Modelling the collision risk in the Yangtze River using Bayesian ...

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Modelling the Collision Risk in the Yangtze River Using Bayesian Networks Bing Wu

Yang Wang

Intelligent Transportation System Research Center (ITSC), National Engineering Research Center for Water Transport Safety, Wuhan University of Technology Wuhan, China E-mail: [email protected]

Intelligent Transportation System Research Center (ITSC), National Engineering Research Center for Water Transport Safety, Wuhan University of Technology Wuhan, China E-mail: [email protected]

Likang Zong

Carlos Guedes Soares

Intelligent Transportation System Research Center (ITSC), National Engineering Research Center for Water Transport Safety, Wuhan University of Technology Wuhan, China E-mail: [email protected]

Centre for Marine Technology and Ocean Engineering (CENTEC), Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal. E-mail: [email protected]

Xinping Yan Intelligent Transportation System Research Center (ITSC), National Engineering Research Center for Water Transport Safety, Wuhan University of Technology Wuhan, China E-mail: [email protected] navigation condition, the throughput of this river has steadily increased in the recent years, and in 2016, there were16 ports with more than one billion throughputs.Therefore, this river is also named as the “golden waterway”. However, the navigation condition in the different geographical position varies from each other. As shown in Fig.1, the navigation condition of Yangtze River can be classified as three categories[1], which are upstream, midstream and downstream, respectively.

Abstract—Ships navigating in the traffic separation scheme pose serious risk owing to the distinguishing characteristics of narrow channels, such as limited depth, dense traffic, and disturbance of crossing ships. This paper models the collision risk in the Yangtze River by considering both the causation factors and the emergency management of maritime accidents using historical data. First, more than one hundred collision accident data are collected for the period of 2009 and 2012. Second, a Bayesian network is proposed to model the collision risk of ships in the Yangtze River, while the qualitative part is established by domain experts and previous works, and the quantitative part is developed based on historical data. Third, the collision risk is compared with the navigational risk in Tianjin Port. The findings are beneficial for the safety management of sailing ships in the Yangtze River. Keywords—collision risk; Bayesian network;maritime accidents; emergency management

I. INTRODUCTION The Yangtze River is the longest river in China, which is more than 6300km long. In the recent years, the Chinese government has invested a lot money to improve the capacity of this river, and the navigation condition has tremendously improved, for example, from Nanjing to Shanghai, the water depth has been increased from 10.5m to 12.5m, together with the width of channel increased from 300m to 500m; and from Wuhu to Wuhan, the water depth has increased from 4.5m to 6.0m; in the lock area, a ship lift was constructed to alleviate water traffic pressure of lock. Owing to the improved

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Fig. 1. Three categories of the Yangtze River.

The upstream of the Yangtze River is the lock area, and the boundary between upstream and midstream is the famous Three George Dam. In the upstream, the water depth is more than one hundred meters; the current velocity is very slow when the water level of the dam is unchanged (145m or 175m); however, when changing the water level of the dam from145mto 175m to control the flooding, the current velocity will be very high (3m/s or even more); the maximum ship deadweight tonnage is more than 10000t, which means the



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information system, where the emergency resources can be displayed and through the developed tools, the optimal emergency resource scheduling for different types of maritime accidents can be carried out. Moreover, the emergency management is also enhanced since the maritime emergency simulation system has been under development since 2014 [2].

ship length is more than 100m.When navigating from upstream to downstream, the ship must pass the lock. As the lock has five steps, the ship has to pass the lock step by step and this process lasts more than 2 hours, therefore, the congestion always exist in this area. Recently, a ship lift is constructed and available for use to transform the small size ships (i.e. less than 100m length) especially for the passage ships.

Among all the maritime accidents, different types of ships are involved. Specifically, cargo ships account for 52%, and oil tanker ranks second, accounting for 13%, while containerships ranks last, accounting for only 5%. It can be seen that container ships have a better situation than other ships though the throughputs of containerships are also high in the Yangtze River.

