Abstract:The trajectory of the inland waterway ship is important and useful in analysing the features of 16 the ship behaviour and simulating traffic flows. In the proposed research, a method is designed to restore 17 the trajectory of an inland waterway ship based on the Automatic Identification System (AIS) data. Firstly, 18 three rules are developed to identify and remove the inaccurate data, based on the reception range of the 19 received AIS data and the manoeuvring characteristics of the inland waterway ship. Secondly, the method 20 of restoring the full trajectory incorporating navigational features of the inland waterway ship is proposed 21 to model the ship trajectory. The trajectory is characterised by three types (line, curve and arc) and five 22 steps (line, curve, arc, curve and line) during the turning section. In order to validate the proposed method, 23 the AIS data of two inland waterway ships collected from three AIS-base-stations is selected for the 24 analysis, all inaccurate AIS data is identified and removed by the use of three cleansing rules. The results 25show that the three developed rules can effectively identify the inaccurate AIS data. The AIS data collected 26 by an AIS-shipboard-unit is then used to: (1) restore the ship trajectory, and (2) to restore the full trajectory in an effective manner by using AIS data. 33

The information on the Closest Point of Approach (CPA) of another vessel to own ship is required in a potential collision situation as it helps determines the risk to each vessel. CPA is usually calculated based on the speed and direction of the approaching ship neglecting the Change Of Speed (COS) and the Rate Of Turn (ROT). This will make the CPA less useful. To improve the CPA calculation, Automatic Identification System (AIS) information containing the Speed Over Ground (SOG), Course Over Ground (COG), COS and ROT is used. Firstly, a model using these four factors is built to predict ship positions better. Secondly, a three-step CPA searching method is developed. The developed CPA calculation method can assist in informing the navigation decisions and reducing unnecessary manoeuvres. Through the analysis of a real collision scenario, this paper shows that the proposed method can help identify and warn of anomalous ship behaviours in a realistic time frame.

Navigational accidents (collisions and groundings) account for approximately 85% of mari-time accidents, and consequence estimation for such accidents is essential for both emergency resource allocation when such accidents occur and for risk management in the framework of a formal safety assessment. As the traditional Bayesian network requires expert judgement to develop the graphical structure, this paper proposes a mutual information-based Bayesian network method to reduce the requirement for expert judgements. The central premise of the proposed Bayesian network method involves calculating mutual information to obtain the quantitative element among multiple influencing factors. Seven-hundred and ninety-seven historical navigational accident records from 2006 to 2013 were used to validate the methodology. It is anticipated the model will provide a practical and reasonable method for consequence estimation of navigational accidents.

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