社会责任视角下中国企业家精神建设的思考

全球性的企业社会责任会对中国企业以及企业家精神建设产生巨大影响。文中主要探讨了企业家精神与社会责任的关系,并在分析了当前我国企业家精神建设存在的主要问题的基础上提出有关企业家精神建设的建议。     

基于正交设计与 BP -GA 算法的船体结构耐撞性能优化设计

船体结构耐撞性优化设计的主要目的是在船舶碰撞研究的基础上对结构进行优化设计,提高船体结构的耐撞性能。基于正交试验设计、BP神经网络和遗传算法,形成了船体结构耐撞性能优化设计方法。提出了一种耐撞性综合指标,并以此指标作为优化的目标函数,以结构质量为约束条件,利用MSC/Dytran有限元软件对船舶碰撞进行数值仿真,完成对某船舷侧结构进行耐撞性优化设计,结果表明优化过后结构耐撞性能有较大提高,这为结构耐撞性能优化设计提供了一种新的思路和方法。     

The polarization of global container flows by interoceanic canals: geographic coverage and network vulnerability

It is widely acknowledged that the two major interoceanic canals of Suez and Panama play a central role in global shipping flows. However, this role has rarely been measured with precision both in terms of the geographic coverage and network topological properties of canal-dependent flows. Based on vessel movement data for containerships, this research clarifies the weight and share of canal-dependent flows globally and at the level of world regions, routes, and ports. It also estimates and maps the effects of removing canal-dependent flows from the network by means of graph-theoretical methods. While main results converge in showing a decreasing importance of canal shipping in the context of growing south-south trade exchanges, certain areas remain more dependent than others, such as Asia, Europe, and North America. The research also underlines factors of port vulnerability across the globe in relation with the two canals.     

无线传感器网络三维覆盖策略研究

从覆盖面积和有效覆盖率2个方面评估了正三角形网格和正四边形网格的性能优劣.根据理论分析和公式推导,将三维区域完全覆盖问题转化为多个平面覆盖问题研究.采用性能较好的正三角形网格进行节点部署,从拓扑学的角度给出了一种三维区域完全覆盖的部署策略,提供了所需的最少节点数的数学表达式,进行了仿真验证.     

基于货物时间价值的物流网络最优配送路径模型研究

本文构建了物流配送网络中货物时间价值相关的最小配送费用路径模型,并研究了其算法。模型把整个配送过程分为运输过程和装卸过程,并且考虑了由于运输过程和装卸过程时间延迟造成的货物价值损失,这也是和其它类似成本路径模型最大的区别。该模型算法可以利用计算机自动完成,不受物流配送网络大小和节点的限制,并保证算法的正确性。     

《快速公交（BRT）智能系统技术要求》实施效果研究

《快速公交(BRT)智能系统技术要求》以朝阳路和安立路BRT智能系统的建设为出台的重要基础,在运营调度、乘客信息服务、路口信号优先、图像监控、电子设备等十三个方面执行效果良好,大大提高了BRT智能系统建设和运行维护质量,提升了系统运行效率.     

Online Prediction of Travel Time: Experience From a Pilot Trial

Abstract

This study was designed to present an online model which predicted travel times on an interurban two-lane two-way highway section on the basis of field measurements. The study included two parts: an evaluation of the performance of the model, and an examination of the possibility to improve the model in case of unsatisfactory performance. The model was based on MLP neural networks. The main results of the evaluation showed that the prediction model outperformed a non-predictive system. However, the model for one section had not performed as well during the trial period as was expected. This might be due to a slight change in the congestion phenomenon. After further development, the findings showed that the model could be improved considerably with new data. The main implication was that even a simple prediction model improves the quality of travel time information substantially, compared to estimates based directly on the latest measurements.     

A microeconomic interpretation for the system optimal traffic assignment problem with nonadditive path cost

Using a Bergson–Samuelson welfare function, we outline a microeconomic interpretation of the effects of the non-linearity in the time/cost relationship for travellers in a congested transport network. It is demonstrated that a marginal cost traffic flow assignment following Wardrop's second principle, although it minimizes the total cost of a transport network, may reduce social welfare compared to the market equilibrium assignment based on Wardrop's first principle. A welfare-maximizing assignment model is presented and used to show that if the travellers' utility functions are linear, the assignment that maximizes social welfare will be the same as the assignment that minimizes total network cost, but if users' utility functions are non-linear (reflecting the traditional non-satiation and diminishing marginal utility axioms), the two assignments will be different. It is further shown that the effects of this non-linearity are such that a welfare-maximizing assignment will meet with less user resistance than a minimum total network cost assignment.     

Modelling trip distribution with fuzzy and genetic fuzzy systems

This paper explores the potential capabilities of fuzzy and genetic fuzzy system approaches in urban trip distribution modelling with some new features. First, a simple fuzzy rule-based system (FRBS) and a novel genetic fuzzy rule-based system [GFRBS: a fuzzy system improved by a knowledge base learning process with genetic algorithms (GAs)] are designed to model intra-city passenger flows for Istanbul. Subsequently, their accuracy, applicability and generalizability characteristics are evaluated against the well-known gravity- and neural network (NN)-based trip distribution models. The overall results show that: traditional doubly constrained gravity models are still simple and efficient; NNs may not show expected performance when they are forced to satisfy trip constraints; simply-designed FRBSs, learning from observations and expertise, are both efficient and interpretable even if the data are large and noisy; and use of GAs in fuzzy rule-based learning considerably increases modelling performance, although it brings additional computation cost.