The effects of activity-travel context and individual attitudes on car-sharing decisions under travel time uncertainty: A hybrid choice modeling approach

People’s daily decision to use car-sharing rather than other transport modes for conducting a specific activity has been investigated recently in assessing the market potential of car-sharing systems. Most studies have estimated transport mode choice models with an extended choice set using attributes such as average travel time and costs. However, car-sharing systems have some distinctive features: users have to reserve a car in advance and pay time-based costs for using the car. Therefore, the effects of activity-travel context and travel time uncertainty require further consideration in models that predict car-sharing demand. Moreover, the relationships between individual latent attitudes and the intention to use car-sharing have not yet been investigated in much detail. In contributing to the research on car-sharing, the present study is designed to examine the effects of activity-travel context and individual latent attitudes on short-term car-sharing decisions under travel time uncertainty. The effects of all these factors were simultaneously estimated using a hybrid choice modeling framework. The data used in this study was collected in the Netherlands, 2015 using a stated choice experiment. Hypothetical choice situations were designed to collect respondents’ intention to use a shared-car for their travel to work. A total of 791 respondents completed the experiment. The estimation results suggest that time constraints, lack of spontaneity and a larger variation in travel times have significant negative effects on people’s intention to use a shared-car. Furthermore, this intention is significantly associated with latent attitudes about pro-environmental preferences, the symbolic value of cars, and privacy-seeking.     

Improving the robustness of an MPC-based obstacle avoidance algorithm to parametric uncertainty using worst-case scenarios

Previous work by the authors focused on obstacle avoidance in large, high-speed autonomous ground vehicles within unknown and unstructured environments. This work resulted in a nonlinear model predictive control based algorithm that simultaneously optimises both the speed and steering commands. The algorithm can exploit the dynamic limits of the vehicle to navigate it to a target position as quickly as possible without compromising safety. In the algorithm, a model of the vehicle is used explicitly to predict and optimise future actions, but in practice the model parameter values are not known exactly. Thus, in this paper, the robustness of the algorithm to parametric uncertainty is evaluated. It is first demonstrated that using nominal parameter values in the algorithm leads to safety issues in 24% of the evaluated scenarios with the considered parametric uncertainty distributions. To improve the algorithm's robustness, a novel double-worst-case formulation is developed that simultaneously accounts for the robust satisfaction of the two safety requirements of high-speed obstacle avoidance: collision-free and no-wheel-lift-off. Results from simulations with stratified random scenarios and worst-case scenarios show that the double-worst-case formulation renders the algorithm robust to all uncertainty realisations tested. The trade-off between robustness and the task completion performance is also quantified.     

基于凸集模型的船舶坐墩配墩鲁棒设计方法

提出了船舶坐墩支墩配置的鲁棒设计方法.用凸集模型描述支墩组合刚度的不确定性,用极小化不确定性参数不满意度函数的方法实现目标函数的鲁棒性,用一个优化过程分析约束函数的最劣值实现约束函数的鲁棒性.计算示例表明,用所提方法得到的设计方案优于直接极大化不确定性参数最小值的结果.     

逆反射测量仪计量检定技术

通过使用逆反射测量仪对产品进行检测和为研究逆反射测量仪计量检定技术而对逆反射测量仪进行的比对和测试试验,介绍了逆反射测量仪的工作原理及逆反射测量仪和其自带校准板的计量检定方法和技术。分别阐述了标准表法和标准试样法的环境条件、仪器设备需求和试验步骤,同时也对使用两种方法实施校准过程中可能引入不确定度的因素进行了简要地分析;并从使用的标准、环境需求、设备需求和引入不确定度的因素四个角度对两种方法进行比较,得出其各自的适用条件和在逆反射测量仪计量检定工作中的作用。为逆反射测量仪计量检定工作的开展提供参考。     

交通事故车速估算的不确定因子方法

道路交通事故包含含有很多种不确定的因素,根据这些因素推断车辆碰撞前的速度具有较大的不确定性,无法得到一个准确值。提出一种用于车速估算的不确定因子方法,即采用不确定度理论,通过适当选择不确定度对车速计算结果影响最大的参数作了不确定因子,根据其不确定度推算车速的不确定度和取值范围。分别选取轮胎与路面间的摩擦系数,被撞车碰撞前速度等参数作为不确定因子,给出了该方法在动能和动量等车速算法中的应用。     

