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BARTIN, Bekir Oğuz

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Bekir Oğuz

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Now showing 1 - 7 of 7
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    Safety performance functions for Two-Lane urban arterial segments
    (Elsevier, 2023-11) Bartın, Bekir Oğuz; Ozbay, K.; Xu, C.; Civil Engineering; BARTIN, Bekir Oğuz
    This paper presents the calibration and development process of the safety performance function for the undivided two-lane urban and suburban arterial segments in New Jersey. Data requirements, the availability of required data, and the data processing and extraction methods are presented, along with detailed results of the calibration and development process. Negative binomial, Poisson, zero-inflated Poisson and Hurdle models were generated using the development database. The best model fit was based on likelihood ratio test, AIC and BIC statistics, Vuong test and rootograms. The test database was used to calculate the calibration factor for U2 segments. The predictions of the location-specific count models were then evaluated and compared to those of calibrated Highway Safety Manual model, using the test dataset. The validation test results showed that the negative binomial and hurdle models exhibited better performance in terms of absolute residuals and absolute Pearson residual statistics. This paper also shows the impact of crash location information on analyses results, and underlines that efforts made to manually extract the missing required data can easily be offset by the inaccuracies in crash frequency databases, and the thresholds used to identify intersection related crashes.
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    Simulation of vehicles’ gap acceptance decision at unsignalized intersections using SUMO
    (Elsevier, 2022) Bagheri, Mohammad; Bartın, Bekir Oğuz; Ozbay, K.; Civil Engineering; BARTIN, Bekir Oğuz; Bagheri, Mohammad
    In this paper, an artificial neural network (ANN)-based gap acceptance behavior model was proposed. The feasibility of implementing this model in a microscopic simulation tool was tested using the application programming interface of Simulation of Urban Mobility (SUMO) simulation package. A stop-controlled intersection in New Jersey was selected as a case study. The simulation model of this intersection was calibrated using ground truth data extracted during the afternoon peak hours. The ANN-based SUMO model was compared to SUMO model with default gap acceptance parameters and the SUMO model with calibrated gap acceptance parameters. The comparison was based on wait time and accepted gap values at the minor approach of the intersection. The results showed that the ANN-based model produced superior results based on the selected outputs. The analysis results also indicated that the ANN-based model leads to significantly more realistic driving behavior of vehicles on the major approach of the intersection.
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    Estimating roadway horizontal alignment from geographic information systems data: An artificial neural network–based approach
    (ASCE, 2023-11-01) Bartın, Bekir Oğuz; Jami, Mojibulrahman; Ozbay, K.; Civil Engineering; BARTIN, Bekir Oğuz; Jami, Mojibulrahman
    Estimating horizontal alignment using discretized roadway data points, such as GIS maps, is complicated because the number of curved and tangent segments and their start and end points are not known a priori. This study proposes a two-step approach: The first step estimates the number and type of segments and their start and end points using an artificial neural network (ANN)-based approach. The second step estimates the segment-related attributes such as radii and length by circular curve-fitting. The novelty of this study lies in the simplicity of the input vector to the ANN model, which contains only the latitude and longitude readings of a point and those of its neighboring points. Training and test data were comprised of points extracted from curved and tangent segments of random horizontal alignments, generated synthetically using a computer programming code. The proposed approach was evaluated and compared with other available methods presented in the literature using real roadway horizontal alignment data from one freeway and one rural roadway with a total length of 47 km and 65 curved segments. The analysis results indicated that the proposed approach outperforms other approaches in terms of estimation performance, particularly when the roadway follows a winding alignment.
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    Investigation of the extent of field data required for reliable calibration and validation of large scale traffic simulation models: A case study
    (IEEE, 2020-09-20) Bartın, Bekir Oğuz; Ozbay, K.; Gao, J.; Kurkcu, A.; Civil Engineering; BARTIN, Bekir Oğuz
    Availability, accuracy and relevance of field data are essential for developing a reliable simulation model. Large scale simulation models in particular require data from many sources and in great detail. Considering the sheer size of many simulation models used in practice, collecting all the required data is both costly and time-consuming, and in many cases even impossible. Therefore a trade-off is usually made in terms of the amount of data collected or the number of selected data collection locations used for the calibration and validation process. The fundamental question addressed in this paper is the following: what is the marginal gain in using an additional type of field data for the calibration and validation process? Using a case study where the calibration and validation of a test network is performed under different scenarios of available data types, and the results are compared in hindsight. The results indicate that only traffic flow and travel time data would suffice for the calibration and validation process, and that the marginal benefit of acquiring additional data such as, queue length, in this case study, is likely to be insignificant.