The midstream is from Wuhu to Three George Dam. In this area, it can also be divided into two parts. From Wuhu to Wuhan, the water depth is 6.0m, and from Wuhan to Three George Dam, the water depth is 4.5m. These two parts are assumed as the midstream because of the decrease of dimension and the large bend of the fairway. The maximum ship DWT for two-way traffic is 5000t. The “Eastern Star” accident, which caused more than 400 fatalities, occurred in this area. However, though the navigation condition is not good at the midstream, few accidents occurred because the low density of traffic. The downstream is from Nanjing to Shanghai. The water depth is 12.5m since 2014, and the maximum ship DWT for two-way traffic is 50 000t with channel width 500m, while one-way traffic for 100 000t ships. It can be seen that the water depth and channel width is limited for such large sized ships. Therefore, in order to mitigate the navigational risk, the traffic separation schemes were introduced in 2005, and there is also a special lane for the small sized ships with water depth less than 7.0m. This waterway also has the distinguishing characteristic of dense traffic because the majority of abovementioned ports with billion throughputs are located in this area, and also there is the disturbance of crossing ships for the ships intends to transport cargo from one side of the Yangtze River to the other side. Many accidents occurred in this waterway, and according to the local regulations, the ocean-going ships navigating in this area are required to have compulsory pilotage.

Fig. 2. Number of maritime accidents in the period of 2006 to 2014

Fig. 3. Different types of ships involved in maritime accidents

The distribution of different types of maritime accidents can also be determined. The collision accidents, which are also the most frequently occurring accidents, rank first with a frequency of 49%. Not under control ships, though not causing accidents when well-handled are also taken consideration, which accounts for 35%. The grounding accidents account for 10%.

As the navigation condition varies in the different waterway area and the downstream poses more risk than others, this paper focuses on the navigational risk in the downstream. Moreover, as collision is the most frequentlyoccurring accident both from previous works and historical data, this paper only focus on the collision risk and identifies the important influencing factors. The remainder of this paper is organized as follows. Section II introduces the safety situation in Yangtze River, Section III presents the collision risk model using Bayesian network, Discussions are carried out in Section IV and conclusions are drawn in Section V. II. MARITIME SAFETY SITUATION IN YANGTZE RIVER From the year 2006 to 2014, the maritime accidents have been steadily decreasing. This means the maritime safety situation has been improving. This can be explained as follows. First, the navigation condition has also improved, as has been mentioned in the introductory part. Second, the maritime safety administration has adopted advanced techniques to enhance maritime safety, for example, the Geographic Information System (GIS) based information system is used for early warning of maritime accidents. Third, the emergency management has also been improved by the GIS based

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Fig. 4. Frequency of different types of maritime accidents

III.MODELING THE COLLISION RISK USING BAYESIAN NETWORK

A. Identification of collision risk influencing factors The majority of the previous works have focused on the influencing factors of maritime safety[3]. Hänninen and



