Browsing by Author "Liu, Lei"

Now showing 1 - 3 of 3

Assessment of the feasibility of vessel trains in the ocean shipping sector
(2024-05) Liu, Lei; Liu, Kaiyuan; Shibasaki, Ryuichi; Zhang, Yong; Zhang, Mingyang
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This paper introduces the concept of vessel train, a new mode in maritime transport offering both environmental and economic advantages. The study provides a framework for analyzing the economic feasibility of vessel train in ocean shipping, focusing on four different scenarios to evaluate its impact on navigation speed and productivity for both round-trip and year-long operations. Using the container ship route from Yangshan Port to the Port of Piraeus as a case study, the research compares vessel train with traditional shipping methods by categorizing shipping costs. It also includes a sensitivity analysis considering factors like route length, frictional resistance reduction, and variations in the container freight index, fuel prices, and carbon tax rates. The findings indicate that vessel train can reduce transportation costs and fuel emissions. The paper concludes that vessel train could significantly contribute to a competitive and sustainable future in shipping.
Is the spatial-temporal dependence model reliable for the short-term freight volume forecast of inland ports? A case study of the Yangtze River, China
(2021-09-08) Liu, Lei; Zhang, Yong; Chen, Chen; Hu, Yue; Liu, Cong; Chen, Jing
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
The purpose of this study is to investigate whether spatial-temporal dependence models can improve the prediction performance of short-term freight volume forecasts in inland ports. To evaluate the effectiveness of spatial-temporal dependence forecasting, the basic time series forecasting models for use in our comparison were first built based on an autoregression integrated moving average model (ARIMA), a back-propagation neural network (BPNN), and support vector regression (SVR). Subsequently, combining a gradient boosting decision tree (GBDT) with SVR, an SVR- GBDT model for spatial-temporal dependence forecast was constructed. The SVR model was only used to build a spatial-temporal dependence forecasting model, which does not distinguish spatial and temporal information but instead takes them as data features. Taking inland ports in the Yangtze River as an example, the results indicated that the ports’ weekly freight volumes had a higher autocorrelation with the previous 1–3 weeks, and the Pearson correlation values of the ports’ weekly cargo volume were mainly located in the interval (0.2–0.5). In addition, the weekly freight volumes of the inland ports were higher depending on their past data, and the spatial-temporal dependence model improved the performance of the weekly freight volume forecasts for the inland river. This study may help to (1) reveal the significance of spatial correlation factors in ports’ short-term freight volume predictions, (2) develop prediction models for inland ports, and (3) improve the planning and operation of port entities.
A probabilistic analytics method to identify striking ship of ship-buoy contact at coastal waters
(2022-12-15) Liu, Lei; Zhang, Mingyang; Hu, Yue; Zhu, Wei; Xu, Sheng; Yu, Qing
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
The identification of the ship that contact with the buoy can provide evidence for accident accountability. To this aim, the paper develops a probabilistic analytics method to evaluate the ship-buoy contact risk for the striking ship identification at the coastal areas by combining buoy domain and bounding box models. The method makes use of Automatic Identification System (AIS) data and navigational buoy data. Firstly, an AIS-based probabilistic buoy domain model is adopted for the determination of the safety boundary of the buoy to detect potential striking ships with a higher contact probability. Then, the bounding boxes of the navigational buoy and the detected potential striking ships are developed to detect the real striking ship by analyzing the interaction be-tween the ship bounding box and the buoy bounding box. Finally, the probabilistic analytics method is demonstrated in the South China Sea and validated using historical ship-buoy contact records. Results indicated that, from a probabilistic perspective, the safety buoy domain (critical boundary) existed with diverse distances dynamically. The proposed method could assist the identification of striking ships while aiding the definition of the safety buoy domain for preventing ship-buoy contacts. As a result, it has the potential to support the development of ship-buoy contact risk management and assist surveillance operators and master on board by improving their cognitive abilities in dangerous traffic scenarios.