基于LSTM的舰船运动姿态短期预测

舰船的六自由度运动状态形成复杂的非线性过程,运动姿态会受到耦合作用、不定周期、噪声信号以及混沌特性等因素的干扰,因此很难得到精确的预测结果.为了提升舰船运动姿态的预测精度,利用舰船时间序列的特点,建立了基于长短期记忆单元(LSTM)模型,对其进行了舰船姿态预测仿真,将结果与时间序列分析法的结果进行对比.实例分析表明:基于LSTM模型的预测方法具有精确度高、易实现的特点.这为舰船运动短期预测提供了一个新的思路和方法.     

武汉市过长江交通流量特征分析

武汉市过长江交通是武汉市最主要的交通流向之一。根据流量观测数据分析长江交通流量特征,表明武汉市区过长江交通饱和,交通需求总量大,出行目的种类多。过长江交通没有明显、固定的高峰日和高峰小时,除通勤交通外。还有公务等大量非通勤交通。其中武昌与汉口之间交通是主流向,需要适当新增通道缓解过江难问题。     

船摇前馈控制补偿效果及优化方法研究

雷达隔离船摇的主要手段之一.针对船摇前馈在实际工程应用中补偿效果不理想的问题,通过对前馈复合控制原理、船摇前馈实现方法和效果影响因素的研究,提出了工程实现的优化方法.采用时间序列分析方法进行船摇数据预报可以提高近一个数量级的预报精度,从而直接提高船摇前馈补偿量的计算精度和补偿效果.通过在前馈补偿环节中加入自适应设计以实现船摇前馈信息有效平稳地加入控制环路.此外,选择适宜的时机加入船摇前馈也是工程应用的一个重要因素.     

船舶液压起重机延迟反馈吊重消摆控制

由于操纵和工作环境的变化,起重机的吊重在工作过程中会产生摆动,这种摆动降低了起重机的工作效率和安全性能。文章以集美大学轮机工程实验中心船舶液压起重机为研究对象,采用机电液仿真建模技术及拉格朗日方程,在MATLAB Simulink仿真软件平台上,建立起重机操作液压系统及吊重摆动模型,采用与试验数据对比的方法对所建立的模型进行验证。设计基于吊重摆动位置延迟反馈的控制器,通过将延迟反馈信号叠加到操作信号上的方法实现吊重的消摆控制。结果表明,在各种操作情况下,延迟反馈控制器均能很好地抑制吊重的摆动。     

A Pareto-improving hybrid rationing and pricing policy with multiclass network equilibria

This paper extends the work on Pareto-improving hybrid rationing and pricing policy for general road networks by considering heterogeneous users with different values of time. Mathematical programming models are proposed to find a multiclass Pareto-improving pure road space rationing scheme (MPI-PR) and multiclass hybrid rationing and pricing schemes (MHPI and MHPI-S). A numerical example with a multimodal network is provided for comparing both the efficiency and equity of the three proposed policies. We discover that MHPI-S can achieve the largest reduction in total system delay, MHPI can induce the least spatial inequity and MHPI-S is a progressive policy which is appealing to policy makers. Furthermore, numerical results reveal that different classes of users react differently to the same hybrid policies and multiclass Pareto-improving hybrid schemes yield less delay reduction when compared to their single-class counterparts.     

Aggregating mobile object trajectories: cumulative time geographic density estimation for GPS data

We present new approaches that expand upon the time geographic density estimation (TGDE) framework previously employed to estimate potential path trees. In the past, TGDE metrics have identified possible locations an individual moving object may have passed between, given known origin and destination points. This paper utilizes a new form of TGDE to investigate taxicab GPS traces over a specified time horizon with position ‘gaps’. To this end, we propose a new extension to the TGDE framework, TGDE-C, which is used to determine the cumulative TGDE values for a group of GPS traces, at a given location. These metrics are applied to multiple taxis and allow for time of day analysis. Additionally, we combine these new extensions with existing TGDE metrics that allow us to determine how accessible individual or groups of vehicles are to urban opportunities.     

