铁路建设物流基地的探讨

阐述了铁路建设物流基地的必要性,提出了所建设的物流基地应具有装卸及仓储服务功能,成为运输新产品的发、到基地,具有增值服务及信息服务功能,最后列举了建设物流基地的具体要求。     

道路运输企业发展现代物流切入点选择方法研究

针对道路运输企业发展现代物流切入点选择的重要性和风险性,文章在分析道路运输企业发展物流切入点的基础上,建立了切入点选择的评价指标体系,提出一种以行业吸引力竞争能力矩阵为基础,并综合运用模糊评判法、层次分析法和德尔菲法的切入点选择方法,最后在某道路运输企业发展现代物流切入点选择中得到应用。     

分布式数据库车站货运管理信息系统

阐述了分布式数据库车站货运管理信息系统的基本功能、局域网ＬＡＮ（ＬｏｃａｌＡｒｅａＮｅｔｗｏｒｋ）网络拓扑结构的选择和特点，以及货运管理信息系统数据库的设计等．     

铁路运输中货车周转与货物运送时间的分析

对铁路货车周转时间及货物运送时间进行了较详细的探讨,对造成周转时间与货物运送时间延长的各时间因素及其影响程度进行了分析计算,并提出了相应的建议.     

关于货物列车制动力弱的原因分析和建议

分析和探讨了影响货物列车制动力的因素,从列车制动管系、车辆制动性能、司机操纵方法等方面系统分析了造成货物列车制动力弱的原因,并提出了有针对性的防范建议。     

Determinants of modal choice in freight transport

In the next few years, exciting developments in the field of freight transport are likely to occur. The Channel Tunnel will be perceived as giving railways much greater distance of operation, compared to the current train ferry to/from Great Britain. The further development of swap-body technology will allow easier modal transfer and the creation, in 1992, of a single market in Europe will transform the pattern of trade. All of these are likely to have significant impacts on modal choice, and hence modal split, in freight transport. Reappraisal by many firms of the modes of transport used is likely but will it result in a net transfer of freight from road to rail and, if so, to what extent? To answer such questions, an accurate and reliable method of predicting modal split is required. Research in the past has concentrated on the development of modal split models based on generalised costs. These fail to explain adequately the prevalence of road freight in the UK. From surveys of freight managers within industry, it is clear that models to date rely too heavily on the economic cost factor and too little on behavioural factors (Jeffs 1985). This paper derives from a recent study of freight transport modal choice from the standpoint of the transport decision-maker within the firm. It attempts to shed light on the actual parameters which should be incorporated into a modal split model. Many variables appear to exert an influence on modal choice decision-making process. However, it is possible to categorise them into six main groups, namely: customer-requirements; product-characteristics; company structure/organisation; government interventions; available transport facilities; and perceptions of the decision-maker him/herself. It is the interactions and inter-relationships between these which ultimately determine freight modal split. This study has shown that the relationship between the outcome of the transport decision process and the values of particular determinants of modal split is not straight-forward, due to the complexity and variety of interactions involved. Perhaps one of the main reasons for researchers' failure hitherto to develop a successful modal-split model has been the preoccupation with techniques that rely on the development of common metric (e.g. generalised cost), which has led to the exclusion of some important explanatory variables along quite different dimensions. Another important issue concerns the appropriate level of aggregation. In order not to reduce the explanatory power of the key variables, it is important to work at a disaggregate level, although this does make substantial demands on data. The use of factor analysis enables both the aggregation of information without loss of behavioural reality and the specification of variables in terms of a common metric. In conclusion, freight transport has usually been examined within too narrow a framework. It must be placed firmly within the context of the total industrial process. The demand for freight transport is directly influenced by the level, composition and geographical distribution of production and consumption activities. Freight flows are complex and so it is highly unlikely that a universal mode-choice model can ever be developed. Future research should, therefore, be directed towards developing partial models in response to specific needs of those involved in decision-taking in the freight sector.     

基于广义回归神经网络的货运量预测

根据货运量形成的原因 ,分析了货运量和相关影响因素之间的关系以及货运量预测的特点。在此基础上 ,建立货运量预测的广义回归神经网络 (GeneralRegressionNeuralNetwork ,GRNN)模型 ,并以我国 1981～ 2 0 0 1年的货运量和相关经济指标的历史统计数据作为学习样本 ,通过拟合训练和外推预测分析 ,验证了GRNN用于货运量预测的有效性     

基于Rough Set理论的铁路货运量预测

利用Rough Set理论通过对数据进行分析和推理发现隐含知识的优点，在结合该理论与铁路货运量预测要求的基础上，提出一个基于Rough Set理论的铁路货运量预测流程；合理选择统计指标并将相关原始数据代人预测流程涉及的各步骤后，得出预测我国铁路货运量发展水平的规则集；利用该规则集预测了“十五”期间我国铁路货运量的发展水平；该规则集有望在我国“十一五”规划的制定中发挥一定的参考作用。     

Freight transportation demand: A survey of recent econometric studies

This paper surveys econometric studies of freight transportation demand which have been published since the mid-1970s. It describes the variables, data sources, and estimation procedures utilized by the studies. In addition, it summarizes their statistical results. The studies included in this survey typically accounted for freight rates and service characteristics (e.g., transit time and reliability). Data sources often varied across the studies.Based on the data they utilized, the surveyed studies are classified as either aggregate or disaggregate. The data in the aggregate studies consist of information on total flows by modes at the regional or national level, while the data in the disaggregate studies pertain to individual shipments. The earlier aggregate studies estimated linear logit models. It has been pointed out that when they are estimated on aggregate data these models are subject to certain shortcomings. To avoid these shortcomings, more recent aggregate studies have estimated flexible forms such as translog functions. The disaggregate studies surveyed in this paper used either logit or probit models.Statistical results often varied with the commodities analyzed, making it somewhat difficult to generalize the findings of the different studies. One finding common to several studies is that freight rates have a significant impact on shipment decisions. This paper discusses certain theoretical and empirical limitations of the surveyed studies. It also offers suggestions for future research in freight transport demand. Freight demand models can be used to examine various effects of the recent deregulation in freight transportation.