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提出了一种新的超对策结局偏好模糊认知信息的融合方法.用一种模糊集结算子来表示专家群体对其他局中人结局偏好的模糊认知,通过求解线性规划模型得到结局偏好群体模糊认知的水平截集;以与模糊数的截集相关的解模糊函数和Newton-Cotes积分公式,得到专家群体对其他局中人结局偏好清晰认知的关系矩阵;运用大多数一致性优胜者模糊集来确定其他局中人的结局偏好向量.给出的数值例子说明了文中提出方法的有效性. 相似文献
804.
女性企业家领导力模式实证研究 总被引:1,自引:0,他引:1
企业家领导力模式问题学术界已经研究得比较透彻了,从领导者的品质、个体行为、环境等各个方面都有诸多论述,本文主要借鉴国外关于领导力模式的最新研究成果,结合中国背景的实际,聚焦于女性企业家的领导力模式研究,通过256个样本进行了中国背景下的探索性和验证性分析,最终得出中国女性企业家具有人本型领导模式、变革型领导模式、转换型领导模式、和谐型领导模式和交易型领导模式五种领导力模式的结论。 相似文献
805.
城市公共交通中的公交输送能力问题探讨 总被引:1,自引:0,他引:1
本文通过对城市交通现状的分析,指出发展城市公共交通的必要性,针对公交运行中的问题,引出了公交输送能力这一考查指标,并建立了公交输送能力模型。最后以中关村大街人民大学站为例进行了定量分析,提出如何用公交输送能力来明确公共交通中出现的问题,对研究公交线路和车辆的优化配置提供了理论依据。 相似文献
806.
依据交通承载力确定土地适宜开发强度——以北京中心城控制性详细规划为例 总被引:5,自引:0,他引:5
为了实现土地利用与交通的协调发展,基于土地利用与交通双向互动理论,对评价土地开发强度与交通承载力关系的定量分析方法进行了研究。建立了静态对比测算和动态模型测试相结合的交通承载力分析方法,以协调土地利用与交通的关系。以《北京中心城控制性详细规划》为例,在静态对比测算中,将北京市中心城区各街区居住用地、产业用地发开强度与交通承载力进行了对比分析。在动态模型测试中,对3种方案的道路流量、交通负荷进行了对比,在空间分布上定位不协调区域及不协调程度。 相似文献
807.
Hongchao Liu Kevin N. Balke Wei-Hua Lin 《Transportation Research Part C: Emerging Technologies》2008,16(6):742-754
A novel approach is presented in which signalized intersections are treated as normal highway bottlenecks for improved computational efficiency. It is unique in two ways. First, it treats the signalized intersections as common freeway bottlenecks by a reversed cause and effect modeling approach. Both traffic arrivals and departures are modeled by smooth continuous functions of time as if there were no interruptions to traffic flows from signals. The use of smooth continuous functions for departure curves instead of commonly used step functions makes it easy to apply differential calculus in optimization and future extension to a system of intersections. Second, a dynamic linear programming (LP) model is then developed to maximize the total vehicular output from the intersection during the entire period of congestion subject to prevailing capacity and other operational constraints. The continuous optimal departure flow rate (the effect) is then converted to signal timing parameters (the cause) that can be readily implemented. Two numerical examples are presented to demonstrate the properties of the proposed algorithm and examine its performance. 相似文献
808.
Ipek N. Sener Rachel B. Copperman Ram M. Pendyala Chandra R. Bhat 《Transportation》2008,35(5):673-696
This paper presents a detailed analysis of discretionary leisure activity engagement by children. Children’s leisure activity
engagement is of much interest to transportation professionals from an activity-based travel demand modeling perspective,
to child development professionals from a sociological perspective, and to health professionals from an active lifestyle perspective
that can help prevent obesity and other medical ailments from an early age. Using data from the 2002 Child Development Supplement
of the Panel Study of Income Dynamics, this paper presents a detailed analysis of children’s discretionary activity engagement
by day of week (weekend versus weekday), location (in-home versus out-of-home), type of activity (physically active versus
passive), and nature of activity (structured versus unstructured). A mixed multiple discrete-continuous extreme value model
formulation is adopted to account for the fact that children may participate in multiple activities and allocate positive
time duration to each of the activities chosen. It is found that children participate at the highest rate and for the longest
duration in passive unstructured leisure activities inside the home. Children in households with parents who are employed,
higher income, or higher education were found to participate in structured outdoor activities at higher rates. The child activity
modeling framework and methodology presented in this paper lends itself for incorporation into larger activity-based travel
model systems where it is imperative that children’s activity-travel patterns be explicitly modeled—both from a child health
and well-being policy perspective and from a travel forecasting perspective.
