Extracting roadway vertical alignment from USGS LiDAR point cloud data using an Artificial Neural Network based method

Mojibulrahman Jami; Bekir Bartin; Kaan Ozbay

doi:10.55329/piou4930

Authors

Mojibulrahman Jami Özyeğin University, Türkiye
Bekir Bartin Özyeğin University, Türkiye https://orcid.org/0000-0001-6941-228X
Kaan Ozbay New York University https://orcid.org/0000-0001-7909-6532

DOI:

https://doi.org/10.55329/piou4930

Keywords:

Artificial Neural Networks, Highway Safety Manual, LiDAR data, machine learning, road safety, roadway vertical alignment, Northern America

Abstract

Vertical grades and vertical curvature significantly influence traffic safety. However, obtaining accurate and large-scale data on roadway vertical alignment remains a major challenge. This paper presents a cost-effective and efficient method for estimating roadway vertical alignment using publicly available aerial LiDAR data provided by the United States Geological Survey. An Artificial Neural Network (ANN) model was proposed to predict whether a LiDAR point belongs to a vertical curve or a tangent segment. Due to the limited availability of actual roadway vertical alignment data and the substantial data requirements of machine learning models, a synthetic training dataset was generated by systematically varying road grades and segment lengths to represent realistic combinations of tangents, crest and sag curves. This approach ensured that the model was exposed to a wide range of geometric configurations and allowed it to learn generalized relationships between vertical alignment features and their corresponding geometric parameters. The model was then independently evaluated by comparing the vertical alignment estimated from the extracted aerial LiDAR data for two-lane two-way rural roadways, Route 152 in New Jersey and Route 299 in California, with their corresponding actual vertical alignment data. In addition, a case study was conducted on another rural two-lane highway in which the model was used to compute safe speeds for each roadway segment. The resulting speeds were then compared with the posted speed limits along the corridor. The satisfactory estimation results of this study indicate that the proposed approach can be used for conducting large-scale analyses to estimate vertical alignment using publicly available LiDAR data.

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Author Biographies

Mojibulrahman Jami, Özyeğin University, Türkiye

Mojibulrahman Jami is a Civil Engineering Ph.D. candidate at Özyeğin University in Istanbul, Turkey, specializing in Transportation Engineering. He earned a B.S. in Civil Engineering from Herat University and holds two M.S. degrees: Urban Management from the University of Florence, Florence, Italy, and Highway and Transportation Engineering from Islamic Azad University, Tehran, Iran. A professional design engineer, he has practical experience delivering infrastructure projects. His research centers on transportation safety with an emphasis on roadway alignment extraction using diverse data sources.

CRediT contribution: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing—original draft, Writing—review & editing.

Bekir Bartin, Özyeğin University, Türkiye

Bekir Bartin is a full time Associate Professor in Civil Engineering Department at Özyeğin University in Istanbul, Turkey. Prior to his current position he was the founding chair at Civil Engineering Department at Altınbaş University (formerly Istanbul Kemerburgaz University). Dr. Bartin worked as a full-time research associate at Rutgers University from 2006 to 2012, where he conducted research projects, supervised a team of graduate students, and taught graduate and undergraduate courses. His research expertise lies in development of simulation models of large-scale complex transportation systems, application of reinforcement learning methods in traffic simulation, economic evaluation of transportation investment projects, traffic safety and security. He has served as the principal and co-principal investigator of more than 20 research projects. Dr. Bartin has published 38 peer-reviewed journal articles, 2 book chapters and more than 50 conference proceedings. He is an affiliate of the C2SMART center, a first tier University Transportation Center at New York University (NYU), funded by the U.S Department of Transportation.

CRediT contribution: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing—original draft, Writing—review & editing.

Kaan Ozbay, New York University

Kaan Ozbay is a full-time professor at the Department of Civil and Urban Engineering at New York University. Since 2017, Professor Ozbay has been the Founding Director of the C2SMARTER Center. He is also Global Network Professor of Civil and Urban Engineering, NYU Abu Dhabi (NYUAD) and Global Network Professor of Engineering and Computer Science, NYU Shanghai (NYUSH). Dr. Ozbay is the recipient of the prestigious National Science Foundation (NSF) CAREER award. Dr. Ozbay is the co-editor of a new book titled “Dynamic Traffic Control & Guidance” published by Springer Verlag’s "Complex Social, Economic and Engineered Networks" series in 2013. In addition to this book, Dr. Ozbay is the co-author of three other books titled “Feedback Based Ramp Metering for Intelligent Transportation Systems” published by Kluwer Academics in 2004, "Feedback Control Theory for Dynamic Traffic Assignment", Springer-Verlag and “Incident Management for Intelligent Transportation Systems” published by Artech House publishers both in 1999. Dr. Ozbay published approximately 425 refereed papers in scholarly journals and conference proceedings. Professor Ozbay serves as the “Associate Editor” of Networks and Spatial Economic journal and Transportmetrica B: Transportation Dynamics journal. He is a member of the editorial board of the ITS journal.

CRediT contribution: Conceptualization, Formal analysis, Investigation, Methodology, Writing—original draft, Writing—review & editing.

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