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Öğe A hybrid traffic controller system based on flower pollination algorithm and type-2 fuzzy logic optimized with crow search algorithm for signalized intersections(Springer, 2024) Korkmaz, Ersin; Akgungor, Ali PayidarIn this study, a hybrid traffic signal control (HTSC) system based on phase and time optimization was developed. The Flower Pollination Algorithm (FPA) approach was used for phase optimization, while Type-2 Fuzzy Logic, optimized with the Crow Search Algorithm (CSA), was utilized for time optimization. The hybrid system's performance was investigated using nine different traffic conditions and four different intersection geometries. The hybrid system was compared with three controller systems which are a fixed-time signal controller, a signal controller based on the FPA approach (FPA_TSC), and the optimized Type-1 fuzzy logic signal controller (Type-1 FL-TSC). The HTSC approach achieved the best performance with about 32% improvement over the fixed-time traffic controller and it showed 5% and 6% better performance than the FPA_TSC and Type-1 FL-TSC, respectively. Considering the performance of the new hybrid system, it is effective in minimizing delays and driver dissatisfaction occurring from signalization. It also contributes to the reduction of emissions and fuel consumption. The HTSC approach can be used as an alternative signal control method in the control of intersections with high traffic volume due to its fast and effective performance.Öğe AN ANALYSIS OF TYPE I DILEMMA ZONE AT SIGNALISED INTERSECTIONS(Fac Transport Silesian Univ Technology, 2021) Akgungor, Ali Payidar; Mercan, Elif ZahideIntersections, for vehicles coming from different directions, are conflict points in road networks. When a driver approaching a signalised intersection encounters the yellow light, he/she is in a dilemma either to safely stop or to pass through the intersection during clearance time. The decision to stop or to pass may change depending on some factors such as duration of yellow light, deceleration and acceleration rate, width of intersection, speed and length of vehicle, etc. This study aims to put forth the effects of some related factors affecting the length of the Type I dilemma zone. To perform this study, five factors including vehicle speed, maximum deceleration rate, perception-reaction time, clearance time, the total intersection width-vehicle length were considered and a total of 648 different traffic cases were investigated. The study results showed that the Type I dilemma zone length increased with the increase of speed, total intersection width-vehicle length and perception-reaction time, but decreased with the increase of clearance time and deceleration rate.Öğe Application of Smeed and Andreassen accident models for Turkey: Various scenario analyses(Gazi Univ, Fac Engineering Architecture, 2008) Akgungor, Ali Payidar; Dogan, ErdemIn this study, accident prediction models for Turkey were developed by using the historical data, between 1986 and 2005, including population (P), the number of vehicles (N), accidents (C), injuries (I), and fatalities (D). In the models development, the structural form of Smeed and Andreassen Models were employed. However, Smeed Model was developed in different point of view so that this model was called as Smeed Similarity Model. The number of accident, injury and fatalities in Turkey which is on the way of a full member of European Union, were tried to estimate under different three scenarios until the year of 2010. Both models were compared in terms of percent difference (PD), mean absolute percent errors (MAPE), and root mean square errors (RMSE). Despite that Andreassen model for the years between 1986 and 2005 had errors lower than Smeed Similarity model, for future estimates latter gave more plausible results with scenarios.Öğe Comparison of artificial bee colony and flower pollination algorithms in vehicle delay models at signalized intersections(SPRINGER LONDON LTD, 2020) Korkmaz, Ersin; Akgungor, Ali PayidarDelay is a significant research topic since it includes indicators such as travel quality, lost time and fuel consumption. Furthermore, the delay is used for optimization of traffic control systems and determination of the level of service at signalized intersections. Therefore, researchers have focused on accurate estimation of delay. The objective of this study is to simply and accurately estimate the delay and evaluate the performance of the proposed approaches which are artificial bee colony (ABC) and flower pollination algorithms (FPA). In this study, ABC and FPA have been used to develop different delay models which are linear, semi-quadratic, quadratic and power forms. Analysis period (T), the green ratio (g/C; effective green to cycle length) and the degree of saturation (x = v/c; volume to capacity) are used as input parameters while developing the models. The results of present models are compared to estimations obtained from analytical models which are Highway Capacity Manual and Australian (Akcelik) delay models. Semi-quadratic form yielded to best results in terms of coefficient of determination (R-2), mean square error and mean absolute error. Additionally, FPA approach showed better performance than ABC approach finding the optimal solution in the lower number of iterations.