销量虽然繁荣,隐性问题犹在

<正>产量过剩,令人眼花缭乱的车型可能降低汽车利润在2009年,中国超越美国成为了世界上最大的轻型车市场。2010年的前4个月,中国轻型车销量创纪录地达到了580万辆,与去年同期360万辆的销量相比提高了60%。强劲的销售势头使得众多汽车制造商充满了极度乐观的情绪。汽车制造商们纷纷宣布计划加大投资以扩大在中国的产能。近期,大众宣布计划将在佛     

Abrolhos 2000 : Conserving the Southern Atlantic's Richest Coastal Biodiversity into the Next Century

The Abrolhos bank, an area of continental shelf off the coast of Bahia, Brazil, has the most biologically diverse coral reefs in the entire southern Atlantic Ocean. The coral reefs and nearby coastal ecosystems constitute a global conservation priority and are the target of the Abrolhos 2000 project, initiated by Conservation International as part of its global marine conservation strategy. Although portions of the Abrolhos reefs are located within a marine park, they are not afforded adequate protection due to insufficient conservation resources and a failure to be part of a broader integrated coastal management program. Through partnerships with government agencies, nongovernmental organizations, local communities, the private sector, and other stakeholders, Abrolhos 2000 is working to provide these needs while establishing local capacity for conserving coastal and marine ecosystems. The project's initial successes provide examples of useful strategies for making integrated coastal management work in the context of emerging economies.     

Optimal control of brakes and steering for autonomous collision avoidance using modified Hamiltonian algorithm

ABSTRACT

This paper considers the problem of collision avoidance for road vehicles, operating at the limits of friction. A two-level modelling and control methodology is proposed, with the upper level using a friction-limited particle model for motion planning, and the lower level using a nonlinear 3DOF model for optimal control allocation. Motion planning adopts a two-phase approach: the first phase is to avoid the obstacle, the second is to recover lane keeping with minimal additional lateral deviation. This methodology differs from the more standard approach of path-planning/path-following, as there is no explicit path reference used; the control reference is a target acceleration vector which simultaneously induces changes in direction and speed. The lower level control distributes vehicle targets to the brake and steer actuators via a new and efficient method, the Modified Hamiltonian Algorithm (MHA). MHA balances CG acceleration targets with yaw moment tracking to preserve lateral stability. A nonlinear 7DOF two-track vehicle model confirms the overall validity of this novel methodology for collision avoidance.     

A note on goodness-of-fit statistics for probit and logit models

In the case of models designed to explain the choice among a finite set of alternatives, a number of goodness-of-fit statistics have been reported. This paper is primarily concerned with the properties of one of these statistics, the likelihood ratio index.By comparing the likelihood ratio index with some of the other statistics and by examining its mathematical properties, it is concluded that the index has desirable properties in binary and multinomial situations. However, the way in which the likelihood ratio index has been applied in many recent studies has led to results which are possibly unexpected. In these cases, the index was a measure of the extent to which a hypothesized model improved upon the explanatory power of a model with all coefficients, including the constant or the coefficients of alternative-specific dummies, equal to zero. It is shown that the minimum value of this likelihood ratio index depends on the relative proportions of sampled individuals selecting the various alternatives, contrary to the expectation of a zero minimum value. The dependence on the sampled proportions also prevents comparison of indices resulting from different samples.A simple adjustment alleviates these difficulties. This new definition makes the likelihood ratio index a measure of the extent to which the hypothesized model improves upon the explanatory of a model with only a constant or alternative-specific dummies. It is recommended that this index is more appropriate for assessing the value of choice models.A list of symbols used is given on page 387.     

Big data in public transportation: a review of sources and methods

ABSTRACT

The collection of big data, as an alternative to traditional resource-intensive manual data collection approaches, has become significantly more feasible over the past decade. The availability of such data, coupled with more sophisticated predictive statistical techniques, has contributed to an increase in attention towards the application of these data, particularly for transportation analysis. Within the transportation literature, there is a growing emphasis on developing sources of commonly collected public transportation data into more powerful analytical tools. A commonly held belief is that application of big data to transportation problems will yield new insights previously unattainable through traditional transportation data sets. However, there exist many ambiguities related to what constitutes big data, the ethical implications of big data collection and application, and how to best utilize the emerging data sets. The existing literature exploring big data provides no clear and consistent definition. While the collection of big data has grown and its application in both research and practice continues to expand, there is a significant disparity between methods of analysis applied to such data. This paper summarizes the recent literature on sources of big data and commonly applied methods used in its application to public transportation problems. We assess predominant big data sources, most frequently studied topics, and methodologies employed. The literature suggests smart card and automated data are the two big data sources most frequently used by researchers to conduct public transit analyses. The studies reviewed indicate that big data has largely been used to understand transit users’ travel behavior and to assess public transit service quality. The techniques reported in the literature largely mirror those used with smaller data sets. The application of more advanced statistical methods, commonly associated with big data, has been limited to a small number of studies. In order to fully capture the value of big data, new approaches to analysis will be necessary.