Estimating roadway horizontal alignment from geographic information systems data: An artificial neural network–based approach

Name: Estimating roadway horizontal alignment from geographic information systems data: An artificial neural network–based approach
Author: Bartın, Bekir Oğuz, Jami, Mojibulrahman, Ozbay, K.

İsim	Estimating roadway horizontal alignment from geographic information systems data: An artificial neural network–based approach
Yazar	Bartın, Bekir Oğuz, Jami, Mojibulrahman, Ozbay, K.
Basım Tarihi:	2023-11-01
Basım Yeri	- ASCE
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	0733-9453
Kayıt Numarası	6631dd21-419d-42f5-a419-e37b3d3981ec
Lokasyon	Civil Engineering
Tarih	2023-11-01
Notlar	C2SMART ; Ozyegin University ; U.S. Department of Transportation ; New Jersey Department of Transportation ; New York University
Örnek Metin	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.
DOI	10.1061/JSUED2.SUENG-1439
Cilt	149

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Estimating roadway horizontal alignment from geographic information systems data: An artificial neural network–based approach

Yazar Bartın, Bekir Oğuz, Jami, Mojibulrahman, Ozbay, K.

Basım Tarihi 2023-11-01

Basım Yeri - ASCE

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 0733-9453

Kayıt Numarası 6631dd21-419d-42f5-a419-e37b3d3981ec

Lokasyon Civil Engineering

Tarih 2023-11-01

Notlar C2SMART ; Ozyegin University ; U.S. Department of Transportation ; New Jersey Department of Transportation ; New York University

Örnek Metin 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.

DOI 10.1061/JSUED2.SUENG-1439

Cilt 149