基于内在因素提取的内河航运量神经网络预测方法

内河航运量预测是内河航道网规划的依据,利用主成分分析法提取影响内河航运量的内在因素,利用内在影响因素与内河航运量之间的联系,结合BP神经网络建立模型对内河航运量进行预测。算例表明,该模型可以提高预测精度。     

基于系统动力学的干散货航运市场运力供给研究

运力过剩是现阶段国际干散货航运市场面临的重要难题,引起干散货航运市场持续低迷.为了揭示干散货航运市场运力供给的发展,阐述了干散货航运市场运力供给现状以及未来的发展趋势,应用系统动力学方法建立未来运力供给模型,对影响干散货航运市场运力供给的各种因素及相互制约关系进行了分析,提出了应对运力过剩问题的若干调整策略.     

Impacts of the Belt and Road Initiative in the Indian subcontinent under future port development scenarios

ABSTRACT

The aim of this paper is twofold: the first is to assess the extent to which current port development initiatives contribute to securing the status of the Port of Colombo as a regional transshipment (T/S) hub to serve the Indian subcontinent; and the second is to generate plausible future development scenarios for the maritime industry in the context of the Belt and Road Initiative (BRI). A scenario analysis method is applied to examine the past trends and to build future development scenarios. The results showed that major Indian ports have experienced a significant growth in cargo volumes and vessel traffic. South and East Indian coastal ports continue to use the Port of Colombo as a T/S port in tandem with the corridors connected to the Indian Ocean. Scenario analysis highlights the growing importance of BRI-centric land-based economic corridors, which would generate a large amount of cargos from hinterlands up to China. This would be further fueled through the Great Mekong region-driven industrialization, which would add to the west-bound maritime cargo volume. The paper concludes that the increased capital influx from China would more likely to result in a substantial development of the present port and road/rail infrastructure in Sri Lanka.     

Neural network-based fuel consumption estimation for container ships in Korea

ABSTRACT

Due to the outstanding strength of advanced machine-learning techniques, they have become increasingly common in predictive studies in recent years, particularly in predicting ship energy performance. In constructing predictive models, prior studies have mostly employed vessels’ technical parameters to establish machine-learning algorithms. To bridge this research gap and enable wider applications, this paper presents the design of a multilayer perceptron artificial neural network (MLP ANN) as a machine-learning technique to estimate ship fuel consumption. We utilized the real operational data from 100–143 container ships to estimate fuel consumption for five different container ships grouped by size. We compared the performance of two ANN models and two multiple-regression models. Four input parameters (sailing time, speed, cargo weight, and capacity) were included in the first ANN and the first regression model, while the other two models only consider two inputs from physical function. The mean absolute percentage error of the ANN models with four inputs was the smallest and less than those in extended statistical models, demonstrating the MLP’s superiority over the statistical model. The MLP ANN model can thus be applied to confirm the effectiveness of the slow-steaming method for achieving energy efficiency.     

Effect of a speed reduction of containerships in response to higher energy costs in Sulphur Emission Control Areas

The objective of this paper is to explore the possible consequences of the future low-sulphur fuel requirements in Sulphur Emission Control Areas (SECA) on vessel speed, from the standpoint of the container shipping industry. Rational energy use, speed reduction, and revenues are closely related in the container shipping sector because speed reductions may provide substantial energy and cost savings. The operators could consider reducing their speed in SECA in order to save on fuel that will become relatively expensive. However, to maintain a weekly frequency without adding new ships, such a behaviour implies that the required speed at sea outside the SECA area increases. This paper aims to investigate if such a difference in speed is cost-effective, and if the increase in speed outside SECA may result in an increase in CO₂ emissions of the total cycle. We propose a cost model that estimates the cost-minimising combination of speeds inside and outside SECA, and the resulting CO₂ emissions of the liner service. Applying this model to representative liner services serving North Europe, we find that differentiating speed accordingly slightly decreases total costs and increases CO₂ emissions in a similar way. The results are sensitive to the price of low-sulphur fuels, the part of the cycle in SECA and the number of ships deployed in the service.     

发展广州船舶交易市场的思考

简述了大力发展广州船舶交易市场的意义,分析了广州船舶交易市场发展的有利条件和现状,探讨了市场发展缓慢的原因,为寻求发展策略提供思路.     

关于我国航运企业航运附属公司注册地税务筹划问题的探讨

税收支出是企业经营活动中的一项重要支出。对我国航运企业进行税务筹划研究,有利于降低我国航运企业税负水平,提高我国航运企业的国际竞争力。     

船舶防海盗综合机制研究

2010年全球海盗兴风作浪,被劫持船舶及人质数量均创历史新高。为引起国际社会对海盗问题的关注,国际海事组织将2011年世界海事日主题定为＂协调一致应对海盗＂。通过分析2010年海盗发展态势,本文从主管机关、航运企业、船舶及海军四方面提出切实可行的应对措施,减少海盗对航运业的影响,降低被劫持的风险。     

沿江高速铁路引入合肥枢纽的关键问题

规划年度(2030年)合肥枢纽将形成衔接9个方向、13条线路的大型放射状枢纽.但随着沿江高速铁路建设的提前,沿江高速铁路与既有沪蓉铁路在枢纽内共通道能力是否满足需求、枢纽的客运系统布局和客运站分工是否合理、引入车站站型方案是否最优是面临的关键问题.本文根据研究年度(近期2035年、远期2045年)的枢纽客运量及车流特点,结合既有沪蓉铁路的功能定位及分工调整,对枢纽内主要客运站和区段能力进行分析,对引入车站站型进行研究.通过定量和定性分析,从契合城市总体规划、运输组织的便捷性、线路平面条件、工程实施难易度、工程建设条件、投资等多方面综合比选,优化了枢纽的客运系统布局和客运站分工,确定了合理的枢纽引入方案.     

大武至久治公路门堂黄河大桥静载试验检测分析

青海省公路科研勘测设计院在大武至久治高速公路辅道工程测设时,线位在久治县门堂乡须跨越黄河,在制定跨越方案时考虑利用大久路现有的门堂黄河大桥(1995年建成通车),但该桥目前已正常服役近二十年,且在近期通过重车较多,为了解该桥实际受力状况,判断该桥上部结构目前承载能力能否满足原设计要求,对该桥进行了外观普查及静、动载荷试验。本文主要对该桥的静载试验做详细的阐述。