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Pentti[4]used Bayesian networks to analyse the predominant factors of collision risk. Balmat et al. [5]proposed a fuzzy logic based approach to define the risk factor of individual ships, and a decision-making approach has been proposed to define the individual risk[6]. Zhang et al.[7] utilized Bayesian networks to predict the consequence of the maritime accidents in Tianjin Port and Antao et al also used this method for maritime accidents[8]. Zhang et al.[9] analyzed the risk of congestion in the Yangtze River. It can be seen that the above-mentioned researches only focused on the influencing factors of causation factors, and the emergency management, which plays a significant role in reducing the consequences, was always ignored in the previous works. The response actions are also very important considering the dynamic feature of accident development [10-13]. Therefore, this paper intends to consider both the causation factors and emergency management. Wu [14] takes the influencing factors of emergency management into consideration for selection of the best safety control options in the Yangtze River. The effectiveness of emergency management has also been carried out in the Yangtze River [15], and some search and rescue data have also been introduced to evaluate the maritime safety administration performance[16]. The influencing factors of collision risk, including both causation factors and emergency management, can be identified from both previous works and historical data. From the historical data, the influencing factors can be identified. They are wind, visibility, time of day, emergency resources involved, number of people in distress, ship type, ship tonnage, arrival time of tug, hull damage after collision, position of accidents. These variables are all recorded or with detailed description in the historical data and they are assumed as nodes in the Bayesian network. The associated states can also be easily defined, which are shown in Table I. However, the intermediate nodes are much more complex. Some of the intermediate nodes also have descriptions, such as pollution, economic loss, loss of ship, run aground, tug assistance, flooding, number of fatality, which are easy to be obtained. While some influencing factors are introduced for facilitating the modelling process, such as the natural environment, condition after intervention and condition for search and rescue (SAR). The reason why these two variables are introduced is that if there are too many parent nodes for one child node, the conditional probability tables will be too many to understand [17]. Also the intermediate nodes and associated nodes are shown in Table I.

node type Parent node

Intermediate node

Consequence

Node name time of day wind visibility time of day emergency resources involved number of people in distress ship type ship tonnage arrival time of tug hull damage after collision position of accidents pollution economic loss loss of ship run aground tug assistance flooding condition after intervention arrival time of tug collision accidents

Node states day/night Less than 3/from 3 to 6/more than 6 good/bad day/night no/less than 3/more than 3 Less than 3/from 3 to ten/more than 10 cargo ship/dangerous cargo ship Below 500/from 500 to 1000/more than 1000 less than 15min/30min seriously/moderately/slightly bridge area/fairway yes/no negligible/minor/major/catastrophic yes/no feasible/unfeasible feasible/unfeasible quickly/moderately/ slowly good/bad less than 15 min/30min negligible/minor/major/catastrophic

B. Establishment of the qualitative part using expert knowledge The qualitative part is established using the graphical structure of Bayesian network. When establishing this part, several experts are invited to give opinions on this structure. The groups and individuals invited to make expert judgments were: Jiangsu Maritime Safety Administration, the institution in charge of safety in the downstream part of the Yangtze River; Nanjing port tug and lighter company (NJP), a company who have been frequently requested to collision ships handling; the shipping company, SINOTRANS & CSC HOLDINGS CO., LTD; and a number of university professors who had previously worked as seafarers, in a total of five experts. The final graphical structure of this Bayesian network model is shown in Fig. 5. It can be seen that the natural environment is related to the visibility and wind, and considering the time of the day, the condition for SAR is established. Moreover, the condition for SAR, emergency resource involved and the number of people in distress is the parent nodes of the number of fatalities. The ship type especially the dangerous cargo ship may have a large probability to cause pollution, while the loss of ship are influenced by the ship condition (hull damage after collision) and the intervention measures (including tug assistance and run aground). It should be mentioned that in China, the consequences are defined as negligible, minor, major, catastrophic, and the standard to define these classifications is shown in Table II, which has been introduced by Zhang[7] when analyzing the maritime risk in the Tianjin Port.

It should be mentioned that some states of the nodes are derived from the historical data from 2009 to 2012 in the Yangtze River, some of the nodes are described by using linguistic variables rather than numerical data such as the flooding speed, pollution, and economic loss. While some of the nodes are incomplete this makes the nodes to have only two states, such as the visibility. However, from the analysis of the collected data, the result is convincing and reasonable, as will be introduced in the following sections. Some nodes should also be explained. For example, the emergency resources involved include MSA, army, passing by ships, helicopters, and the number of emergency resources involved represent show many organizations have been involved in the search and rescue.

