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Open AccessArticle
Comprehensive Study on Optimizing Inland Waterway Vessel Routes Using AIS Data
by
Xiaoyu Yuan
Xiaoyu Yuan 1,2,
Jiawei Wang
Jiawei Wang 1,
Guang Zhao
Guang Zhao 3,4 and
Hongbo Wang
Hongbo Wang
Hongbo Wang (Member, IEEE) was born in Changchun, China, in 1969. She received a Bachelor's degree a [...]
Hongbo Wang (Member, IEEE) was born in Changchun, China, in 1969. She received a Bachelor's degree in Engineering from the Department of Hydroacoustic Electronics, Harbin Shipbuilding Engineering College, a Master's degree in Engineering from the Department of Vehicle Engineering, School of Automotive Engineering, Jilin University, a Ph.D. degree from Saint Petersburg State University, Russia. Currently, she is a Professor at the College of Electronic Science and Engineering at Jilin University. Her research interests include optimal control, weather routing, collision avoidance, and environmental perception. She has been engaged in software and hardware design and research on ship motion control systems for more than 20 years. Hongbo Wang has undertaken many important scientific research projects and won one-second prize in science and technology from China Shipbuilding Industry Corporation (2010), one-third prize in science and technology from China Shipbuilding Industry Corporation (2009), and one-second prize in science and technology progress from Jiangxi Province (2006).
1,*
1
State Key Laboratory on Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun 130012, China
2
Laboratory of Science and Technology on Marine Navigation and Control, China State Ship-Building Corporation, Tianjin 300131, China
3
Tianjin Navigation Instrument Research Institute, Tianjin 300131, China
4
Tianjin Key Laboratory of Quantum Precision Measurement Technology, Tianjin 300051, China
*
Author to whom correspondence should be addressed.
J. Mar. Sci. Eng. 2024, 12(10), 1775; https://doi.org/10.3390/jmse12101775 (registering DOI)
Submission received: 11 August 2024
/
Revised: 26 September 2024
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Accepted: 1 October 2024
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Published: 6 October 2024
Abstract
Inland waterway transport is an important mode of transportation for many countries and regions. Route planning optimization can reduce navigation time, avoid traffic congestion, and improve transportation efficiency. In actual operations, many vessels determine their navigation routes based on the experience of their shipowners. When the captain fails to obtain accurate information, experience-based routes may pose significant navigation risks and may not consider the overall economic efficiency. This study proposes a comprehensive method for optimizing inland waterway vessel routes using automatic identification system (AIS) data, considering the geographical characteristics of inland waterways and navigation constraints. First, AIS data from vessels in inland waters are collected, and the multi-objective Peak Douglas–Peucker (MPDP) algorithm is applied to compress the trajectory data. Compared to the traditional DP algorithm, the MPDP algorithm reduces the average compression rate by 5.27%, decreases length loss by 0.04%, optimizes Euclidean distance by 50.16%, and improves the mean deviations in heading and speed by 23.53% and 10.86%, respectively. Next, the Ordering Points to Identify the Clustering Structure (OPTICS) algorithm is used to perform cluster analysis on the compressed route points. Compared to the traditional DBSCAN algorithm, the OPTICS algorithm identifies more clusters that are both detailed and hierarchically structured, including some critical waypoints that DBSCAN may overlook. Based on the clustering results, the A* algorithm is used to determine the connectivity between clusters. Finally, the nondominated sorting genetic algorithm II is used to select suitable route points within the connected clusters, optimizing objectives, including path length and route congestion, to form an optimized complete route. Experiments using vessel data from the waters near Shuangshan Island indicate that, when compared to three classic original routes, the proposed method achieves path length optimizations of 4.28%, 1.67%, and 0.24%, respectively, and reduces congestion by 24.15%. These improvements significantly enhance the planning efficiency of inland waterway vessel routes. These findings provide a scientific basis and technical support for inland waterway transport.
Share and Cite
MDPI and ACS Style
Yuan, X.; Wang, J.; Zhao, G.; Wang, H.
Comprehensive Study on Optimizing Inland Waterway Vessel Routes Using AIS Data. J. Mar. Sci. Eng. 2024, 12, 1775.
https://doi.org/10.3390/jmse12101775
AMA Style
Yuan X, Wang J, Zhao G, Wang H.
Comprehensive Study on Optimizing Inland Waterway Vessel Routes Using AIS Data. Journal of Marine Science and Engineering. 2024; 12(10):1775.
https://doi.org/10.3390/jmse12101775
Chicago/Turabian Style
Yuan, Xiaoyu, Jiawei Wang, Guang Zhao, and Hongbo Wang.
2024. "Comprehensive Study on Optimizing Inland Waterway Vessel Routes Using AIS Data" Journal of Marine Science and Engineering 12, no. 10: 1775.
https://doi.org/10.3390/jmse12101775
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