This study proposes an Artificial Neural Network (ANN) model and a Genetic Algorithm (GA) model to estimate the number of accidents (A), fatalities (F) and injuries (I) in Ankara, Turkey, utilizing the data obtained between 1986 and 2005. For model development, the number of vehicles (N), fatalities, injuries, accidents and population (P) were selected as model parameters. In the ANN model, the sigmoid and linear functions were used as activation functions with the feed forward‐back propagation algorithm. In the GA approach, two forms of genetic algorithm models including a linear and an exponential form of mathematical expressions were developed. The results of the GA model showed that the exponential model form was suitable to estimate the number of accidents and fatalities while the linear form was the most appropriate for predicting the number of injuries. The best fit model with the lowest mean absolute errors (MAE) between the observed and estimated values is selected for future estimations. The comparison of the model results indicated that the performance of the ANN model was better than that of the GA model. To investigate the performance of the ANN model for future estimations, a fifteen year period from 2006 to 2020 with two possible scenarios was employed. In the first scenario, the annual average growth rates of population and the number of vehicles are assumed to be 2.0 % and 7.5%, respectively. In the second scenario, the average number of vehicles per capita is assumed to reach 0.60, which represents approximately two and a half‐fold increase in fifteen years. The results obtained from both scenarios reveal the suitability of the current methods for road safety applications.

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To cite this article: Ali Payidar Akgüngör (2008) A new delay parameter dependent on variable analysis periods at signalized intersections. part 1: Model development,

The study presents an investigation into traffic based noise pollution in the city of Kirikkale, Turkey. For this purpose, traffic noise levels were measured at 15 intersections across the city during three peak times ‐ morning (08:00–09:00), noon (12:30–13:30) and evening (17:00–18:00) hours. The comparison of Leq values against the limit values of the Turkish Noise and Control Regulations for Settlement Zones showed that Leqvalues exceeded the limits at all stations. A linear regression analysis performed between the Leq and logarithm of total traffic volume (log Q) produced a coefficient of determination of 0.52. A multi regression analysis carried out between the Leq and four different vehicle types resulted in a correlation coefficient of 0.74. The correlation matrix indicated that the highest correlation was found for trucks/buses with r = 0.92. The spatial maps of traffic noise created by the Kriging method under ArcView GIS displayed that there seemed to be significant differences in the spatial variation of traffic noise across the city. In order to reduce traffic based noise levels within the city some useful suggestions were presented.

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This study aims at optimizing fuzzy logic controller (FLC) triangle membership functions (MFs) for different traffic volumes via differential evolution (DE). To achieve this goal, a new FLC with a red time limiter, which actually calculates green time and the extension time of traffic movement phase, is developed to control an intersection. Subsequently, this FLC is optimized with two levels, namely Level-1 and Level-2. Level-1 searches each fuzzy class’s minimum and maximum values (α and β) that generate the lowest average delay per vehicle with DE. Using DE Level-2 inherits Level-1 ranges and reshapes the MFs to explore lower delay values computed by Level-1. The proposed method is tested with nine different traffic scenarios. For each scenario, 15 different headways are applied for a four-leg isolated intersection. The results indicate that the intersection average performance is increased up to 52%, 48%, and 14% at 800, 1600, and 2400 veh/h total intersection volumes, respectively, after Level-1 optimizations. They also reveal that intersection control produces higher delay values in only four scenarios after Level-2 procedures. Consequently, it is shown that the DE has significant potential to optimize FLCs at the intersection signal control. In addition, tuning fuzzy class ranges is found to be more critical than the MF reshaping process in traffic control via FLCs.

Chemical additives are very important in determining the behavioral characteristics of self-compacting concrete. For this reason, determining the building materials that make up the chemical structure of selfcompacting concrete and the interactions of these materials is of great importance. The present study pertains to the effects of the use of different chemical admixtures (high-range water-reducing, i.e., superplasticizer, hydration accelerating, air-entraining, shrinkage reducing, and hydration heat reducing admixtures) on the fresh and hardened properties of self-compacting concrete. The in uence of using a single one or a hybrid combination of the air-entraining, hydration-accelerating, heat-reducing, and shrinkage-reducing admixtures on the mechanical properties of fresh and hardened SCC was investigated through a set of tests. For this purpose, sixteen different SCC mixtures with different combinations of chemical additives were prepared and tested. The properties of fresh concrete were examined as well as the compressive and tensile strengths of the mixtures. SCC mixtures with shrinkage-reducing admixtures were evaluated in terms of shrinkage development. The effect of the use of admixtures was found to be more pronounced on the early-age concrete strength. The use of any type of additive in addition to the shrinkage reducing admixture increased the speed of ow of fresh concrete.