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Xin, X, Yang, Z, Liu, K, Zhang, J and Wu, X (2022) Multi-stage and multi-topology analysis of ship traffic complexity for probabilistic collision detection. Expert Systems with Applications, 213. p. 118890. ISSN 0957-4174
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Multi-stage and multi-topology analysis of ship traffic complexity for probabilistic collision detection .pdf - Accepted Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (6MB) | Preview |
Abstract
Maritime traffic situational awareness plays a vital role in the development of intelligent transportation-support systems. The state-of-the-art study focuses on near-miss collision risk between/among ships but reveals challenges in estimating large-scale traffic situations associated with dynamic and uncertain ship motions at a regional level. This study develops a systematic methodology to evaluate ship traffic complexity to comprehend the traffic situation in complex waters. In the new methodology, the topological and evolutionary characteristics of ship traffic networks and the uncertainty in ship movements are considered simultaneously to realise probabilistic collision detection. The methodology, through the effective integration of probabilistic conflict estimation and traffic complexity modelling and assessment, enables the evaluation of traffic complexity in a fine-grained hierarchical manner. With the AIS-based trajectory data collected from the world's largest port (i.e. Ningbo-Zhoushan Port), a thorough validation of the evaluation performance is conducted and demonstrated through scenario analysis and model robustness. Moreover, some critical research results are obtained in terms of traffic network heterogeneity analysis; statistics including occurrence frequency, temporal distribution, life cycle, and transition probability of traffic complexity patterns; and correlation examination between the number of ships and traffic complexity patterns. These findings offer new insights into improving maritime traffic awareness capabilities and promoting maritime traffic safety management.
| Item Type: | Article |
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| Uncontrolled Keywords: | Artificial Intelligence & Image Processing; 01 Mathematical Sciences; 08 Information and Computing Sciences; 09 Engineering |
| Subjects: | H Social Sciences > HD Industries. Land use. Labor > HD61 Risk Management H Social Sciences > HE Transportation and Communications T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Engineering |
| Publisher: | Elsevier |
| SWORD Depositor: | A Symplectic |
| Date Deposited: | 07 Nov 2022 12:11 |
| Last Modified: | 29 Sep 2023 00:50 |
| DOI or ID number: | 10.1016/j.eswa.2022.118890 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/18038 |
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