船台测量场中多级转站的不确定度

对在船台测量场内利用全站仪进行多级转站测量时误差的传递过程进行研究,分析误差的来源及其累积方法,建立转站过程中误差传递的数学模型,并据此推导出多级转站测量的系统误差传递公式及其不确定度估计公式。采用蒙特卡洛方法开展考虑全站仪测量误差的多级转站过程仿真试验,并将仿真结果与计算结果相对比。结果表明,提出的误差估计方法能较好地反映多级转站时误差的传递过程和不确定度的分布规律。     

Guidance for transport planning and policymaking in the face of an uncertain future

Uncertainty of outcome is widely recognised as a concern facing decision-makers and their advisors. In a number of spheres of policy, it appears uncertainty has intensified in the face of globalisation, economic instability, climate change, technological innovation and changing consumer preferences. How can planners and policymakers plan for an uncertain future? There is growing interest in, and use of, techniques that can help decision-making processes where deep uncertainty is involved. This paper is based upon one of the most recent international examples of a foresight exercise employed to examine uncertainty – specifically that which concerns uncertainty over the nature and extent of future demand for car travel. The principal focus of the paper is on the insights and guidance this examination of uncertainty brings forth for transport planning and policymaking. To accommodate deep uncertainty requires a flexible and open approach in terms of how policy and investment possibilities are formulated and judged. The paper argues for a focus upon the Triple Access System of spatial proximity, physical mobility and digital connectivity as a framework for policy and investment decisions that can harness flexibility and resilience. Uncertainty becomes an opportunity for decision-makers with the realisation that they are shaping the future rather than (only) responding to a predicted future. The paper outlines two forms of policymaking pathway: regime-compliant (in which adherence to trends and the nature of the world we have known pushes policy) and regime-testing (in which the nature of the world as we have known it is brought into question and vision pulls policy decisions). Stronger orientation towards regime-testing to assist in managing an uncertain future is advocated.     

Sensitivity-based uncertainty analysis of a combined travel demand model

Travel demand forecasting is subject to great uncertainties. A systematic uncertainty analysis can provide insights into the level of confidence on the model outputs, and also identify critical sources of uncertainty for enhancing the robustness of the travel demand model. In this paper, we develop a systematic framework for quantitative uncertainty analysis of a combined travel demand model (CTDM) using the analytical sensitivity-based method. The CTDM overcomes limitations of the sequential four-step procedure since it is based on a single unifying rationale. The analytical sensitivity-based method requires less computational effort than the sampling-based method. Meanwhile, the uncertainties stemming from inputs and parameters can be treated separately so that the individual and collective effects of uncertainty on the outputs can be clearly assessed and quantified. Numerical examples are finally used to demonstrate the proposed sensitivity-based uncertainty analysis method for the CTDM.     

Heteroscedastic Gaussian processes for uncertainty modeling in large-scale crowdsourced traffic data

Accurately modeling traffic speeds is a fundamental part of efficient intelligent transportation systems. Nowadays, with the widespread deployment of GPS-enabled devices, it has become possible to crowdsource the collection of speed information to road users (e.g. through mobile applications or dedicated in-vehicle devices). Despite its rather wide spatial coverage, crowdsourced speed data also brings very important challenges, such as the highly variable measurement noise in the data due to a variety of driving behaviors and sample sizes. When not properly accounted for, this noise can severely compromise any application that relies on accurate traffic data. In this article, we propose the use of heteroscedastic Gaussian processes (HGP) to model the time-varying uncertainty in large-scale crowdsourced traffic data. Furthermore, we develop a HGP conditioned on sample size and traffic regime (SSRC-HGP), which makes use of sample size information (probe vehicles per minute) as well as previous observed speeds, in order to more accurately model the uncertainty in observed speeds. Using 6 months of crowdsourced traffic data from Copenhagen, we empirically show that the proposed heteroscedastic models produce significantly better predictive distributions when compared to current state-of-the-art methods for both speed imputation and short-term forecasting tasks.     

基于离散分布的智能停车诱导系统中空余泊位数的预测研究

本文根据城市公共停车场泊位占有率在时间上表现出的周期性及其随时间的变化趋势,不考虑其由于受各种随机因素影响所表现出的不确定性,将车辆进出停车场的行为看成可描述事件。利用离散分布模型描述行为进行,建立数学模型,表达经过某段时间后停车场的空余停泊位数。最后通过算例计算验证模型的可行性,可用于停车诱导屏的泊位预测。     

分光光度计测量结果的不确定度评定

测量不确定度是对测量结果而言,表达这个结果分散性程度的参数,是计量学中的一个重要内容。本文对分光光度计校准过程的透射比示值误差测量结果的不确定度和波长示值误差测量结果的不确定度进行了评定,给出了评定方法、步骤及结果,并对测量不确定度进行了讨论。     

铁路罐车容积测量不确定度的评定

根据国家计量技术规范“JJF1059—1999测量不确定度评定与表示”,针对铁路罐车容积测量不确定度进行研究,分别建立罐内测量法数学模型和罐外测量法数学模型。通过分析计算和评定,得出当包含因子取2时,采用罐内测量法和罐外测量法检定的铁路罐车容积扩展不确定度分别为2.6×10-3和2.9×10-3,符合“JJG140—1998铁路罐车容积”中当包含因子取2时“铁路罐车容积扩展不确定度小于4×10-3”的要求,表明我国铁路罐车容积的检定采用罐内测量法、罐外测量法是可行的。提出开展容量比较法、注水称重法的研究和研发铁路罐车容积自动测量装置,以完善铁路罐车容积的量传体系,与国际接轨,提高铁路罐车容积的计量准确性。     

Relations Among Some Fuzzy Entropy Formulae

Fuzzy entropy has been widely used to analyze and design fuzzy systems，and many fuzzy entropy formulae have been proposed.For further in-deepth analysis of fuzzy entropy，the axioms and some important formulae of fuzzy entropy are intoduced.Some equivalence results among these fuzzy entropy formulae are proved，and it is shown that fuzzy entropy is aspedal distance measurement．     

移动闭塞信号系统的安全距离计算浅析

从安全距离的基本概念、计算要求、所需考虑的参数及计算过程等方面,对移动闭塞信号系统的安全距离进行了初步的分析和探讨,得出了移动闭塞信号系统安全距离的一般计算原则和方法。