Yazar "Bagdatli, Muhammed Emin Cihangir" seçeneğine göre listele

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    A decision model based on gene expression programming for discretionary lane-changing move
    (Taylor & Francis Ltd, 2024) Bagdatli, Muhammed Emin Cihangir; Choghtay, Raz Mohammad
    This study focuses on modeling Discretionary Lane-Changing (DLC), which accounts for the majority of lane-change moves in traffic flows. A binary decision model for lane-changing moves was improved with the method of Gene Expression Programming (GEP). The decision to prefer GEP is due to its high performance in a variety of engineering solutions in the literature. The GEP model was trained with Next Generation SIMulation (NGSIM) trajectory data gathered at the I-80 Freeway in Emeryville, California, and then tested with data gathered at the U.S. Highway 101 in LA, California. The test results indicate that the model made decisions of change lane with 92.98% accuracy, and do not change lane with 99.65% accuracy. A sensitivity analysis was also conducted to discover potential limits of the performance of the GEP model. The performance of this model was compared with other high-performance decision models developed with the NGSIM's DLC data in the literature and with TransModeler's gap acceptance model. This comparison indicates that the GEP model is the most successful decision model for discretionary lane-changing moves. The GEP model has a high potential to be applied in DLC decision support systems in (semi-) automated vehicles, as well as traffic simulation software.
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    Investigating lane-changing moves of vehicles departing from signalized junction
    (Taylor & Francis Ltd, 2022) Bagdatli, Muhammed Emin Cihangir; Dokuz, Ahmet Sakir; Honul, Ayetullah
    Discretionary Lane-Changing (DLC) possesses different characteristics under various traffic conditions. Therefore, in order to model DLC moves in the correct manner, they need to be approached separately for various conditions. This study has examined the DLC moves of vehicles, which depart from signalized junction. It is observed that a large number of vehicles move to a neighboring lane in order to travel under better traffic conditions, after the traffic lights turn green. There are many parameters, which motivate the vehicles to change lanes at this stage. This study has focused on discovering these parameters. Data was collected through field work for this purpose. Thereinafter, the impact value of each parameter has been discovered using four separate feature selection methods and has been sorted. Fuzzy Cognitive Maps have been developed with the obtained impact values. The models have been validated using the field data and have been compared with each other.
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    Modeling discretionary lane-changing decisions using an improved fuzzy cognitive map with association rule mining
    (Taylor & Francis Ltd, 2021) Bagdatli, Muhammed Emin Cihangir; Dokuz, Ahmet Sakir
    The discretionary lane-changing process consists of two phases. The first phase is decision making on lane-changing, and the second phase is the execution of this decision. The first phase has a complex structure that is affected by many parameters. In this phase, some parameters are present that affect lane-changing directly, while some other indirect parameters motivate drivers to perform lane-changing. This study focuses on discovering the parameters that prompt drivers to change lanes. The parameters determined as a result of the interviews with the drivers were examined in the field study. Then, the impact of the parameters for lane-changing were discovered using association rule mining and the proposed Significant Association Features Extractor (SigAFE) algorithm. Fuzzy Cognitive Map (FCM) discretionary lane-changing decision models were developed using the impact values that were discovered using the SigAFE algorithm. The performances of the models were revealed with the actual data of the field study.
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    Sustainability Impact of Bus Priority Treatments in Small-Scale Cities
    (Sage Publications Inc, 2023) Bagdatli, Muhammed Emin Cihangir; Ipek, Fatima
    Bus priority treatments (BPTs) are among the effective alternative solutions that provide significant advantages to urban transport. The literature has revealed some of the benefits of BPTs in various aspects. In this study, the sustainability impact of BPTs on cities has been investigated with numerous interviews. The study has been conducted by considering the economic, social and environmental factors, which are the three main pillars of sustainability, in a balanced way. This study is unique because it focuses on cities with small population and considers the sustainability impact approaches that have not been applied for BPTs in previous studies. The study results show that BPTs have a high potential to positively affect cities, in social, environmental and economic aspects. It is thus concluded that they can provide significant advantages to cities in the three main pillars of sustainability. This study has also revealed that BPTs can contribute positively to the sustainability of small-scale cities as well as medium-/large-scale cities.