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    Estimating roadway horizontal alignment using artificial neural network
    (IEEE, 2021) Bartın, Bekir Oğuz; Jami, Mojibulrahman; Özbay, K.; Civil Engineering; BARTIN, Bekir Oğuz; Jami, Mojibulrahman
    This paper presents a novel approach for extracting horizontal alignment data from Geographic Information Systems (GIS) centerline shapefiles. Estimating the road horizontal alignment is formulated as a minimization problem, and a two-tiered approach is proposed. Step 1 is the segmentation: determining the curved and tangent sections along a roadway. Step 1 is conducted by applying an artificial neural network (ANN) model, trained using two different datasets, actual and synthetic alignment data, generated using subjective decision on whether a vertex is part of a curved or a tangent section. Step 2 uses the segmentation results and estimates the curvature information using a known algebraic method, called Taubin circle fit. A 10.72 mile long freeway section is used for evaluating the accuracy of the proposed approach, of which the actual alignment information is available. Six different metrics are used for evaluation. The results show the high accuracy of the ANN method, where the overlap of estimated and actual section lengths are 0.97 and 0.92 for curved and tangent sections, respectively.
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    Implementing artificial neural network-based gap acceptance models in the simulation model of a traffic circle in SUMO
    (Sage, 2023-12) Bagheri, Mohammad; Bartın, Bekir Oğuz; Ozbay, K.; Civil Engineering; BARTIN, Bekir Oğuz; Bagheri, Mohammad
    The impact of various operational and design alternatives at roundabouts and traffic circles can be evaluated using microscopic simulation tools. Most microscopic simulation software utilizes default underlying models for this purpose, which may not be generalized to specific facilities. Since the effectiveness of traffic operations at traffic circles and roundabouts is highly affected by the gap rejection-acceptance behavior of drivers, it is essential to accurately model drivers' gap acceptance behavior using location-specific data. The objective of this paper was to evaluate the feasibility of implementing an artificial neural network (ANN)-based gap acceptance model in SUMO, using its application programming interface. A traffic circle in New Jersey was chosen as a case study. Separate ANN models for one stop-controlled and two yield-controlled intersections were trained based on the collected ground truth data. The output of the ANN-based model was then compared with that of the SUMO model, which was calibrated by modifying the default gap acceptance parameters to match the field data. Based on the results of the analyses it was concluded that the advantage of the ANN-based model lies not only in the accuracy of the selected output variables in comparison to the observed field values, but also in the realistic vehicle crossings at the uncontrolled intersections in the simulation model.
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    Automatic identification of roadway horizontal alignment information using geographic information system data: CurvS tool
    (Sage, 2022) Bartın, Bekir Oğuz; Demiroluk, S.; Ozbay, K.; Jami, Mojibulrahman; Civil Engineering; BARTIN, Bekir Oğuz; Jami, Mojibulrahman
    This paper introduces CurvS, a web-based tool for researchers and analysts that automatically extracts, visualizes, and analyses roadway horizontal alignment information using readily available geographic information system roadway centerline data. The functionalities of CurvS are presented along with a brief background on its methodology. The validation of its estimation results are presented using actual horizontal alignment data from two different roadway types: Route 83, a two-lane two-way rural roadway in New Jersey and I-80, a freeway segment in Nevada. Different metrics are used for validation. These are identification rates of curved and tangent sections, overlap ratio of curved and tangent sections between estimated and actual horizontal alignment data, and percent fit of curve radii. The validation results show that CurvS is able to identify all the curves on these two roadways, and the estimated section lengths are significantly close to the actual alignment data, especially for the I-80 freeway segment, where 90% of curved length and 94% of tangent section length are correctly matched. Even when curves have small central angles, such as the ones in Route 83, CurvS’s estimations covers 71% of curved length and 96% of tangent section length.