Formulating cargo inventory costs for liner shipping network design

This study examines how to incorporate the inventory costs of containerized cargoes into existing liner service planning models such that the designed networks could be improved while not causing extra modeling/computational burden. Two approaches are compared: (i) not considering the inventory costs at all and (ii) incorporating the inventory costs associated with onboard time and those related to transshipment by assuming a fixed connection time. The two models are compared with the ideal model capturing the exact inventory costs on a route choice problem and a capacity planning problem based on extensive randomly generated and practical numerical experiments. The results show that: first, ignoring the inventory costs in service planning models may lead to network design with much higher costs (poor network design decisions); second, in service planning models assuming weekly frequency, the inventory costs associated with onboard time could be formulated exactly, and those related to the connection time of weekly services could be approximated by assuming fixed connection time of 3.5 days for ports with 1 day’s minimum connection time and 4.5 days for ports with 2 days’ minimum connection time.     

Mathematically calculating the transit time of cargo through a liner shipping network with various trans-shipment policies

This paper derives the mathematical expressions for the transit time of cargo through a liner shipping network. Main efforts are devoted to deriving the calculation expressions of the connection time of cargo during trans-shipment. For the forward and many-to-one trans-shipment policies, we conduct a minor correction towards the expressions in existing studies to improve the completeness. Meanwhile, we propose an alternative but more straightforward calculation method for connection time which bypasses the complicated inductive argument in existing studies. Then we introduce two new trans-shipment policies: backward trans-shipment and one-to-many trans-shipment, and mathematically calculate the corresponding connection times. Numerical experiments also deliver some managerial insights into the effectiveness of backward trans-shipment in transit time control.     

A stochastic model of traffic flow: Theoretical foundations

In a variety of applications of traffic flow, including traffic simulation, real-time estimation and prediction, one requires a probabilistic model of traffic flow. The usual approach to constructing such models involves the addition of random noise terms to deterministic equations, which could lead to negative traffic densities and mean dynamics that are inconsistent with the original deterministic dynamics. This paper offers a new stochastic model of traffic flow that addresses these issues. The source of randomness in the proposed model is the uncertainty inherent in driver gap choice, which is represented by random state dependent vehicle time headways. A wide range of time headway distributions is allowed. From the random time headways, counting processes are defined, which represent cumulative flows across cell boundaries in a discrete space and continuous time conservation framework. We show that our construction implicitly ensures non-negativity of traffic densities and that the fluid limit of the stochastic model is consistent with cell transmission model (CTM) based deterministic dynamics.     

An adaptive information fusion model to predict the short-term link travel time distribution in dynamic traffic networks

As intelligent transportation systems (ITS) approach the realm of widespread deployment, there is an increasing need to robustly capture the variability of link travel time in real-time to generate reliable predictions of real-time traffic conditions. This study proposes an adaptive information fusion model to predict the short-term link travel time distribution by iteratively combining past information on link travel time on the current day with the real-time link travel time information available at discrete time points. The past link travel time information is represented as a discrete distribution. The real-time link travel time is represented as a range, and is characterized using information quality in terms of information accuracy and time delay. A nonlinear programming formulation is used to specify the adaptive information fusion model to update the short-term link travel time distribution by focusing on information quality. The model adapts good information by weighing it higher while shielding the effects of bad information by reducing its weight. Numerical experiments suggest that the proposed model adequately represents the short-term link travel time distribution in terms of accuracy and robustness, while ensuring consistency with ambient traffic flow conditions. Further, they illustrate that the mean of a representative short-term travel time distribution is not necessarily a good tracking indicator of the actual (ground truth) time-dependent travel time on that link. Parametric sensitivity analysis illustrates that information accuracy significantly influences the model, and dominates the effects of time delay and the consistency constraint parameter. The proposed information fusion model bridges key methodological gaps in the ITS deployment context related to information fusion and the need for short-term travel time distributions.