Fuzzy modelling of static system optimum traffic assignment problem having multi origin-destination pair

Traffic congestion is an unpreventable problem to avoid in a transportation network and it has negative effects on traffic accident, time wasting, traffic delay and safety problem. Besides, in transportation networks, drivers do not want to deal with traffic jam while traversing between specified origin-destination pair. Therefore, traffic assignment (TA) is imperative to improve traffic management, transportation safety, time, and cost savings. System Optimum Traffic Assignment Problem (SOTAP) is a kind of TA model which aims to minimize the total system travel time on the network, and satisfies the flow conservation constraints. To model the SOTAP more realistically, the imprecise parameters can be taken as fuzzy. Therefore, in this paper, we focus on converting the conventional SOTAP to a fuzzy quadratic programming problem (QPP) which is named System Optimum Fuzzy Traffic Assignment Problem (SOFTAP). Here, link travel time is expressed with BPR function as generally used in the literature by converting to fuzzy except link-dependent parameters. Thus, the nonlinear objective function of SOFTAP is expressed in terms of fuzzy link flows and fuzzy link travel times. A solution approach from the literature is modified to the reconstructed SOFTAP.     

一种级联两箱通用数字预失真器及其辨识算法

提出了一种由单形规范线性分段(SCPWL)函数与记忆多项式级联的数字预失真器，并给出了复数域两步最小二乘参数辨识算法。不同于以往一种预失真器适用一种功放模型的情况，所提的预失真算法利用SCPWL函数的分段特性以及记忆多项式的非线性记忆特性，在完成参数辨识的同时自动地调整结构，可适用于传统以及强非线性新型功放模型的线性化补偿。将所提预失真器分别应用于传统记忆多项式、两箱模型以及新型包络跟踪功放。经过计算机仿真，功放输出的幅频特性和频谱曲线表明所提预失真器能够有效地补偿多种功放的非线性特性。算法仿真比较结果也表明，针对包络跟踪功放，所提复数两步最小二乘算法的邻道泄漏比(ACLR)可改善约35 dB，性能优于最小均方(LMS)类算法约30 dB。     

Improving forecasting performance using covariate-dependent copula models

Copulas provide an attractive approach to the construction of multivariate distributions with flexible marginal distributions and different forms of dependences. Of particular importance in many areas is the possibility of forecasting the tail-dependences explicitly. Most of the available approaches are only able to estimate tail-dependences and correlations via nuisance parameters, and cannot be used for either interpretation or forecasting. We propose a general Bayesian approach for modeling and forecasting tail-dependences and correlations as explicit functions of covariates, with the aim of improving the copula forecasting performance. The proposed covariate-dependent copula model also allows for Bayesian variable selection from among the covariates of the marginal models, as well as the copula density. The copulas that we study include the Joe-Clayton copula, the Clayton copula, the Gumbel copula and the Student’s $t$-copula. Posterior inference is carried out using an efficient MCMC simulation method. Our approach is applied to both simulated data and the S&P 100 and S&P 600 stock indices. The forecasting performance of the proposed approach is compared with those of other modeling strategies based on log predictive scores. A value-at-risk evaluation is also performed for the model comparisons.     

Predictive regressions under asymmetric loss: Factor augmentation and model selection

This paper discusses the specifics of forecasting using factor-augmented predictive regressions under general loss functions. In line with the literature, we employ principal component analysis to extract factors from the set of predictors. In addition, we also extract information on the volatility of the series to be predicted, since the volatility is forecast-relevant under non-quadratic loss functions. We ensure asymptotic unbiasedness of the forecasts under the relevant loss by estimating the predictive regression through the minimization of the in-sample average loss. Finally, we select the most promising predictors for the series to be forecast by employing an information criterion that is tailored to the relevant loss. Using a large monthly data set for the US economy, we assess the proposed adjustments in a pseudo out-of-sample forecasting exercise for various variables. As expected, the use of estimation under the relevant loss is found to be effective. Using an additional volatility proxy as the predictor and conducting model selection that is tailored to the relevant loss function enhances the forecast performance significantly.     

