A dynamic model for marginal cost pricing of port infrastructures

ABSTRACT

A dynamic model for marginal cost pricing of port infrastructures links costs to system performance by combining a power-law function with time-dependent queueing analysis. Additionally, the model incorporates the marginal cost of capacity, including the effects of economies of scale. This allows the calculation of the marginal cost price under a dynamic framework. The model accounts for nonlinear behaviour of port demand, which is sensitive to price and service levels. The effects over time of cost and service levels on the port’s operational performance are quantified. The proposed model allows determining the optimal timing for capacity investment. The model is a starting point for the application of marginal cost pricing to ports. However, for practical application of such pricing method it is necessary to apply a system’s approach, as productivity and costs must be assessed at the terminal’s component level. This should allow the derivation of a marginal cost function at the terminal’s component level.     

CSM-td信号集中监测系统智能分析方案

描述了10版信号集中监测(CSM)系统智能化发展实现的方向及逻辑模型。该系统将信号集中监测作为系统输入源,通过测试数据与数据仓库中的数据模型进行分析比较,及时发现设备隐患,分析产生问题的原因,给出解决问题的方案。     

基于船舶领域和MMG的最晚施舵点/CRI研究

提出了一种基于MMG和船舶领域的LSP计算模型,以及改进的空间碰撞危险度（space collision risk index,SCRI）/时间碰撞危险度（time collision risk index,TCRI）模型,并进行了计算机仿真.结果表明：水动力模型精度可满足研究与实践要求;基于二分法的LSP模型能快速、可靠收敛;LSP和SCRI/TCRI模型更符合海员通常做法和避碰规则的公认理解.     

基于SIMULINK的岸边集装箱起重机减摇系统仿真

在岸边集装箱起重机起升机构和小车运行机构模型的基础上建立简化的单摆模型,通过对模型的受力分析建立非线性方程.同时在MATLAB的SIMUUNK环境下对此模型进行动态仿真,达到通过控制小车的驱动力来控制吊具的摆角的目的.     

Cycle commuting in Belgium: Spatial determinants and ‘re-cycling’ strategies

This paper attempts to explain the spatial variation of the use of a bicycle for commuting to work at the level of the 589 municipalities in Belgium. Regression techniques were used and special attention was paid to autocorrelation, heterogeneity and multicollinearity. Spatial lag models were used to correct for the presence of spatial dependence and a disaggregated modelling strategy was adopted for the northern and southern parts of the country. The results show that much of the inter-municipality variation in bicycle use is related to environmental aspects such as the relief, traffic volumes and cycling accidents. Town size, distance travelled and demographic aspects also have some effect. In addition, there are regional differences in the effects of the structural covariates on bicycle use: the impact of variables such as traffic volume and cycling accidents differs substantially between the north and the south of the country. This paper also suggests that high rates of bicycle use in one municipality stimulate cycling in neighbouring municipalities, and hence that a mass effect can be initiated, i.e. more cycle commuting encourages even more commuters in the area to cycle. These findings provide some recommendations for decision-makers wishing to promote a shift from car to bicycle use.     

圆电极电磁流量计干标定模型及仿真

目前在电磁流量计的干标定研究中，为方便公式推导，通常将实际的圆电极简化为方电极模型．这种简化在电极尺寸较大时，会引入不必要的计算误差，影响流量计的标定精度．本文从圆电极模型出发，论述了其对应权重函数的计算过程．并针对大尺寸圆电极模型进行理想化计算，得到其点电极模型，并运用此模型进行了仿真研究．     

Design and evaluation of an integrated inventory and transportation system

This paper proposes the adoption of an integrated inventory and transportation system (IITS) to minimize the total costs of inventory and transportation. A non-linear programing is developed by analyzing transportation and inventory costs with one supplier and many retailers in the distribution environment. The paper compares the proposed model with the traditional approach in computing total costs with numerical data. The results indicate that the total costs can be optimized by adopting integrated programing rather than the traditional approach, along with achieving improved customer service levels. In particular, sensitivity analysis is applied to determine the performance of the IITS under various transportation costs, holding costs and shortage costs. It shows that the transportation cost per unit is most sensitive in the proposed model. In this situation, the IITS is more effective for cost saving when set-up cost, holding and shortage costs are high, but is less effective for situations involving high per-unit transportation costs.     

Privacy protection method for fine-grained urban traffic modeling using mobile sensors

With the ubiquitous nature of mobile sensing technologies, privacy issues are becoming increasingly important, and need to be carefully addressed. Data needs for transportation modeling and privacy protection should be deliberately balanced for different applications. This paper focuses on developing privacy mechanisms that would simultaneously satisfy privacy protection and data needs for fine-grained urban traffic modeling applications using mobile sensors. To accomplish this, a virtual trip lines (VTLs) zone-based system and related filtering approaches are developed. Traffic-knowledge-based adversary models are proposed and tested to evaluate the effectiveness of such a privacy protection system by making privacy attacks. The results show that in addition to ensuring an acceptable level of privacy, the released datasets from the privacy-enhancing system can also be applied to urban traffic modeling with satisfactory results. Albeit application-specific, such a “Privacy-by-Design” approach would hopefully shed some light on other transportation applications using mobile sensors.     

Short-term forecasting of high-speed rail demand: A hybrid approach combining ensemble empirical mode decomposition and gray support vector machine with real-world applications in China

Short-term forecasting of high-speed rail (HSR) passenger flow provides daily ridership estimates that account for day-to-day demand variations in the near future (e.g., next week, next month). It is one of the most critical tasks in high-speed passenger rail planning, operational decision-making and dynamic operation adjustment. An accurate short-term HSR demand prediction provides a basis for effective rail revenue management. In this paper, a hybrid short-term demand forecasting approach is developed by combining the ensemble empirical mode decomposition (EEMD) and grey support vector machine (GSVM) models. There are three steps in this hybrid forecasting approach: (i) decompose short-term passenger flow data with noises into a number of intrinsic mode functions (IMFs) and a trend term; (ii) predict each IMF using GSVM calibrated by the particle swarm optimization (PSO); (iii) reconstruct the refined IMF components to produce the final predicted daily HSR passenger flow, where the PSO is also applied to achieve the optimal refactoring combination. This innovative hybrid approach is demonstrated with three typical origin–destination pairs along the Wuhan-Guangzhou HSR in China. Mean absolute percentage errors of the EEMD-GSVM predictions using testing sets are 6.7%, 5.1% and 6.5%, respectively, which are much lower than those of two existing forecasting approaches (support vector machine and autoregressive integrated moving average). Application results indicate that the proposed hybrid forecasting approach performs well in terms of prediction accuracy and is especially suitable for short-term HSR passenger flow forecasting.