Estimating Roadway Horizontal Alignment from Geographic Information Systems Data: An Artificial Neural Network–Based Approach
Publication: Journal of Surveying Engineering
Volume 149, Issue 4
Abstract
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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Data Availability Statement
All data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The study was supported by the NJDOT (FHWA-NJ-2019-007) and partially by C2SMART, a Tier 1 UTC at New York University funded by the USDOT and Ozyegin University. The contents of this paper only reflect the views of the authors who are responsible for the facts and do not represent any official views of any sponsoring organizations or agencies.
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Published In
Journal of Surveying Engineering
Volume 149 • Issue 4 • November 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Jan 2, 2023
Accepted: Jun 12, 2023
Published online: Aug 9, 2023
Published in print: Nov 1, 2023
Discussion open until: Jan 9, 2024
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