Öğe Comparison Of Different Approaches In Traffic Forecasting Models For The D-200 Highway In Turkey(Fac Transport Silesian Univ Technology, 2018) Dogan, Erdem; Korkmaz, Ersin; Akgungor, Ali PayidarShort-term traffic estimations have a significant influence in terms of effectively controlling vehicle traffic. In this study, short-term traffic forecasting models have been developed based on different approaches. Seasonal autoregressive integrated moving average (SARIMA), artificial bee colony (ABC) and differential evolution (DE) algorithms are the techniques used in the optimization of models, which have been developed by using observation data for the D-200 highway in Turkey. 80% of the data were used for training, with the remaining data used for testing. The performances of the models were illustrated with mean absolute errors (MAEs), mean absolute percentage errors (MAPEs), the coefficient of determination (R2) and the root-mean-square errors (RMSEs). It is understood that all the models provided consistent and useful results when the developed models were compared with the statistical results. In the models created separately for two lanes, the R2 values of the models were calculated to be approximately 92% for the right lane, which is generally used by heavy vehicles, and 88% for the left lane, which is used by less traffic. Based on the MAE and RMSE values, the model developed by the ABC algorithm gave the lowest error and showed more effective performance than the other approaches. Thus, the ABC model showed that it is appropriate for use on other highways in Turkey.Öğe Delay estimation models for signalized intersections using differential evolution algorithm(Academic Publication Council, 2017) Korkmaz, Ersin; Akgungor, Ali PayidarDelay is an important parameter in the optimization of traffic signals and the determination of the level of service (LOS) of a signalized intersection since it directly reflects the lost travel time and fuel consumption. The accurate estimation of delay is, therefore, an important issue. The purpose of this study is to develop new delay models with less input parameters by using one of the artificial intelligent techniques. In this research, three types of differential evolution delay estimation models (DEDEM), i.e. linear, exponential and quadratic, were developed using differential evolution (DE) approach. In developing of the delay models, the green ratio (g/C effective green to cycle length) and the degree of saturation (x=v/c; volume to capacity) were considered. The first one changed from 0.35 to 0.60, the second one varied between 0.7 and 1.4. The model outputs were compared analytically to the HCM and Australian (Akcelik) delay models. The study results illustrated that R2, Mean Square Error (MSE) and Mean Absolute Error (MAE) values of DEDEMquadratic, which are 0.97, 207.98, 12.12 respectively, were better than analytical delay models and other types models. As a result, the quadratic form of DEDEM model can be used as an alternative estimation model for delay, and DE approach can be utilized as a model-fitting algorithm as well.Öğe Effects of the sole or combined use of chemical admixtures on properties of self-compacting concrete(Springernature, 2021) Sevim, Ozer; Kalkan, Ilker; Demir, Ilhami; Akgungor, Ali PayidarChemical 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 self-compacting 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 influence 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 flow of fresh concrete.Öğe Energy Demand Estimation in Turkey According to Road and Rail Transportation: Walrus Optimizer and White Shark Optimizer Algorithm-Based Model Development and Application(Mdpi, 2024) Korkmaz, Ersin; Dogan, Erdem; Akgungor, Ali PayidarTransport energy demand (TED) forecasting is a crucial issue for countries like Turkey that are dependent on external resources. The accuracy and effectiveness of these forecasts are extremely important, especially for the strategies and plans to be developed. With this in mind, different forms of forecasting models were developed in the present study using the Walrus Optimizer (WO) and White Shark Optimizer (WSO) algorithms to estimate Turkey's energy consumption related to road and railway transportation modes. Additionally, another objective of this study was to examine the impacts of different transport modes on energy demand. To investigate the effect of demand