Ipek N. Sener is currently a Ph.D. candidate in transportation engineering at The University of Texas at Austin. She received her M.S. degrees in Civil Engineering and in Architecture, and her B.S. degree in Civil Engineering from the Middle East Technical University in Ankara, Turkey. Rachel B. Copperman is currently a Ph.D. student at The University of Texas at Austin in transportation engineering. She received her M.S.E. from The University of Texas at Austin in Civil Engineering and her B.S. from the University of Virginia in Systems Engineering. Rachel grew up in Arlington, Virginia. Ram M. Pendyala is a Professor in Transportation at Arizona State University in Tempe. He teaches and conducts research in activity-based travel behavior modeling, multimodal transportation planning, and travel demand forecasting. He is the chair of the Transportation Research Board Committee on Traveler Behavior and Values and vice chair of the International Association for Travel Behaviour Research. Chandra R. Bhat is a Professor in Transportation at The University of Texas at Austin. He has contributed toward the development of advanced econometric techniques for travel behavior analysis, in recognition of which he received the 2004 Walter L. Huber Award and the 2005 James Laurie Prize from the American Society of Civil Engineers (ASCE), and the 2008 Wilbur S. Smith Distinguished Transportation Educator Award from the Institute of Transportation Engineers (ITE). He is the immediate past chair of the Transportation Research Board Committee on Transportation Demand Forecasting and the International Association for Travel Behaviour Research. 相似文献
Chandra R. Bhat (Corresponding author)Email: |
Ipek N. Sener is currently a Ph.D. candidate in transportation engineering at The University of Texas at Austin. She received her M.S. degrees in Civil Engineering and in Architecture, and her B.S. degree in Civil Engineering from the Middle East Technical University in Ankara, Turkey. Rachel B. Copperman is currently a Ph.D. student at The University of Texas at Austin in transportation engineering. She received her M.S.E. from The University of Texas at Austin in Civil Engineering and her B.S. from the University of Virginia in Systems Engineering. Rachel grew up in Arlington, Virginia. Ram M. Pendyala is a Professor in Transportation at Arizona State University in Tempe. He teaches and conducts research in activity-based travel behavior modeling, multimodal transportation planning, and travel demand forecasting. He is the chair of the Transportation Research Board Committee on Traveler Behavior and Values and vice chair of the International Association for Travel Behaviour Research. Chandra R. Bhat is a Professor in Transportation at The University of Texas at Austin. He has contributed toward the development of advanced econometric techniques for travel behavior analysis, in recognition of which he received the 2004 Walter L. Huber Award and the 2005 James Laurie Prize from the American Society of Civil Engineers (ASCE), and the 2008 Wilbur S. Smith Distinguished Transportation Educator Award from the Institute of Transportation Engineers (ITE). He is the immediate past chair of the Transportation Research Board Committee on Transportation Demand Forecasting and the International Association for Travel Behaviour Research. 相似文献
809.
810.
A two-level optimization method for the design of complex truss and parallel distributed implementation on a LAN is presented using parallel virtual machine (PVM) for Win 32 as message passing between PCs. The volumes of truss are minimized by decomposing the original optimization problem into a number of bar optimization problems executed concurrently and a coordinate optimization problem, subject to constraints on nodal displacements, and stresses, buckling and crippling of bars, etc. The system sensitivity analysis that derives the partial derivatives of displacements and stresses with respect to areas are also performed in parallel so as to shorten the analysis time. The convergence and the speedup performances as well as parallel computing efficiency of the method are investigated by the optimization examples of a 52-bar planar truss and a 3 126-bar three-diraensional truss. The results show that the ideal speedup is obtained in the cases of 2 PCs for the 3 126-bar space truss optimization, while no speedup is observed for the 52-bar truss. It is concluded that (1) the parallel distributed algorithm proposed is efficient on the PC-based LAN for the coarsegrained large optimization problem; (2) to get a high speedup, the problem granularity should match with the network granularity;and (3) the larger the problem size is, the higher the parallel efficiency is. 相似文献