广东省货运代理业发展现状、存在问题及其对策研究

广东省经济与贸易的快速发展带动了本省及周边地区水路运输业的发展。广东省水运业的快速发展催生了大量水运服务（特别是货运代理）业务需求,货代业已发展成为具有一定规模的新兴服务产业。本文基于对广东省货运代理业发展现状和存在问题的分析提出促进广东省货运代理业的发展对策：提高货代从业人员素质,加大专业的人才储备;落实好货代行业新标准,加强行业自律;客户需要个性化的服务,企业推出差异化服务来赢得生存空间;加强信息化建设,增强企业核心竞争力。     

公路运输行业的转型发展及其理论依据

在我国,公路运输企业一般不拥有基础路网的所有权和经营权,通常由网络特性决定的自然垄断行业的特点并不显著。我国的公路运输业更多地体现规模经济和范围经济的特点。本文认为,要提高作为经济发展基础产业的公路运输业的运行效率,必须弄清楚公路运输业的重要经济特性,以使国家对行业的发展进行有效的规制,促进行业的发展并实现向现代物流业的成功转型。     

信号交叉口行人自行车交通强度状态划分研究

混合交通是造成交叉口拥堵和交通事故的重要原因之一.为了便于有效的研究交叉口混合交通信号控制策略及交叉口交通仿真,提出了信号交叉口人行横道绿灯时间行人自行车交通强度状态划分方法.分析了交叉口人行横道绿灯放行的交通特性,定义了人行横道绿灯放行时刻行人自行车聚集群的概念.从信号交叉口人行横道的"时空资源"利用角度,提出了划分人行横道绿灯时间行人自行车交通强度状态概念及必要性,选取了行人自行车绿灯时间通过流率、绿灯时间利用率以及行人自行车绿灯时间的空间占有率三个指标作为强度划分依据,选取K均值聚类为强度状态划分方法,对人行横道绿灯时刻行人自行车交通强度状态划分为三类,分别为低强度、中等强度和高强度.结合北京市平乐园交叉口为例,进行了行人自行车交通强度状态划分.     

中国已成为具有重要影响力的航海大国

中国已经成为航运大国、港口大国、造船大国.中国目前商船拥有量居世界第5位,全球十大港口中国占据了5个,造船量居世界第3位.中国正以前所未有的胸怀和气概向航海强国迈进.     

重型普通载货汽车典型用户的整车行驶循环工况研究

以重型普通载货汽车为研究对象,针对用户使用情况进行了调研和实车试验,并应用数理统计和多参数统计理论主成分分析与聚类分析方法,解析出重型普通载货汽车典型用户行驶循环工况。基于所提取的循环工况进行了综合油耗的仿真分析,结果表明,仿真计算油耗与用户实际使用油耗一致,证明了所提取重型普通载货汽车整车行驶循环工况的正确性。     

融入国际汽车工业体系后我国民族汽车工业的前景展望

随着跨国公司地介入,中国汽车工业在各个方面将会有很大的发展。但是,中国汽车工业从整体上看,仍然是一个国际竞争力较弱的产业。在这种背景下,中国汽车工业融入世界汽车体系后,中国汽车工业也许会有更大的发展.本文先从发展规模和工业格局上分析了中国汽车工业的现状,然后从产业水平、贸易竞争指数、劳动生产率、国内市场容量和其他一些指数分析了中国汽车工业的国际竞争力水平,从而可以从两个方面看到WTO对我国汽车工业的影响。同时,通过分析世界汽车工业的发展趋势,和跨国公司对中国市场的介入情况,了解到中国民族汽车工业正逐步融入国际汽车工业体系,中国汽车企业在国际汽车工业体系中也随之有相应的变化。通过这些分析,笔者总结了中国民族汽车工业在发展水平、生产格局、技术状况、零部件工业、消费环境等方面的问题和因此引发的中国汽车工业可能的变化。     

Multimodal travel groups and attitudes: A latent class cluster analysis of Dutch travelers