distribution among transport modes on energy consumption, model parameters such as passenger-kilometers (P-km), freight-kilometers (F-km), carbon dioxide emissions (CO2), gross domestic product (GDP), and population (POP) were utilized in the development of the models. It was found that the WO algorithm outperformed the WSO algorithm and was the most suitable method for energy demand forecasting. All the developed models demonstrated a better performance level than those reported in previous studies, with the best performance achieved by the semi-quadratic model developed with the WO, showing a 0.95% MAPE value. Projections for energy demand up to the year 2035 were established based on two different scenarios: the current demand distribution among transport modes, and a demand shift from road to rail transportation. It is anticipated that the proposed energy demand models will serve as an important guide for effective planning and strategy development. Moreover, the findings suggest that a balanced distribution among transport modes will have a positive impact on transport energy and will result in lower energy requirements.Öğe Estimating The Number Of Traffic Accidents, Injuries And Fatalities In Turkey Using Adaptive Neuro-Fuzzy Inference System(Scientific Research Center Ltd Belgrade, 2016) Akgungor, Ali Payidar; Korkmaz, Ersin; Dogan, ErdemThis study proposes Adaptive Neuro-Fuzzy Inference System (ANFIS) models to estimate the number of accidents, injuries and fatalities in Turkey. In the model development, population (P) and the number of vehicles (N) are selected as model parameters. Three different ANFIS structure models are developed using the data covering from 2000 to 2014. Developed models' results are statistically compared to observed values for training and test data in terms of root mean square errors (RMSE), mean absolute percentage errors (MAPE) and coefficient of determination (R-2). The results of the ANFIS models showed that they was suitable to estimate the number of accidents, injuries and fatalities. To investigate the performance of ANFIS models for future estimations, a ten-year period from 2015 to 2024 is considered. Thus, future values of population was obtained from the projection of Turkish Statistical Institute (TSI) and the vehicle ownership rate is expected to reach 0.4 by 2024. Therefore, population and the number of vehicles are considered to reach approximately 85 and 34 million, respectively. The results obtained from future estimations reveal the suitability of ANFIS approach for road safety applications.Öğe Estimating transportation energy demand in Turkey using the artificial bee colony algorithm(Pergamon-Elsevier Science Ltd, 2017) Sonmez, Mustafa; Akgungor, Ali Payidar; Bektas, SalihIn this study, three different mathematical models were proposed to estimate transportation energy demand of Turkey using the artificial bee colony algorithm. In the development of the models, gross domestic product, population and total annual vehicle-km were taken as parameters. For transportation energy demand estimations, linear, exponential and quadratic forms of mathematical expressions were used. A 44-year-old historical data from 1970 to 2013 were utilized for the training and testing stages of the models. The performances of the models were then evaluated by six different global error measurement approaches. The models that were developed were used in two possible scenarios to forecast transportation energy demand of Turkey for a 21-year period from 2014 to 2034. Artificial bee colony algorithm revealed the suitability of the optimization method for transportation energy planning and policy developments in Turkey. Furthermore, the results obtained from scenarios indicated that the energy demand of Turkey will be double that of 2013 by 2034. (C) 2017 Elsevier Ltd. All rights reserved.Öğe Estimation Of Car Ownership In Turkey Using Artificial Bee Colony Algorithm(Scientific Research Center Ltd Belgrade, 2016) Korkmaz, Ersin; Dogan, Erdem; Akgungor, Ali PayidarThis study proposes Artificial Bee Colony (ABC) models to estimate the number of cars in Turkey. In other words, car ownership is defined the number of cars per 1000 people. In the models development, population (P), per capita Gross Domestic Product (GDP) as dollars and fuel prices as Petrol, Diesel and Lpg were selected as model parameters. Two different ABC models were developed using the data covering from 2004 to 2015. According to fuel type, the coefficients of models were determined for each fuel type. Therefore, the sum of number of cars for each fuel type presented car ownership in Turkey. Developed models' results were statistically compared to observed values in terms of root mean square errors (RMSE), mean absolute percentage errors (MAPE) and coefficient of determination (R-2). The results of the ABC models showed that they were suitable to estimate the number of cars. To investigate the performance of ABC models for future estimations, a ten-year period from 2016 to 2025 was considered. Thus, future values of population were obtained from the projection of Turkish Statistical Institute (TSI) and the projections of other parameters, per capita GDP and fuel price, were executed according to current growth curve. The results obtained from future estimations reveal the suitability of ABC approach for determination of car ownership.