基于效应的随机灰规划模型和应用

为了解决同时含有随机因素和灰色因素的不确定规划问题,通过结合区间灰数所属区间两个端点的随机性,给出随机区间灰数和随机区间灰函数的定义,提出了随机灰规划模型。通过综合效应函数理论用随机变量期望值和方差综合量化表示灰数所属区间的两个端点值。应用该理论对综合量化后的两个端点值继续进行综合量化,从而将随机灰规划转化为确定型规划问题。应用遗传算法进行求解。通过综合效应函数的理念,综合随机变量的期望和方差,同时综合区间灰数的区间因素,将随机灰规划数学模型转化为确定型规划模型即基于效应的随机灰规划模型。通过选取不同的综合效应函数,得到了关于不同决策意识下的随机灰规划的最优解。这个方法可为决策者进行不确定决策提供参考。     

信息技术提高企业组织敏捷性的作用机理

从考察企业组织的敏捷性与交易成本的相互关系入手,对企业组织敏捷性的影响因素进行了剖析。详细分析了信息技术(IT)对提高企业组织敏捷性的作用机理。指出IT正是通过降低企业内、外部的交易成本、缩短流程时间、提高人的认识理性、扼制机会主义、促进企业组织结构、决策方式     

Is Volatility the Best Predictor of Market Crashes?

The objective of this paper is to determine the best predictor of equity market crashes by focusing particularly on volatility and market liquidity. In finance, volatility has traditionally been regarded as the best measure of market risk. However, this paper shows that the forecast value of market liquidity, in particular our modified calculated market depth, predicts equity market crashes much more accurately than does the forecast values of EGARCH or Implied Volatility.     

我国劳动力流动的动态和预期经济效应分析

劳动力流动是市场机制发挥配置资源作用的表现。从静态分析看,劳动力流动调剂了供需双方缺口,提高了整个社会福利水平。从动态角度看,我国的中心城市和经济先发展地区具有吸引资金、技术和知识资本的优越条件,决定着劳动力流动方向,而且因累积循环作用,引起回波效应大于扩散效     

Targeting Conservation Investments in Heterogeneous Landscapes: A Distance-Function Approach and Application to Watershed Management

To achieve an environmental objective at least cost, decision makers must integrate information about spatially variable biophysical and economic conditions. Although the biophysical attributes that contribute to an environmental outcome are often known, the way in which these attributes interact to produce the outcome is often unknown. We introduce a nonparametric conservation targeting approach that relies on distance functions to cost-efficiently allocate conservation funds. We apply the approach empirically to the case of conservation contracting for water-quality objectives. The contract portfolios derived from the application have many desirable properties, including intuitive appeal and robust performance across plausible parametric scoring measures.     

Generating Enhanced Fishery Rents by Internalizing Product Quality Characteristics

This paper demonstrates howadditional rents are generated in a fisherycharacterized by intraseasonal variation infish characteristics, including size,condition, and composition. Based on anexpanded conceptual model of the optimalharvest rule, fish characteristics affect preand post harvest production yields and outputprices. A dynamic empirical model, which uses asystem of quality characteristics and anhedonic equation, illustrates the complexrelationships and management choices associatedwith internalizing seafood qualitycharacteristics in a hake fishery. The modelretains the regulated open access managementsystem, but controls intertemporal andintersectoral quotas, production portfolios,and total allowable catch. Results demonstratethat including revenue-side effects frominternalizing fish quality can generatesignificantly greater rents and reduce therelative benefits of increased productionyields. If excluded, bioeconomic models canunderestimate the level of regulatory rentdissipation and overemphasize managementobjectives such as full utilization, whichcould misdirect processing decisions and resultin a suboptimal resource management plan.Implications for data collection,multidisciplinary analysis, and improvements inmarine resource management are discussed.