Estimating roadway horizontal alignment using artificial neural network

Name: Estimating roadway horizontal alignment using artificial neural network
Author: Bartın, Bekir Oğuz, Jami, Mojibulrahman, Özbay, K.

İsim	Estimating roadway horizontal alignment using artificial neural network
Yazar	Bartın, Bekir Oğuz, Jami, Mojibulrahman, Özbay, K.
Basım Tarihi:	2021
Basım Yeri	- IEEE
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-172819142-3
Kayıt Numarası	58eba431-e50b-4514-9286-8e423aad99ce
Lokasyon	Civil Engineering
Tarih	2021
Notlar	NJDOT ; C2SMART, a Tier 1 UTC at New York University - USDOT
Örnek Metin	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.
DOI	10.1109/ITSC48978.2021.9565062

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Estimating roadway horizontal alignment using artificial neural network

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

Basım Tarihi 2021

Basım Yeri - IEEE

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-172819142-3

Kayıt Numarası 58eba431-e50b-4514-9286-8e423aad99ce

Lokasyon Civil Engineering

Tarih 2021

Notlar NJDOT ; C2SMART, a Tier 1 UTC at New York University - USDOT

Örnek Metin 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.

DOI 10.1109/ITSC48978.2021.9565062