Öğe Estimation of delay and vehicle stops at signalized intersections using artificial neural network(Univ Rijeka, Fac Engineering, 2016) Dogan, Erdem; Akgungor, Ali Payidar; Arslan, TuranDelay and number of vehicle stops are important indicators that define the level of service of a signalized intersection. Therefore, they are usually considered for optimizing the traffic signal timing. In this study, ANNs are employed to model delay and the number of stops estimation at signalized intersections. Intersection approach volumes, cycle length and left turn lane existence were utilized as input variables since they could easily be obtained from field surveys. On the other hand, the average delay and the number of stops per vehicle were used as the output variables for the ANNs models. Four-leg intersections were examined in this study. Approach volumes including turning volumes are randomly generated for each lane of these intersections, then the traffic simulation program was run 196 times with each generated data. Finally, average delay and the number of stops per vehicle were obtained from the simulations as outputs. In this study, various network architectures were analyzed to get the best architecture that provides the best performance. The results show that the ANNs model has potential to estimate delays and number of vehicle stops.Öğe Estimation Of Passenger-Kilometer And Tonne-Kilometer Values For Highway Transportation In Turkey Using The Flower Pollination Algorithm(Fac Transport Silesian Univ Technology, 2018) Korkmaz, Ersin; Akgungor, Ali PayidarWithin the scope of this study, intercity passenger and freight movements in Turkey are estimated by using the flower pollination algorithm (FPA), while demand forecasts are performed on transport systems considering possible future scenarios. Since the passenger and freight transport system in Turkey mainly involves road transport, passenger-kilometer and tonne-kilometer values of this system are estimated. By relying on three independent parameters, models were developed in three different forms: linear, force and semi-quadratic. Population (P) between 1990 and 2016, gross domestic product per capita (GDPperC) in US dollars and the number of vehicles were used as input parameters for the development of the models. When the passenger-kilometer models were created, the number of cars, buses and minibuses that are predominantly used for passenger transportation was preferred for the number of vehicles, while the number of trucks and vans used for cargo transportation were taken into consideration in the tonne-kilometer models. The coefficients of the models were determined by FPA optimization, with models developed to estimate passenger-kilometer and tonne-kilometer values. The model results were compared with the observation values and their performance was evaluated. Two different scenarios were created to estimate passenger-kilometer and tonne-kilometer in 2030. Parallel to the increase in population and welfare level, it is predicted that demand for passenger and freight transport will increase. In particular, the higher input parameter values in Scenario 1 significantly affect the increase in demand, leading to a demand increase of around 50%. In addition, the FPA has demonstrated effective performance in predicting the demand for passenger and freight transport and that it can be used in many different areas.Öğe Flower pollination algorithm approach for the transportation energy demand estimation in Turkey: model development and application(Taylor & Francis Inc, 2018) Korkmaz, Ersin; Akgungor, Ali PayidarThis study proposes a new optimization technique to estimate the Transportation Energy Demand (TED) employing the Flower Pollination Algorithm (FPA). The TED estimation models were developed based on three parameters, which are Annual Vehicle-Km (AVK), Gross Domestic Product per Capita (GDPperC), and Carbon-dioxide (CO2) emission according to the linear, power and quadratic forms. These three parameters were determined by the WEKA data mining software program among nine parameters. Randomly selected 80% of historical data for 47 years, from 1970 to 2016, were used for the training of the algorithm, and the remains were used in the testing stage of the models. The performances of the models were evaluated according to six different statistical criteria. Transportation energy demand forecasts by 2035 were carried out using three different scenarios using the TED estimation models. According to the scenarios, it is predicted that the transportation energy demand in Turkey will have doubled by 2035 in comparison with 2016. The FPA approach has been successfully applied in the development of the TED estimation models. The most important impact of this study is to help the creation of strategic action plans for energy policies in the transport sector and to contribute to the more efficient use of limited energy resources in the country.