Optimal reefer slot conversion for container freight transportation

Given a fleet of container ships of varying capacity, a cost-efficient approach for improving fleet utilization and reducing the number of delayed containers is to optimize the sequence of container ships in a given string, a problem which belongs to the large ship-deployment class. A string sequence with ‘uniformly’ distributed ship capacity is more likely to accommodate a random container shipment demand. The number of one’s total ship slots acts as a gauge of the capacity of the container ships. Meanwhile, there are two types of ship slots: dry slots and reefer slots. A dry slot only accommodates a dry container, while a reefer slot can accommodate either a dry or a reefer container. The numbers of dry and reefer slots for ships in a string are different. Therefore, in this study, we propose a model that considers both dry and reefer slots and use it to elucidate the optimal ship-deployment sequence. The objective is to minimize the delay of dry and reefer containers when the demand is uncertain. Furthermore, based on the optimal sequence deduced, the study also investigates the need to convert some dry slots to reefer slots for the container ships.     

动力定位船舶的自适应滑模观测器设计

针对含有未知扰动等不确定性的船舶动力定位系统,在系统不确定性不满足匹配条件的情况下,提出了一种自适应滑模观测器的设计方法。首先通过构造辅助输出使匹配条件得以满足,设计高增益观测器估计含有未知变量的辅助输出;接着基于滑模变结构理论对带有辅助输出的新系统进行观测器设计,通常船舶动力定位系统的不确定性上界值未知,采用自适应律调整滑模面增益,保证系统鲁棒性的同时还可避免增益取值过大而造成的抖振问题;然后将观测器增益矩阵的设计转化为求解线性矩阵不等式约束的优化问题;最后通过仿真验证了所提方法的有效性。     

基于鲁棒二次稳定性理论的状态观测器增益矩阵求法

针对具有定子电阻不确定参数的电机模型，设计了全阶自适应状态观测器，运用具有定子电阻参数不确定系统的二次稳定性理论，推导出转子速度的自适应辨识率，同时将定子电阻和速度视作不确定参数，利用LMI工具箱中求解不确定参数系统二次稳定性的方法，得到状态观测器的增益矩阵来保证整个系统在全速度范围内的稳定运行。此方法计算量小，易满足实时性控制要求。仿真结果表明，基于鲁棒二次稳定性理论求出状态观测器增益矩阵能保证系统在全速度范围内的稳定运行，并且可抑制定子电阻不确定性对速度辨识的影响，具有很好的鲁棒性能。     

Model uncertainty and the management of a system of infrastructure facilities

The network-level infrastructure management problem involves selecting and scheduling maintenance, repair, and rehabilitation (MR&R) activities on networks of infrastructure facilities so as to maintain the level of service provided by the network in a cost-effective manner. This problem is frequently formulated as a Markov decision problem (MDP) solved via linear programming (LP). The conditions of facilities are represented by elements of discrete condition rating sets, and transition probabilities are employed to describe deterioration processes. Epistemic and parametric uncertainties not considered within the standard MDP/LP framework are associated with the transition probabilities used in infrastructure management optimization routines. This paper contrasts the expected costs incurred when model uncertainty is ignored with those incurred when this uncertainty is explicitly considered using robust optimization. A case study involving a network-level pavement management MDP/LP problem demonstrates how explicitly considering uncertainty may limit worst-case MR&R expenditures. The methods and results can also be used to identify the costs of uncertainty in transition probability matrices used in infrastructure management systems.     

基于D-S证据理论的舰船技术状态评估方法

在简要叙述船舶技术状态评估现状的基础上,提出了基于D-S证据理论的舰船技术状态评估方法。介绍了船舶技术状态评估的重要性和发展现状,以及可处理由不知道所引起的不确定性的D-S证据理论和基于D-S证据理论的决策方法;通过实例详细阐述如何用D-S证据理论进行舰船技术状态评估。实例证明,通过应用D-S证据理论,提高了舰船技术状态评估的准确性和可靠性,降低了评估的不确定性。