Study of narrow waterways congestion based on automatic identification system (AIS) data: A case study of Houston Ship Channel

Kang, Masood Jafari; Zohoori, Sepideh; Hamidi, Maryam; Wu, Xing

doi:10.1016/j.joes.2021.10.010

Cited by 16 publications

(10 citation statements)

References 32 publications

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“…Eljabu et al emphasized the significance of automatic methods for extracting traffic routes from AIS data, demonstrating the potential of density-based clustering algorithms [12]. Kang et al analyzed AIS data from the Houston Ship Channel to explore vessel congestion patterns, factors contributing to congestion, and speed variations [13]. Kabir et al developed a framework and algorithms for capturing significant directional changes in vessel trajectories for maritime traffic management [14].…”

Section: Historical Analysismentioning

confidence: 99%

Deep Learning Applications in Vessel Dead Reckoning to Deal with Missing Automatic Identification System Data

Sedaghat,

Arbabkhah,

Jafari Kang

et al. 2024

JMSE

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This research introduces an online system for monitoring maritime traffic, aimed at tracking vessels in water routes and predicting their subsequent locations in real time. The proposed framework utilizes an Extract, Transform, and Load (ETL) pipeline to dynamically process AIS data by cleaning, compressing, and enhancing it with additional attributes such as online traffic volume, origin/destination, vessel trips, trip direction, and vessel routing. This processed data, enriched with valuable details, serves as an alternative to raw AIS data stored in a centralized database. For user interactions, a user interface is designed to query the database and provide real-time information on a map-based interface. To deal with false or missing AIS records, two methods, dead reckoning and machine learning techniques, are employed to anticipate the trajectory of the vessel in the next time steps. To evaluate each method, several metrics are used, including R squared, mean absolute error, mean offset, and mean offset from the centerline. The functionality of the proposed system is showcased through a case study conducted in the Gulf Intracoastal Waterway (GIWW). Three years of AIS data are collected and processed as a simulated API to transmit AIS records every five minutes. According to our results, the Seq2Seq model exhibits strong performance (0.99 R squared and an average offset of ~1400 ft). However, the second scenario, dead reckoning, proves comparable to the Seq2Seq model as it involves recalculating vessel headings by comparing each data point with the previous one.

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Section: Historical Analysismentioning

confidence: 99%

Deep Learning Applications in Vessel Dead Reckoning to Deal with Missing Automatic Identification System Data

Sedaghat,

Arbabkhah,

Jafari Kang

et al. 2024

JMSE

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“…Each AIS message contains both static and dynamic information. Static information includes vessel attributes, while dynamic information covers the spatial-temporal data of the vessel [3]. The MarineCadastre website provides access to AIS data [49].…”

Section: Problem Definition Ais Datamentioning

confidence: 99%

“…Contrasted with the ample maneuvering space in open seas or larger water bodies, these confined areas demand precise navigation. Skillful handling and often the aid of local pilots are essential for navigating these constrained spaces [3]. Challenges include a restricted turning radius where big vessels have to sail extra miles to make a U-turn [4], possible strong currents or tides, and close quarters with other vessels or the boundaries of the waterway itself [5].…”

Section: Introductionmentioning

confidence: 99%

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Automatic Identification System-Based Prediction of Tanker and Cargo Estimated Time of Arrival in Narrow Waterways

Arbabkhah,

Sedaghat,

Jafari Kang

et al. 2024

JMSE

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In maritime logistics, accurately predicting the Estimated Time of Arrival (ETA) of vessels is pivotal for optimizing port operations and the global supply chain. This study proposes a machine learning method for predicting ETA, drawing on historical Automatic Identification System (AIS) data spanning 2018 to 2020. The proposed framework includes a preprocessing module for extracting, transforming, and applying feature engineering to raw AIS data, alongside a modeling module that employs an XGBoost model to accurately estimate vessel travel times. The framework’s efficacy was validated using AIS data from the Port of Houston, and the results indicate that the model can estimate travel times with a Mean Absolute Percentage Error (MAPE) of just 5%. Moreover, the model retains consistent accuracy in a simplified form, pointing towards the potential for reduced complexity and increased generalizability in maritime ETA predictions.

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“…In the study of narrow waterways, Kang, M.J.; Zohoori, S.; et al [26] applied the road traffic congestion index to the sea and quantified the congestion of different sections by considering the geometry of the channel, time, vessel type, and scale to facilitate congestion resolution in narrow waterways. Liu, C.; Zhou, X.; et al [27] , based on the structural characteristics of traffic flow under AIS data, used the K-means clustering algorithm while considering vessel type and vessel domain to further obtain the capacity of different vessels in narrow waterways.…”

Section: Introductionmentioning

confidence: 99%

Study of Ship Overtaking Boundary in Narrow Waterways

Liu¹,

LI²

2023

Preprint

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This paper proposes a data-driven approach to the calculation of ship overtaking boundary conditions in narrow channels based on ship overtaking characteristics by the Automatic Identification System (AIS) data. First, we collect the ship tracking process data in a narrow channel by processing the original AIS data, followed by a normality test and correlation analysis, and finally, the boundary conditions are obtained using a weighted K-means clustering algorithm. to identify the relevant data. We applied the proposed approach to estimate the overtaking boundary conditions of ships in the straight channel section of Shanghai South Passage. The method yields boundary conditions that significantly reduce collision risk when a ship engages in pursuit behavior in narrow waterways, providing a scientific basis for the overtaking of Marine Autonomous Surface Ships (MASS) in autonomous navigation within such environments, further promoting the automation level of USVs and autonomous vessels.

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Study of narrow waterways congestion based on automatic identification system (AIS) data: A case study of Houston Ship Channel

Cited by 16 publications

References 32 publications

Deep Learning Applications in Vessel Dead Reckoning to Deal with Missing Automatic Identification System Data

Deep Learning Applications in Vessel Dead Reckoning to Deal with Missing Automatic Identification System Data

Automatic Identification System-Based Prediction of Tanker and Cargo Estimated Time of Arrival in Narrow Waterways

Study of Ship Overtaking Boundary in Narrow Waterways

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