Big AIS data based spatial-temporal analyses of ship traffic in Singapore port waters

Zhang, Liye; Meng, Qiang; Fwa, T F

doi:10.1016/j.tre.2017.07.011

Cited by 149 publications

(56 citation statements)

References 26 publications

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“…Xin et al [31] proposed a ship traffic simulation model for in the Xiazhimen waterway for analyzing the statistical characteristics of ship traffic. Zhang et al [32] analyzed the spatial-temporal dynamic of traffic in port waterways while using the AIS data. Finally, Yang et al [33] overviewed and summarized main contributions of the AIS data for marine navigation safety.…”

Section: Related Workmentioning

confidence: 99%

DDTree: A Hybrid Deep Learning Model for Real-Time Waterway Depth Prediction and Smart Navigation

et al. 2020

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Timely and accurate depth estimation of a shallow waterway can improve shipping efficiency and reduce the danger of waterway transport accidents. However, waterway depth data measured during actual maritime navigation is limited, and the depth values can have large variability. Big data collected in real time by automatic identification systems (AIS) might provide a way to estimate accurate waterway depths, although these data include no direct channel depth information. We suggest a deep neural network (DNN) based model, called DDTree, for using the real-time AIS data and the data from Global Mapper to predict waterway depth for ships in an accurate and timely way. The model combines a decision tree and DNN, which is trained and tested on the AIS and Global Mapper data from the Nantong and Fangcheng ports on the southeastern and southwestern coast of China. The actual waterway depth data were used together with the AIS data as the input to DDTree. The latest data on waterway depths from the Chinese maritime agency were used to verify the results. The experiments show that the DDTree model has a prediction accuracy of 91.15%. Therefore, the DDTree model can provide an accurate prediction of waterway depth and compensate for the shortage of waterway depth monitoring means. The proposed hybrid DDTree model could improve marine situational awareness, navigation safety, and shipping efficiency, and contribute to smart navigation.

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Section: Related Workmentioning

confidence: 99%

DDTree: A Hybrid Deep Learning Model for Real-Time Waterway Depth Prediction and Smart Navigation

et al. 2020

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“…(4) The ship's front and rear spacing is small, and the shipping line is intricate. Hence, the ship management requirements are higher [39,41].…”

Section: Traffic-intensive Watersmentioning

confidence: 99%

Risk Causal Analysis of Traffic-Intensive Waters Based on Infectious Disease Dynamics

Chen

Liu

Wan

2019

JMSE

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Accidents occur frequently in traffic-intensive waters, which restrict the safe and rapid development of the shipping industry. Due to the suddenness, randomness, and uncertainty of accidents in traffic-intensive waters, the probability of the risk factors causing traffic accidents is usually high. Thus, properly analyzing those key risk factors is of great significance to improve the safety of shipping. Based on the analysis of influencing factors of ship navigational risks in traffic-intensive waters, this paper proposes a cloud model to excavate the factors affecting navigational risk, which could accurately screen out the key risk factors. Furthermore, the risk causal model of ship navigation in traffic-intensive waters is constructed by using the infectious disease dynamics method in order to model the key risk causal transmission process. Moreover, an empirical study of the Yangtze River estuary is conducted to illustrate the feasibility of the proposed models. The research results show that the cloud model is useful in screening the key risk factors, and the constructed causal model of ship navigational risks in traffic-intensive waters is able to provide accurate analysis of the transmission process of key risk factors, which can be used to reduce the navigational risk of ships in traffic-intensive waters. This research provides both theoretical basis and practical reference for regulators in the risk management and control of ships in traffic-intensive waters.

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“…Annually, there could be more than 100.000 vessels within this strait [1], [2]. This condition makes SOMS becomes the bottleneck of ship traffic connecting major world trade good's involving 600 ports in 120 countries [3].…”

Section: 1overview Of the Straits Of Malacca And Singapore (Soms)mentioning

confidence: 99%

“…The high annual vessel collision in this area has attract a great attention from maritime authority, since SOMS has a great impact on the global economy if there is a problem which makes the vessel unable to pass through the channel such as traffic congestion, marine collision. Such event induces a great cost for global logistic industry [3].…”

Section: Ship Traffic Collision History In Somsmentioning

confidence: 99%

Modelling of Ship Collision Frequency in The Strait of Malacca and Singapore (SOMS) Influenced by Indonesian Port Development

Handani¹,

Ariana²,

Dinariyana³

et al. 2019

Maritime Safety International Conference (MASTIC 2018)

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The Strait of Malacca and Singapore (SOMS) is one of the busiest shipping channel lies between sea water territory of Indonesia, Singapore and Malaysia. The strait becomes a very important part of world freight transportation since it connects the shipping lane between East Asia, West Asia, Australia and Europe. Even though Traffic Separation Scheme (TSS) has been applied in the area to prevent the unlikely event of vessel collision, the number of catastrophic ship accident tends to increase by year. The Indonesian government plans to develop the ports located around SOMS. Such development increases the number of ships call per year which causes the ship traffic density in SOMS becomes more crowdy. This paper conducts an analysis on the vessel collision frequency influenced by the Indonesian ports development around waterway of SOMS including ports in Tanjung Pinang, Dumai and Batam Island. IALA Waterways Risk Assessment Program (IWRAP) is utilized to determine the annual frequency of head-on collision, overtaking collision and crossing collision. The Result of IWRAP modeling in this study shows that there is a significant increase of the future total ship collision frequency. The crossing collision contribute the most for the annual vessel collision, since there are higher number of ships cross the TSS caused by the development of Indonesian port around SOMS.

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Big AIS data based spatial-temporal analyses of ship traffic in Singapore port waters

Cited by 149 publications

References 26 publications

DDTree: A Hybrid Deep Learning Model for Real-Time Waterway Depth Prediction and Smart Navigation

DDTree: A Hybrid Deep Learning Model for Real-Time Waterway Depth Prediction and Smart Navigation

Risk Causal Analysis of Traffic-Intensive Waters Based on Infectious Disease Dynamics

Modelling of Ship Collision Frequency in The Strait of Malacca and Singapore (SOMS) Influenced by Indonesian Port Development

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