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TABLE I. NODES AND ASSOCIATED STATES FOR COLLISION RISK ANALYSIS

It can be seen from Table II that the fatalities are all the same for the different types of ship tonnage. However, the economic losses differ from the different ship tonnage, therefore, when defining the economic loss, the ship tonnage



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consequences of collision accidents, which is defined in Table II.

is taken into consideration, but the ship tonnage is not the parent node of the number of fatalities. Finally, the number of fatalities and economic loss can be used for defining the

 Fig. 5. Graphical structure of the consequences of collison accidents.

However, as some nodes are established for facilitating the modelling process, these CPTs are much more difficult to obtain while only expert judgments can be utilized. In this paper, the well-known modified IF-THEN rules are used for constructing these CPTs, and the experts used for derivation of the qualitative part are invited again. For detailed information about this method, please refer to the related references [20, 21].

TABLEII. CLASSIFICATION ON MARITIME CONSEQUENCE FROM MOT Ship tonnage

Negligible

Ships over 3000 gross tonnage

Below minor accident

Ships between 500 and 3000 gross tonnage

Below minor accident

Ships below 500 gross tonnage

Below minor accident

Minor Serious injury, or economic loss between 500k and 3000k RMB Serious injury, or economic loss between 200k and 500k RMB Serious injury, or economic loss between 100k and 200k RMB

Major

Catastrophic

1-2 fatalities, or economic loss between 3000k and 5000k RMB

Over 2 fatalities, or economic loss over 5000k RMB

1-2 fatalities, or economic loss between 500k and 3000k RMB

Over 2 fatalities, or economic loss over 3000k RMB

1-2 fatalities, or economic loss between 200k and 500k RMB

Over 2 fatalities, or economic loss over 500k RMB

By introducing this method, the other CPTs can then be defined. Take the natural environment for example, the result is shown in Table IV. It can be seen from this table that the natural environment is assumed to be good, otherwise, the natural environment is assumed to be have a large probability to be normal or bad. TABLE IV. CPT FOR NATURAL ENVIRONMENT Visibility Wind good normal bad

C. Determination of the conditional probability tables The conditional probability tables (CPTs) are derived from two ways. One way is very simple, since historical data exists the conditional probability tables can be obtained by using the statistical data. For example, the CPT for pollution, and the CPT for pollution is shown in Table III. It should be mentioned that from 2009 to 2012 there are 134 collision accidents, and the probabilities are based on these data. From Table III, the cargo ship also has a small probability to cause pollution, while the dangerous cargo ship has a bigger probability to cause pollution, which is verified in the previous work in the Gulf of Finland[18]and in San Francisco[19].

Less 3 0.9 0.1 0

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Less 3 0.1 0.8 0.1

bad 3 to 6 0 0.9 0.1

above 6 0 0.4 0.6

TABLE V. CPT FOR ECONOMIC LOSS

Ship tonnage

TABLE III. CPT FOR POLLUTION OBTAINED BY USING HISTORICAL DATA Cargo ship 0.0086 0.9914

above 6 0.1 0.4 0.5

Another CPT that should be mentioned is the economic loss. As the majority of historical data only recorded whether the ship was lost or not and also whether pollution was caused or not by collision, the economic loss cannot be obtained from the historical data. However, from the standard of Table II, this should be taken into consideration. Therefore, expert judgments are also used here. For the sake of space, only part of the CPT for economic loss is presented here, which is shown in Table V.

pollution Loss of ship

Ship type Yes No

good 3 to 6 0.1 0.8 0.1

negligible minor major catastrophic

Dangerous cargo ship 0.1111 0.8889



yes Below 500 0.44 0.39 0.08 0.09

yes 500 to 3k 0.4 0.35 0.13 0.12

above 3k 0.35 0.3 0.2 0.15

Below 500 0.5 0.4 0.06 0.04

no 500 to 3k 0.4 0.4 0.1 0.1

above 3k 0.3 0.6 0.05 0.05

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Tianjin Port, the probabilities are similar to this result, which makes it convincing. Moreover, the minor accident is a little bit higher than negligible, which is also the same in Tianjin Port.

