A predictive analytics method for maritime traffic flow complexity estimation in inland waterways

Mingyang Zhang, Di Zhang, Shanshan Fu, Pentti Kujala and Spyros Hirdaris

Reliability Engineering and System Safety, 2022, vol. 220, issue C

Abstract: Maritime traffic flow complexity is the factor that presents in most existing maritime safety analysis methods. It is considered as one of the main influencing factors affecting maritime safety. It can be estimated quantitatively through the analysis of traffic data. To explore maritime ship traffic in more detail, a predictive analytics method utilizing the Lempel-Ziv algorithm and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) for traffic safety management is proposed. The Lempel-Ziv algorithm quantifies the entropy for the evaluation of the irregularity and unpredictability of ship travel time sequences, and TOPSIS ranks complexity. The results presented utilize Automatic Identification System (AIS) data corresponding to complex inland waterway traffic scenarios encountered in the Yangtze River. They show that high complexity implies ship travel time sequences are neither periodic nor stochastic, but dependent on the evolution patterns of traffic encounters. A correlation analysis of traffic flow complexity with the number of maritime accidents is carried out, showing that higher traffic flow complexity may result in more unwanted events. It is therefore concluded that the proposed method may help to (1) differentiate traffic flow complexity accurately, indicating that the higher the complexity value is, the higher the irregularity and unpredictability of maritime traffic flow are, (2) provide helpful reference for optimizing traffic management over the life cycle of fleet operations as well as maritime safety management of high traffic flow complexity areas.

Keywords: Traffic safety; Maritime operations; Inland waterways; Artificial intelligence; Lempel-Ziv algorithm; TOPSIS (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (15)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:220:y:2022:i:c:s0951832021007870

DOI: 10.1016/j.ress.2021.108317

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