基于多智能体的公交线网动态优化系统阐述

随着我国城市化建设不断加快,道路交通拥堵问题也日益严重。为解决城市交通问题,国家加大了城市公共交通发展力度。城市公交的快速发展虽在一定程度上缓解了交通压力,但也带来了多线路公交车运行区间重叠等问题。多线路公交车运行区间重叠易造成交通资源的浪费,也会使车辆碰撞等安全事故的发生几率上升。因此,在加大力度发展城市公共交通的同时,还需要寻求科学合理的公交线路调度方法,通过对相关技术手段与科技产品的运用提高公交车到站的准点率,有效降低重叠运行区间车辆串车率,为市民的出行提供方便。论文基于相关的理论、技术与经验提出一种基于多智能体的公交线网动态优化系统,对系统的原理、构成、功能等进行分析论述,以供参考。     

SCS模型在无资料地区径流模拟估算中的应用*——以清河流域为例

［目的］无资料地区的水文模拟，一直是摆在水文工作者中的一大难题。文章尝试利用由美国农业部水土保持局开发的SCS（Soil Conservation Service）模型对资料稀缺的西安市清河流域开展径流模拟估算。［方法］该模型结构简单，参数较少，极大地降低了对研究区基础地理数据与水文数据的依赖程度。由于缺乏实测历年径流总量数据，该文提出径流系数区间法来验证SCS模型模拟效果。以径流系数取值区间的平均值计算出的年际平均径流总量作为验证的参考值。［结果］（1）清河流域历年的年径流总量与年降雨总量有很好的对应关系，在不考虑地下水损耗的情况下，年降雨量越大，地表径流也越大，其中2003年径流总量最大，达到1 400万m3，最小为1997年的300万m3。（2）为弥补研究区资料稀缺问题，用径流系数区间法对模拟结果进行验证，验证结果为确定性系数09，纳什系数06，结果表明，基于SCS模型模拟估算清河流域年际径流总量取得了令人满意的效果。［结论］SCS模型在清河流域的成功应用表明，该模型可以在无资料地区或资料稀缺地区开展径流模拟，由于其建模简洁，极大地降低了在类似无资料区域开展水文模拟的难度，为此类地区开展水文研究提供了新的途径。研究结果可为当地水资源管理提供一定依据。     

COGNITIVE SKILLS AND CONFIDENCE: INTERRELATIONS WITH OVERESTIMATION,OVERPLACEMENT AND OVERPRECISION

This experimental study measures the three major types of judgmental overconfidence in a within‐subjects design. Performance‐based overestimation and overplacement are elicited in a Raven Progressive Matrices test for general intelligence. Calibration‐based overprecision is evaluated in a forecasting by confidence intervals task. This study takes a closer look at the interrelations of these different types. Moreover, as the main focus, it considers the effect of cognitive abilities on overconfidence. These are quantified in a cognitive reflection test. I find that cognitive skills are substantially interrelated with subjects’ confidence levels. Although overconfidence in absolute terms (overestimation) is not affected by cognitive abilities, the effect on overconfidence in relative terms (overplacement) is positive and significant. Overprecision, the calibration‐based type of overconfidence, is found to be significantly affected by cognitive capacity as well. Interval forecasts of cognitively more able subjects were much better calibrated than those of their peers who displayed substantial overconfidence in the precision of their forecasts.     

Approximate confidence and tolerance limits for the discrete Pareto distribution for characterizing extremes in count data

Statistical tolerance intervals for discrete distributions are widely employed for assessing the magnitude of discrete characteristics of interest in applications like quality control, environmental monitoring, and the validation of medical devices. For such data problems, characterizing extreme counts or outliers is also of considerable interest. These applications typically use traditional discrete distributions, like the Poisson, binomial, and negative binomial. The discrete Pareto distribution is an alternative yet flexible model for count data that are heavily right‐skewed. Our contribution is the development of statistical tolerance limits for the discrete Pareto distribution as a strategy for characterizing the extremeness of observed counts in the tail. We discuss the coverage probabilities of our procedure in the broader context of known coverage issues for statistical intervals for discrete distributions. We address this issue by applying a bootstrap calibration to the confidence level of the asymptotic confidence interval for the discrete Pareto distribution's parameter. We illustrate our procedure on a dataset involving cyst formation in mice kidneys.     

化纤检测标准中置信区间的应用

置信区间在化纤检测标准中的应用处于刚刚起步的阶段。笔者在本文中对如何在化纤检测标准中应用置信区间来提升纺织行业的检测水平进行了详细的阐述,并建议纺织生产企业、检测机构引入该方法,这将有利于相关部门提高检测的科学性、合理性,正确反映产品的性能,又可节约检测成本。     

基于二元区间数理论的企业知识吸收能力评价

构建了企业知识吸收能力的评价指标体系,提出了基于二元区间数理论的评价模型。首先建立多指标决策矩阵,并对其进行标准化处理;其次利用数学中的余弦公式来确定企业每个指标与理想指标之间的余弦值,建立余弦矩阵;然后借助改进的层次分析法确定评价指标的权重系数区间;最后,根据所提的评价指标体系和评价模型,对四个企业的吸收能力进行了评价,从而验证了该方法的科学性和实用性。     

税收政策的变化影响公司资本结构吗？--基于两税合并的经验数据

本文基于我国两税合并的背景,运用区间分析、配对样本T检验、回归分析和倍数差分法等方法,实证检验所得税政策的变化是否对资本结构产生影响,进而验证修正的MM理论是否适应中国资本市场。研究结果表明：所得税税负水平与公司资本结构显著正相关;由于新税法的实行,所得税税率下降的内资公司相应的降低了公司财务杠杆;两税合并前后税率下降的样本公司比税率不变的样本公司的资本结构变动显著要大。综合而言,税收政策能够显著影响公司资本结构决策。     

多属性决策模型研究及应用

针对现实多属性决策问题的复杂性,提出了基于组合赋权和区间灰数的TOPSIS方法多属性决策模型。通过组合赋权确定权重并引进区间灰数描述决策者评价信息的不确定性,结合TOPSIS方法建立决策模型。最后结合实例证明该模型的有效性。     

Generalized confidence intervals for process capability indices in the one-way random model

The method of generalized confidence intervals is proposed as an alternative method for constructing confidence intervals for process capability indices under the one-way random model for balanced as well as unbalanced data. The generalized lower confidence limits and the coverage probabilities for three commonly used capability indices were studied via simulation, separately for balanced and unbalanced cases. Simulation results showed that the generalized confidence interval procedure is quite satisfactory both in the balanced and unbalanced cases. Examples are provided to illustrate the results.     

Developing Confidence Intervals for Office Market Forecasts

This study focuses on the precision of models that forecast office construction and absorption. The article is novel because for the first time it applies Feldsteins (1971) technique for developing forecast standard errors in the presence of stochastic exogenous variables. The purpose of the article is not to find behavioral relationships but rather to evaluate forecasts. We find that in the case of many office markets, standard errors of long-term forecasts for absorption and completions are quite large, and therefore the forecasts themselves should not be used as a reliable basis for underwriting.