Use of AIS data for performance evaluation of ship traffic with speed control
Introduction
The Yangtze River in China is the longest and busiest inland waterway in the world. The Shanghai section of the river is 65 nautical miles (nm) long and is located at the downstream of the river toward the East China Sea. Its average annual daily vessel traffic volume was 960 ships in 2018, demonstrating an increase of 15% in comparison with the 833 ships in 2013 according to the Statistical Bulletin of Transportation Industry Development (Ministry of Transport of the People's Republic of China, 2018). Given such an increment in ship volume, the associated traffic safety is eliciting increased attention, particularly in the busy segments of the Shanghai section, such as Baoshan North, Baoshan, and Waigaoqiao channels. In 2011, the Maritime Safety Administration regulated the speed limit to 12 knots for vessels passing through deep-water channels that are greater than 12.5 m of the Shanghai section. However, this speed limit was determined by experts based on their experience and not scientifically approved due to the difficulty of obtaining vessel speed data in the past. In a broad sense, this gap raises a critical research issue for ship traffic safety in narrow waters from a theoretical perspective. This study aims to fill in this gap by introducing a new paradigm shift in setting and justifying the ship speed limit via experimental tests based on AIS data.
The speed characteristics under the speed limit rule, especially when the navigation channel is busy, need to be studied (e.g., the Shanghai section of the Yangtze River). According to domain experience, certain ships sail faster than the speed limit to deliver cargoes as soon as possible; other ships sail much slower than the speed limit for fuel saving. However, if a ship sails too slowly, its maneuverability will be affected and navigation jam may increase. Conversely, if the ship speed is too high, the operation time for striking or collision avoidance will be reduced, thus compromising ship safety. How effective is the 12 knots speed limit in the Shanghai section of the Yangtze River? Will it have 50% ship speeding, such as in Singapore (Kang et al., 2019)? These questions can be answered with the aid of the big data technology AIS. From the behavior of each ship at the micro level, we can obtain ships’ behavior at the macro level; consequently, we can explore the rule of speed under the given speed limit.
The Safety of Life at Sea convention stipulates that vessels above 300 gross tonnages on international voyages should be equipped with AIS transceivers (International Maritime Organization, 2001). By using AIS, crucial time-dependent data, such as ship dynamic location and speed, can be obtained and used to support research in marine-related fields (Goerlandt et al., 2017). In this study, the features of real-time ship speed and ship traffic volume are analyzed to test the rationality of the speed limit in traffic-intensive waters in general and the 12-knot limit in the Shanghai section of the Yangtze River in particular.
The remainder of this paper is organized as follows. Section 2 reviews relevant literature. Section 3 presents the research methodology, including the selection of key channels, error elimination for AIS data, and calculation of traffic flow parameters. Section 4 introduces the results and analyzes the correlation between speed limit and ship traffic flow. Section 5 presents the conclusions.
Section snippets
Literature review
Investigating ship speed characteristics is crucial in ensuring maritime safety. Many ship speed studies have been conducted. On the basis of the 135 investigations conducted by the Marine Accident Investigation Branch in the United Kingdom, Tirunagari et al. (2012) identified traffic density, ship speed, confusion, equipment, bad weather, fatigue, and health as the most common causes of maritime accidents. Mazaheri et al. (2013) reported that a minor relation exists between traffic
AIS data acquisition
A total of 83,140,629 AIS records (from June 1, 2014, 8:00:00 to June 30, 2014, 8:00:00) involving 4923 ships were obtained from the Shanghai section of the Yangtze River. The geographic scope used for AIS data collection is denoted by the blue dotted line in Fig. 1. The corresponding longitudes and latitudes of the four vertices are (121°18′E, 30°N), (121°18′E, 31°N), (122°30′E, 30°N), and (122°30′E, 31°N). The border of the Shanghai section of the Yangtze River is a line between points
Observations from AIS data of the lanes
Fig. 5 shows the numbers of vessels in the six channels per hour based on AIS data. The vessel numbers of each lane (L1, L2, L3, L4, L5, and L6) range in the intervals of [12.28, 24.41], [17.17, 28.59], [9.45, 21.38], [8.07, 16.62], [7.76, 25.69], and [6.72, 17.72], respectively. Moreover, two traffic periods are shown in Fig. 5, with a peak period from 14:00 to 18:00 and an off-peak period from 3:00 to 7:00. Furthermore, the ship number between 8:00 and 18:00 is slightly larger than that
Conclusion
We developed an AIS data-based method for evaluating the performance of shipping traffic under speed limit. The Shanghai section of the Yangtze River was taken as an example. The detailed steps of the proposed method are as follows: (1) cleaning the data to filter out problematic and duplicated data, (2) geocoding the AIS data to waterway segments, (3) calculating the ship traffic characteristics, and (4) estimating the ship speed and traffic volume to analyze the rationality of the preset
Funding
This research is sponsored by the National Natural Science Foundation of China [Grant nos. 71904117, 71704103, 71573172] and Shanghai Pujiang Program [Grant No. 5PJC060]. The authors also acknowledge the funding from the European Union's Horizon 2020 Research and Innovation Programme under grant agreement No. 823904 (ENHANCE).
CRediT authorship contribution statement
Likun Wang: Conceptualization, Methodology, Resources, Formal analysis, Writing - original draft, Writing - review & editing, Supervision, Funding acquisition. Yang Li: Writing - original draft, Formal analysis, Visualization. Zheng Wan: Writing - original draft, Writing - review & editing. Zaili Yang: Writing - review & editing. Tong Wang: Writing - review & editing. Keping Guan: Data curation, Writing - review & editing. Lei Fu: Formal analysis.
Declaration of competing interest
There are no conflicts of interest to declare.
Acknowledgments
We would like to thank the anonymous reviewers for their insightful comments to improve this article.
References (30)
- et al.
Maritime navigation accidents and risk indicators: an exploratory statistical analysis using AIS data and accident reports
Reliab. Eng. Syst. Saf.
(2018) - et al.
Probabilistic risk assessment on maritime spent nuclear fuel transportation (Part II: ship collision probability)
Reliab. Eng. Syst. Saf.
(2017) - et al.
An analysis of wintertime navigational accidents in the Northern Baltic Sea
Saf. Sci.
(2017) - et al.
Determining optimal speed limits in traffic networks
IATSS Res.
(2015) - et al.
Risk assessment of ships maneuvering in an approaching channel based on AIS data
Ocean Eng.
(2019) - et al.
Fundamental diagram of ship traffic in the Singapore Strait
Ocean Eng.
(2018) - et al.
How do ships pass through L-shaped turnings in the Singapore strait?
Ocean Eng.
(2019) - et al.
Analysis of the marine traffic safety in the Gulf of Finland
Reliab. Eng. Syst. Saf.
(2009) - et al.
A novel framework for regional collision risk identification based on AIS data
Appl. Ocean Res.
(2019) - et al.
Study on collision avoidance in busy waterways by using AIS data
Ocean Eng.
(2010)