Öğe Investigating Parameter Interactions with the Factorial Design Method: Webster's Optimal Cycle Length Model(Univ Osijek, Tech Fac, 2018) Akgungor, Ali Payidar; Korkmaz, ErsinAccurate estimation of cycle length is an important factor in the performance of a signalized intersection. Cycle length is determined by employing some parameters such as arrival flow, number of phase, lost time etc., but each parameter has different effects on a cycle length model. If the effects of parameters and their interactions in the cycle length model are known, the performance of the model can be effectively increased. In this study, the sensitivity of optimal cycle length model proposed by Webster and its parameters were analysed with the factorial design method. The reason for selecting this model is that the model has still been used in signal timing practice and has lead many studies of researchers over 50 years. The evaluation of sensitivity analysis shows that while arrival flow as single parameter has a major effect on the optimal cycle length model, the remaining single parameters of the model (i.e., the number of phase in a cycle length, saturation flow and lost time) have secondary importance. Additionally, two parameter interactions of arrival flow-saturation flow have major effect on the model results. For three parameter interactions, the number of phase-arrival flow-saturation flow interaction has a slightly larger effect than the other three-parameter interactions. As a result, the factorial design method is an effective tool to determine the importance of the model parameters for researchers, and it can be employed to other traffic engineering applications.Öğe Modeling of Traffic Signals Syncronisation Effect For Vehicle Emission Reduction(Gazi Univ, 2008) Akay, M. Emin; Akgungor, Ali PayidarIn this study, the change of vehicle emissions in four signalized intersections and along the five streets in Millet Boulevard of Kirikkale are modeled according to scenarios of interrupted flow and green (flow) wave. For this purpose, volumes and traveling speeds along the streets and waiting times (delay) in intersections are obtained by vehicle counting methods in these locations. Vehicle based emissions for present interrupted conditions are determined. Afterwards, to examine the effect of green wave, reductions in vehicle emissions are modeled for 50 km/h constant speed and stopping in only one signalized intersection case. It was observed that green wave scenario reduced the vehicle emissions by 74.0 % in general. It was also observed that by green wave scenario, emissions in streets were increased by 3.8 % while emissions during stopping and starting in intersections were decreased by 18.2 % and 22.6 %, respectively. The most important advantage of green wave scenario is that CO pollutant, the biggest share among pollutants, were decreased by 76.8 %.Öğe Modelling Weekend Traffic With Weather Conditions Using Various Equation Type And Differential Evolution Algorithm(Scientific Research Center Ltd Belgrade, 2016) Dogan, Erdem; Akgungor, Ali Payidar; Korkmaz, ErsinIn weekends, amount of passenger car traffic is usually higher than weekday because of the activity-based traveling on some highways. Forecasting of this traffic, might help to local authorities to take safety precautions decisions on a road segments. This study aims to compose models to forecast weekend traffics using weather conditions and average weekday traffic variables. For this aim, two main models were composed: The Saturday traffic model and the Sunday traffic model. The Saturday traffic model variables are mean weekday daily traffic, maximum temperatures of Saturday and precipitations. The Sunday model is a linear model with only one variable: the predicted traffic values from the Saturday traffic model. In the modeling Saturday traffics, six-month (from January to June) data, which belongs to year 2015 and Ankara Kinkkale highway in Turkey, were used and 2014-March data were used for testing the models. The used temperatures were normalized and the precipitations data were involved as logical (0 or 1) inputs in models. To find best equation type for Saturday traffic model, four various equation forms were selected: (1) Linear, (2) polynomial-1, (3) polynomial-2, (4) multiplicative equation from. The linear and polynomial-1 have three, multiplicative has four, and polynomial -2 equation has five coefficients need to be determinate. Differential evolution algorithm was utilized to determinate best fitted values for these coefficients. Performance of the models were calculated using mean square error and coefficient of determination. The model with the polynomial-2 equation has minimum errors for the modelling stage and R-2 value is around 0.80. The model with the polynomial-2 showed the best performance on testing stage (R-2=0.96). These results show that the weekend traffic is related to weather conditions and it can be modeled convenient equation form and differential evolution algorithm.