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    Transport mode preferences of university students in post-COVID-19 pandemic
    (Elsevier Sci Ltd, 2022) Bagdatli, Muhammed Emin Cihangir; Ipek, Fatima
    The COVID-19 outbreak very quickly disrupted the order of human beings. While many sectors have been trying to cope with the ongoing COVID-19 process, they have also been trying to plan the new process for after the pandemic. Transport is one of the sectors most affected by the pandemic and it is necessary to produce the right political formulations for the post-pandemic period. For this reason, it is necessary to carefully examine the changing user demands in various segments of society due to COVID-19 and reveal effective post-pandemic transport policies. This study contributes to this requirement. Accordingly, this study investigated the transport mode preferences of university students in post-pandemic period in Istanbul, one of the important metropolises of the world, via the use of a survey. The reason for university students were focused on was that the mobility of university students is very high and in addition, they are more flexible than other age groups in using different transport modes. The main findings obtained from the study show that there will be a significant change in demand in transport modes after the pandemic. In particular, while a critical decrease may be observed in the travel demand for public buses, shared minibuses and LRT in public transport in post-pandemic period, a high increase in demand for private car use is highly probable. In addition, the research results indicate that COVID-19 can cause an increase in use of e-scooter/hoverboard and active travel modes. The results obtained through the statistical analysis and the discussions based on these results can make a significant contribution to the postpandemic transport policies of cities with high university student populations and various transport modes, such as Istanbul.
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    Vehicle Delay Estimation at Signalized Intersections Using Machine Learning Algorithms
    (Sage Publications Inc, 2021) Bagdatli, Muhammed Emin Cihangir; Dokuz, Ahmet Sakir
    Accurate determination of average vehicle delays is significant for effective management of a signalized intersection. The vehicle delays can be determined by field studies, however, this approach is costly and time consuming. Analytical methods which are commonly utilized to estimate delay cannot generate accurate estimates, especially in oversaturated traffic flow conditions. Delay estimation models based on artificial intelligence have been presented in the literature in recent years to estimate the delay more accurately. However, the number of artificial/heuristic intelligence techniques utilized for vehicle delay estimation is limited in the literature. In this study, estimation models are developed using four different machine learning methods-support vector regression (SVR), random forest (RF), k nearest neighbor (kNN), and extreme gradient boosting (XGBoost)-that have not previously been applied in the literature for vehicle delay estimation at signalized intersections. The models were tested with data collected from 12 signalized intersections located in Ankara, the capital of Turkey, and the performance of the models was revealed. The models were furthermore compared with successful delay models from the literature. The developed models, in particular the RF and XGBoost models, showed high performance in estimating the delay at signalized intersections under different traffic conditions. The results indicate that the delay estimation models based on the RF and XGBoost techniques can significantly contribute to both the literature and practice.
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    Vehicle Delay Modeling at Signalized Intersections with Gene-Expression Programming
    (Asce-Amer Soc Civil Engineers, 2020) Bagdatli, Muhammed Emin Cihangir
    The accurate determination of vehicle delays is crucial for effective intersection management. Because the optimization of signal duration in a signalized intersection is based on the reduction of delays, the rapid and accurate determination of the upcoming delay becomes an important step toward the solution. The delay times can be determined by field studies, but this approach is time-consuming and costly. An estimation of delays using analytical methods is also an approach used by transportation agencies. However, inaccurate predictions of this approach, especially in oversaturated traffic flows, is a significant disadvantage. In order to overcome these problems, estimation models based on artificial intelligence have been developed in recent years, and examples of their application are presented in the literature in which successful results have been achieved in the estimation of vehicle delays. Starting from this point, gene expression programming, an artificial intelligence technique, was used in this study to obtain models that can estimate vehicle delays rapidly and quite accurately. For the selection of this method, the high success rate of the gene expression programming technique for different engineering problems in the literature was crucial. In this study, three delay estimation models were developed by using different parameters. These models were tested with the data collected from 18 different signalized intersections in the Kayseri and Konya provinces in Turkey. When the results obtained were evaluated, it was seen that the models acquired using the gene expression programming technique were very successful in vehicle delay estimation.