For developing sustainable travel policies, it may be helpful to identify multimodal travelers, that is, travelers who make use of more than one mode of transport within a given period of time. Of special interest is identifying car drivers who also use public transport and/or bicycle, as this group is more likely to respond to policies that stimulate the use of those modes. It is suggested in the literature that this group may have less biased perceptions and different attitudes towards those modes. This supposition is examined in this paper by conducting a latent class cluster analysis, which identifies (multi)modal travel groups based on the self-reported frequency of mode use. Simultaneously, a membership function is estimated to predict the probability of belonging to each of the five identified (multi)modal travel groups, as a function of attitudinal variables in addition to structural variables. The results indicate that the (near) solo car drivers indeed have more negative attitudes towards public transport and bicycle, while frequent car drivers who also use public transport have less negative public transport attitudes. Although the results suggest that in four of the five identified travel groups, attitudes are congruent with travel mode use, this is not the case for the group who uses public transport most often. This group has relatively favorable car attitudes, and given that many young, low-income travelers belong to this group, it may be expected that at least part of this group will start using car more often once they can afford it. Based on the results, challenges for sustainable policies are formulated for each of the identified (multi)modal travel groups.     

Estimating economic losses of industry clusters due to port disruptions

Seaport operations are highly important for industries which rely heavily on imports and exports. A reliable evaluation of port risks is essential to govern the normal running of seaborne transportation and thus the industrial economies. The occurrence of a breakdown in the trade facilitators, such as ports, will disrupt the smooth flow of supply chains for the industries. The estimation of the economic loss for an industry when a port gets disrupted is a challenging task as the relationship between the port and industry clusters is complex. This study aims to develop a systematic framework for performing economic loss estimation of industry clusters due to port disruptions. The whole risk assessment is split into three stages focusing on the establishment of a network flow model, economic estimations and evaluating risk mitigation strategies. The proposed idea is demonstrated by a case study on Shenzhen port and its related manufacturing industries. A dynamic inventory control strategy used by manufacturers is found to be beneficial for mitigating port disruption risks.     

Improving the estimation of total and direction-based heavy-duty vehicle annual average daily traffic

Abstract

The estimation of annual average daily traffic (AADT) is an important parameter collected and maintained by all US departments of transportation. There have been many past research studies that have focused on ways to improve the estimation of AADT. This paper builds upon previous research and compares eight methods, both traditional and cluster-based methodologies, for aggregating monthly adjustment factors for heavy-duty vehicles (US Department of Transportation Federal Highway Administration (FHWA) vehicle classes 4–13). In addition to the direct comparison between the methodologies, the results from the analysis of variance show at the 95% confidence level that the four cluster-based methods produce statistically lower variance and coefficient of variation over the more traditional approaches. In addition to these findings – which are consistent with previous total volume studies – further analysis is performed to compare total heavy-duty monthly adjustment factors, both directions of traffic, with direction-based monthly adjustment factors. The final results show that the variance as well as the coefficient of variation improve on average by 25% when directional aggregate monthly adjustment factors are used instead of total direction.     

Development and evaluation of a statistically reliable traffic counting program

This paper reviews the existing procedures to compute the seasonal factors, growth factors, and the number of automatic traffic recorder stations (ATRs) for developing a statistically reliable traffic counting program. The review is based on, (i) theoretical evaluation of the procedures, (ii) analysis of the results obtained from applying these procedures to extensive real life data, and (iii) experience of past researchers with these methods. The paper primarily deals with existing statistical procedures for determining seasonal factors, since it is believed that these factors play the most important role in estimating Annual Average Daily Traffic (AADTs) from short counts. The procedure of cluster analysis (which is most commonly used to determine seasonal factors) is critically reviewed. The shortcomings of this method motivated the development of a different statistical procedure, based on regression analysis, to determine seasonal factors. It is shown that this newly developed method gives better results than the cluster analysis techniques. Furthermore, separate sections are dedicated to a thorough analysis of determination of number of ATR stations and computation of growth factors.