Yazar "Dogan, Erdem" için listeleme
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Analysis And Comparison Of Long Short-Term Memory Networks Short-Term Traffic Prediction Performance
Dogan, Erdem (FAC TRANSPORT SILESIAN UNIV TECHNOLOGY, 2020)Long short-term memory networks (LSTM) produces promising results in the prediction of traffic flows. However, LSTM needs large numbers of data to produce satisfactory results. Therefore, the effect of LSTM training set ... -
Analysis of the relationship between LSTM network traffic flow prediction performance and statistical characteristics of standard and nonstandard data
Dogan, Erdem (WILEY, 2020)The effectiveness of road traffic control systems can be increased with the help of a model that can accurately predict short-term traffic flow. Therefore, the performance of the preferred approach to develop a prediction ... -
Application of Smeed and Andreassen accident models for Turkey: Various scenario analyses
Akgungor, Ali Payidar; Dogan, Erdem (Gazi Univ, Fac Engineering Architecture, 2008)In 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 ... -
Comparison Of Different Approaches In Traffic Forecasting Models For The D-200 Highway In Turkey
Dogan, Erdem; Korkmaz, Ersin; Akgungor, Ali Payidar (Fac Transport Silesian Univ Technology, 2018)Short-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. ... -
Estimating The Number Of Traffic Accidents, Injuries And Fatalities In Turkey Using Adaptive Neuro-Fuzzy Inference System
Akgungor, Ali Payidar; Korkmaz, Ersin; Dogan, Erdem (Scientific Research Center Ltd Belgrade, 2016)This 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 ... -
Estimation Of Car Ownership In Turkey Using Artificial Bee Colony Algorithm
Korkmaz, Ersin; Dogan, Erdem; Akgungor, Ali Payidar (Scientific Research Center Ltd Belgrade, 2016)This 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 ... -
Estimation of delay and vehicle stops at signalized intersections using artificial neural network
Dogan, Erdem; Akgungor, Ali Payidar; Arslan, Turan (Univ Rijeka, Fac Engineering, 2016)Delay 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 ... -
Modelling Weekend Traffic With Weather Conditions Using Various Equation Type And Differential Evolution Algorithm
Dogan, Erdem; Akgungor, Ali Payidar; Korkmaz, Ersin (Scientific Research Center Ltd Belgrade, 2016)In 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 ... -
Optimizing a fuzzy logic traffic signal controller via the differential evolution algorithm under different traffic scenarios
Dogan, Erdem; Akgungor, Ali P. (Sage Publications Ltd, 2016)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 ... -
Short-Term Traffic Flow Prediction Using Artificial Intelligence With Periodic Clustering And Elected Set
Dogan, Erdem (SVENCILISTE U ZAGREBU, FAKULTET PROMETNIH ZNANOSTI, 2020)Forecasting short-term traffic flow using historical data is a difficult goal to achieve due to the randomness of the event. Due to the lack of a solid approach to short-term traffic prediction, the researchers are still ...