Öğe Optimizing of phase plan, sequence and signal timing based on flower pollination algorithm for signalized intersections(SPRINGER, 2020) Korkmaz, Ersin; Akgungor, Ali PayidarThe purpose of this study is to develop a control system that optimizes the phase plan, sequence and signal timing using the flower pollination algorithm (FPA). At the same time, it is aimed to improve the fixed-time control system with the optimum cycle length search approach based on the differential evolution algorithm. The applicability and performances of these two control systems were examined in 15 different traffic situations according to 4 different intersection geometries. Fixed-time and optimized fuzzy logic traffic controller (FLC) developed by Dogan were used as the reference control systems in performance comparison. The optimum cycle length search system can achieve approximately 18% improvement over the fixed-time system, but showed lower performance than the FPA and FLC control systems. The FPA system has proven its applicability by achieving the best performance with about a 30% improvement compared to the fixed-time system and about 3% improvement compared to the FLC system. The FPA approach, which has a fast and effective performance, has been found to be an alternative method for intersection control, and it is foreseen that it can increase the intersection capacity and reduce the negative effects such as delay and fuel consumption.Öğe Optimum cycle length models using atom search optimization and grasshopper optimization algorithms(Wiley, 2022) Korkmaz, Ersin; Akgungor, Ali PayidarA fixed-time traffic control system is widely used to manage the traffic flow at intersections. Cycle length has an important effect on the performance of the fixed-time control system and the Webster model is widely used in the literature to determine the cycle length. However, when the traffic flow ratio (Y) approaches 1, the Webster model loses its effectiveness and cannot determine the cycle length when Y is above 1. In the scope of this study, it is aimed to develop models that can predict cycle length for all traffic conditions in which are Y <= 1 and Y >= 1. Two different heuristic algorithms, atom search optimization (ASO) and grasshopper optimization algorithm (GOA) were used in the development of models and the performances of these algorithms were demonstrated. The efficiency of the developed models has been demonstrated by comparing them with both Webster's model and VISTRO optimization program. Models developed in exponential, power, and quadratic forms have been able to predict cycle lengths with lower delay values by showing better performance than Webster and VISTRO. In addition, statistical results show that the ASO approach is more successful than the GOA approach.Öğe Restrained shrinkage cracking of self-consolidating concrete roads(Walter De Gruyter Gmbh, 2018) Akgungor, Ali Payidar; Sevim, Ozer; Kalkan, Ilker; Demir, IlhamiThe present study is dedicated to investigate the liability of continuously reinforced concrete pavement (CRCP) cast with self-consolidating concrete (SCC) to restrained shrinkage cracking and the values of restraint stresses in these pavements. SCC, which is becoming increasingly popular due to its several superiorities over conventionally vibrated concrete (CVC), has higher amounts and rates of shrinkage compared to CVC. The higher risk of restrained shrinkage cracking of SCC is a great cause of concern in pavement construction as the penetration of water, chemicals, and salts increases the risk of corrosion of reinforcement. In the present study, an analytical restraint stress expression was developed for typical CRC pavements by modifying the restraint stress equation developed previously for RC beams. Using this equation, the restraint stresses induced to the longitudinal reinforcement by the rigid pavement, cast with CVC or SCC, were calculated for eight different example sections. These restraint stress values were found to reach up to 50% of the limit stresses of bars, allowed by the design guidelines, when the pavement is cast with SCC. The amounts of longitudinal reinforcement used in typical CRCP roads were found to be more critical when the pavement is cast with SCC.
