City and Regional Planning / Şehir ve Bölge Planlama
Permanent URI for this collectionhttps://hdl.handle.net/11147/4274
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Conference Object Making Accident Data Compatible With Its-Based Traffic Management: Turkish Case(Intelligent Transport Systems, 2010) Duvarcı, Yavuz; Geçer Sargın, Feral; Kumova, Bora İsmail; Çınar, Ali Kemal; Selvi, ÖmerOne of the most important reasons of the high rate of accidents would largely lend itself to ineffective data collection and evaluation process since the necessary information cannot be obtained effectively from the traffic accidents reports (TAR). The discord and dealing with non-relevant data may appear at four levels: (1) Country and Cultural, (2) Institutional and organizational, (3) Data collection, (4) Data analysis and Evaluation. The case findings are consistent with this knowledge put forward in the literature; there is a transparency problem in coordination between the institutions as well as the inefficient TAR data, which is open to manipulation; the problem of under-reporting and inappropriate data storage prevails before the false statistical evaluation methods. The old-fashioned data management structure causes incompatibility with the novel technologies, avoiding timely interventions in reducing accidents and alleviating the fatalities. Transmission of the data to the interest agencies for evaluation and effective operation of the ITS-based systems should be considered. The problem areas were explored through diagnoses at institutional, data collection, and evaluation steps and the solutions were determined accordingly for the case city of Izmir.Article Citation - WoS: 14Citation - Scopus: 15A Superstatistical Model of Vehicular Traffic Flow(Elsevier Ltd., 2016) Koşun, Çağlar; Özdemir, SerhanIn the analysis of vehicular traffic flow, a myriad of techniques have been implemented. In this study, superstatistics is used in modeling the traffic flow on a highway segment. Traffic variables such as vehicular speeds, volume, and headway were collected for three days. For the superstatistical approach, at least two distinct time scales must exist, so that a superposition of nonequilibrium systems assumption could hold. When the slow dynamics of the vehicle speeds exhibit a Gaussian distribution in between the fluctuations of the system at large, one speaks of a relaxation to a local equilibrium. These Gaussian distributions are found with corresponding standard deviations 1/β. This translates into a series of fluctuating beta values, hence the statistics of statistics, superstatistics. The traffic flow model has generated an inverse temperature parameter (beta) distribution as well as the speed distribution. This beta distribution has shown that the fluctuations in beta are distributed with respect to a chi-square distribution. It must be mentioned that two distinct Tsallis q values are specified: one is time-dependent and the other is independent. A ramification of these q values is that the highway segment and the traffic flow generate separate characteristics. This highway segment in question is not only nonadditive in nature, but a nonequilibrium driven system, with frequent relaxations to a Gaussian.Article Citation - WoS: 2Citation - Scopus: 2Soft Computing and Regression Modelling Approaches for Link-Capacity Functions(Czech Technical University in Prague, 2016) Koşun, Çağlar; Tayfur, Gökmen; Çelik, Hüseyin MuratLink-capacity functions are the relationships between the fundamental traffic variables like travel time and the flow rate. These relationships are important inputs to the capacity-restrained traffic assignment models. This study investigates the prediction of travel time as a function of several variables V/C (flow rate/capacity), retail activity, parking, number of bus stops and link type. For this purpose, the necessary data collected in Izmir, Turkey are employed by Artificial Neural Networks (ANNs) and Regression-based models of multiple linear regression (MLR) and multiple non-linear regression (MNLR). In ANNs modelling, 70% of the whole dataset is randomly selected for the training, whereas the rest is utilized in testing the model. Similarly, the same training dataset is employed in obtaining the optimal values of the coefficients of the regression-based models. Although all of the variables are used in the input vector of the models to predict the travel time, the most significant independent variables are found to be V/C and retail activity. By considering these two significant input variables, ANNs predicted the travel time with the correlation coefficient R = 0:87 while this value was almost 0.60 for the regression-based models.