Browsing by Author "Jami, Mojibulrahman"

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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.
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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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    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.