D. Derivation of the prior information using historical data In this model, all the prior information can be derived from the historical data (134 cases). The prior information of the Bayesian network for collision risk is shown in Table VI.

Another result that should also be carefully handled is the accident rate. In Tianjin Port, the traffic volume is only with the magnitude of 104, while in the Yangtze River, the magnitude of traffic volume is 105.By using the average accident number, the collision accident rate is 3.72×10-4, which is close to the result of the accident rate in Tianjin Port. However, it should be mentioned that this paper only considers the collision accident data, but when considering the whole accident data, the navigational risk of maritime transportation in the Yangtze River is a little higher than Tianjin Port, and this research will be demonstrated in the future. This is because the distinguishing characters of the fairway in the Yangtze River, which are narrow channel, limited depth, dense traffic, and disturbance of crossing ships. Therefore, in the Yangtze River, the crew need to have a specific seafarer certificate, otherwise, the ship needs compulsory pilotage.

TABLE VI. PRIOR INFORMATION FOR DEVELOPED BAYESIAN NETWORK node visibility wind time of day emergency resources involved number of people in distress ship type arrival time of tug (min) hull damage after collision ship tonnage position of accidents

State 1 good 0.791 less 3 0.112 day 0.239 no 0.724 Less 3 0.667 cargo 0.784 Less 15 0.709 seriously 0.597 below 500 0.3 bridge area 0.149

State 2 bad 0.209 3 to 6 0.754 night 0.761 1 to 2 0.209 3 to 10 0.296 dangerous 0.216 Less 30 0.291 moderately 0.254 500 to 3k 0.3 fairway 0.851

State 3 Above 6 0.134 above 3 0.067 above 10 0.037 slightly 0.149 above 3k 0.4

V. CONCLUDING REMARKS The main contribution of this work is to develop a Bayesian network by considering both causation factors and emergency management. By taking the influencing factors of emergency management into consideration, it should be more reasonable to explain the accident development and the predominant factors can also be discovered, which will be conducted in the future. The result of this paper is also reasonable by comparing the result with Tianjin Port. However, the result can be further analysed in the future, such as sensitivity analysis of the influencing factors. Moreover, as the grounding accident is also frequently occurring, the risk of collision and grounding can be considered together in the future to gain insights from the accident data.

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IV.RESULT ANALYSIS By introducing the prior information and the CPTs, the final consequences of collision risk in the Yangtze River can be achieved. The result is shown in Fig. 6 where it can be seen that the number of fatality is “no” with a probability of 0.937, is “from 1 to 2” with a probability of 0.056, and is “above 2” with a probability of 0.007. This is quite similar with the result in the Yangtze River that among 134 collision cases, only 1 accident caused 3 fatalities. This accident occurred in the number 153 black buoy at 0930. Moreover, only seven cases have caused fatalities, precisely speaking,only one case caused two fatalities and other six accidents caused only one fatality. It can also be seen from the result that major accidents in the final consequences of collision have a little higher probability than the of the state “from 1 to 2” fatalities, and also the catastrophic is also a little higher than the state of “above 2”, which is because the final consequence have taken the economic loss into consideration. This is reasonable when comparing this result with the Tianjin Port[7]. In this paper, the collision risk is negligible with a probability of 0.456, is minor with a probability of 0.477, is major with a probability of 0.058, and is catastrophic with a probability of 0.009. In

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ACKNOWLEDGMENT The research presented in this paper was sponsored by a grant from the Key Project in the National Science & Technology Pillar Program (Grant No.2015BAG20B05), grants from National Science Foundation of China (Grant No. 51609194), grants from the special funds of Hubei Technical Innovation Project (Grant No. 2016AAA055) and the Fundamental Research Funds for the Central Universities (WUT: 2017IVA103).



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 Fig. 6. Result of consequences of collision risk in the Yangtze River.

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