-beam high containment level guardrail designated as SGR09b. Because the SGR09b guardrail system is the only high containment guardrail system passing the NCHRP Report 350 TL4 requirements in its class, developing an accurate finite element model for this guardrail is deemed to be a significant contribution towards enhancement of computer-simulated virtual roadside safety. research. For this reason, a detailed finite element model of the SGR09b guardrail system has been developed and subjected to 8000 kg single unit truck impact under NCHRP Report TL4 conditions. The fidelity of the simulation study was evaluated using the full-scale crash test results. As in the full-scale crash test, in the finite element simulation study, the guardrail system successfully contained and redirected the 8000 kg single unit truck. Based on the crash test results, it was determined that the finite element models for both the SGR09b guardrail system and the 8000 kg single unit truck are fairly accurate and can be used with confidence in further computer-simulated virtual roadside safety research.
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    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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    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.
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    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.
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    Using artificial neural network to predict collisions on horizontal tangents of 3D two-lane highways
    (2011) Cansiz, Omer F.; Easa, Said M.
    The purpose of this study is mainly to predict collision frequency on the horizontal tangents combined with vertical curves using artificial neural network methods. The proposed ANN models are compared with existing regression models. First, the variables that affect collision frequency were investigated. It was found that only the annual average daily traffic, section length, access density, the rate of vertical curvature, smaller curve radius before and after the tangent were statistically significant according to related combinations. Second, three statistical models (negative binomial, zero inflated Poisson and zero inflated negative binomial) were developed using the significant variables for three alignment combinations. Third, ANN models are developed by applying the same variables for each combination. The results clearly show that the ANN models have the lowest mean square error value than those of the statistical models. Similarly, the AIC values of the ANN models are smaller to those of the regression models for all the combinations. Consequently, the ANN models have better statistical performances than statistical models for estimating collision frequency. The ANN models presented in this paper are recommended for evaluating the safety impacts 3D alignment elements on horizontal tangents.