TY - JOUR
T1 - Navigational risk assessment of inland waters based on bi-directional PSO-LSTM algorithm and ship maneuvering characteristics
AU - Zhang, Lei
AU - Zhu, Yuxuan
AU - Valdez Banda, Osiris A.
AU - Du, Lei
AU - Gan, Langxiong
AU - Li, Xiaobin
N1 - Publisher Copyright:
© 2024 Elsevier Ltd
PY - 2024/10/15
Y1 - 2024/10/15
N2 - Inland waterways are characterized by various bends, complex intersections and high traffic density. This complex inland navigation environment brings challenges to vessel traffic service (VTS) in managing inland water traffic. We propose a method to assess navigational risk of inland waters based on bi-directional PSO-LSTM algorithm and ship maneuvering characteristics. First, a bi-directional PSO-LSTM based ship trajectory restoration model is established to repair the Automatic identification System (AIS) data. Subsequently, ship behavioral characteristics are extracted from these repaired AIS data, based on which the model of ship navigation risk assessment in inland waters is established using window approach. Several case studies are conducted to verify this proposed method. The promising results attest that this proposed bi-directional PSO-LSTM algorithm outperforms the traditional neural network and trajectory restoration models. In addition, this navigational risk assessment of inland waters can effectively identify complex waters with high navigational risks, which are consistent with the actual situation. This research helps support VTS in regulating ship traffic in inland waters, thereby ensuring the safety of ship navigation on inland waterways.
AB - Inland waterways are characterized by various bends, complex intersections and high traffic density. This complex inland navigation environment brings challenges to vessel traffic service (VTS) in managing inland water traffic. We propose a method to assess navigational risk of inland waters based on bi-directional PSO-LSTM algorithm and ship maneuvering characteristics. First, a bi-directional PSO-LSTM based ship trajectory restoration model is established to repair the Automatic identification System (AIS) data. Subsequently, ship behavioral characteristics are extracted from these repaired AIS data, based on which the model of ship navigation risk assessment in inland waters is established using window approach. Several case studies are conducted to verify this proposed method. The promising results attest that this proposed bi-directional PSO-LSTM algorithm outperforms the traditional neural network and trajectory restoration models. In addition, this navigational risk assessment of inland waters can effectively identify complex waters with high navigational risks, which are consistent with the actual situation. This research helps support VTS in regulating ship traffic in inland waters, thereby ensuring the safety of ship navigation on inland waterways.
KW - AIS data
KW - Inland waters
KW - Navigational risk assessment
KW - Ship trajectory restoration
UR - http://www.scopus.com/inward/record.url?scp=85198014065&partnerID=8YFLogxK
U2 - 10.1016/j.oceaneng.2024.118628
DO - 10.1016/j.oceaneng.2024.118628
M3 - Article
AN - SCOPUS:85198014065
SN - 0029-8018
VL - 310
JO - Ocean Engineering
JF - Ocean Engineering
M1 - 118628
ER -