城市公共交通发展的动力机制研究

城市公共交通发展动力涵盖政府的主导作用、公交运营企业的主动力、公众的支持力以及营销传播的助动力等"四方力量",四方力量的协同推动城市公共交通系统快速发展。首先对城市公共交通发展的动力机制进行分析,研究快速发展面临的困境以及进行公共交通系统动力协调的必要性;然后提出强化政府主导作用的"4P"发展策略、激发公交运营企业主动力的品牌服务策略、拓展公众支持力的乘客关系管理策略、发挥营销传播助动力的促销策略等公共交通协同发展策略;由此增强公共交通竞争力,提高公交出行方式分担率,推动城市可持续发展。     

一种基于乘性规则和支持向量域的水中目标识别分类器探讨

目标识别是水中设备智能化的关键技术之一.通过阐述支持向量域描述(SVDD)以及乘性规则(GA)的原理,提出基于乘性规则和支持向量域结合的方法对水中目标进行分类识别,对比SVM算法需要支持向量数多,运算速度慢的缺点,该方法能有效提高优化识别速度.     

槽渔滩隧道初期支护变形开裂施工处治

针对四川乐山至雅安高速公路槽渔滩隧道左洞段初期支护变形开裂情况，对变形开裂产生的原因进行分析，并结合现场实际情况及监控量测结果，采取临时加固初期支护、固结围岩并永久堵水等措施，使处治变形开裂段达到了预期效果。     

紧邻地铁深基坑悬臂式挡土墙保护及基坑支护设计研究

为确保昆明轨道交通4 号线朱家村站基坑开挖及支护过程中基坑及挡土墙的安全,通过对不同深基坑支护措施及挡土墙保护方案的比选,并结合数值模拟验算结果,分析在深基坑开挖支护各工况阶段的挡土墙安全指标,选出工期短、经济性好、安全性高的挡土墙保护方案。结果表明,在挡土墙高度较小时,采用型钢斜撑结合基坑支护结构对挡土墙进行保护,安全、快捷、经济、高效, 对于以后类似条件下的紧邻深基坑挡土墙保护具有较强的参考意义。     

弯桥抗倾覆分析

采用桥梁博士3.2进行受力计算,对呼包高速公路金山互通B匝道弯桥改建后的抗倾覆安全度进行了验算.     

基于UML的引俄潜艇技术保障信息系统分析

应用先进保障机制和信息化保障手段有机结合的方法,通过深入调查当前引俄潜艇技术保障存在的实际问题,对引俄潜艇技术保障中的主要要素进行简要分析,运用统一建模语言(UML)完成了信息系统的分析。通过系统应用提高了引俄潜艇的技术保障能力,提升了该类装备作战能力,并呈现军事经济效益。     

深埋大断面黄土隧道初期支护受力特征分析

为避免初期支护局部破坏对深埋黄土隧道安全产生危害，依托甘肃省境内早胜3号隧道施工开展深埋大断面黄土隧道初期支护受力规律的现场试验研究。通过埋设振弦式传感器对围岩与初期支护接触压力、喷射混凝土应变、钢拱架应变、相邻钢拱架间作用力进行监测，同时建立模拟隧道施工的有限元计算模型，分析得到初期支护内力分布特点。结果表明： 1) 初期支护受力随时间表现出迅速增长—缓慢增长—逐渐稳定的特点，曲线在二次衬砌闭合12 d后基本稳定； 2)应力空间分布上部大下部小，偏压特性明显，荷载理论计算值偏小； 3) 拱顶及边墙区域为初期支护薄弱部位，需采取措施并加强监控量测； 4)数值计算表明锚杆末端轴力较小，设计中可考虑适当缩短锚杆长度。     

The dynamic response of rail support

This research reviews principles behind the dynamic response of rail supports, and introduces a method of analysis to find the maximum response in a realistic setting. Assuming a time-dependent, moving mass with massive wheels is essential, because the ratio of the moving mass to the rail mass is significant. However, the dynamic response of the track is not affected by dynamic properties of the train other than its unsprung mass, because the natural frequencies of the train suspension and track are significantly different. A numerical method is developed to model the dynamic response based on these principles, and applied to the Korean urban transit. The dynamic response includes multiple peaks with a large amplitude range, creating noise while the wheel passes the support. The dynamic impact factor (DIF) for the rail support depends mainly on the stiffness and damping of the rail support. The DIF for the rail moment is below the code value, whether this value is based on numerical analysis or on-site measurements. However, our numerical analysis results in a DIF for support settlement that is greater than the code value, if the damping is less than 3%.     

Transportation agencies experiences with decision support systems for airport ground access planning

This article reports on a field investigation into the ways that transportation agencies use quantitative and qualitative information for making strategic decisions regarding airport ground access. The study analyzes the value of this information for planning airport ground access improvements at seven major international airport sites.The major finding of the research is that quantitative modeling for strategic decision support is very difficult, costly and time consuming. Modelers are confident that the models are accurate and reliable but executives generally lack confidence in the results. Transportation officials believe that the information supplied is flawed by a number of defects that minimize its value for strategic decision makers. The information defects described in this article provide an analysis of the structural difficulty of using quantitative modeling for transportation problems of strategic importance. To date, qualitative information is not frequently used, but some transportation agencies are considering its application to designing transportation services. Although this study is limited to airport ground access, the authors feel that this evidence, in conjunction with the evidence from other studies in the transportation area, dictates a need for wariness in the development of decision support systems for transportation planners. Developers of decision support systems for transportation planners must be aware of modeling costs and defects and consider how to improve the timeliness, relevance and credibility of information quantitative models provide transportation executives. Fundamentally it is important to recognize that decision makers tend, either singularly or in concert with other individuals or groups, to be the champions of a long-term vision for the community. When modeling produces inconsistent or wide ranging results that contradict their position, decision makers may not only discard modeling activities, but lose confidence in the models altogether. As a consequence, transportation planners are faced with the challenge of how to improve quantitative modeling. The most reliable and effective means for improvement is incorporation of qualitative techniques which provide greater understanding of customer perceptions and human behavior.     

Trip Chain's Activity Type Recognition Based on Support Vector Machine

This paper focuses on support vector machine (SVM) based trip chain's activity type recognition. First, the time-series location information of person trip is processed to obtain the trip chain elements including moving processes and activities, and the activity options are extracted from the geographic information system (GIS) around the activity sites. Second, the activity features are drawn from spatio-temporal factors of trip chain to serve as the input feature vector of classifier. A SVM based one-to-one classifier is established and the method of one-to-one classifier voting is adopted to decide the most likely activity type from the activity options. Finally, the classifiers are trained with simulation data based on the Gaussian radial basis (RBF) kernel function and the multilayer perception (MLP) kernel function respectively, and then examined by cross validation. The result shows that in the one-to-one classifying scheme, the highest and lowest right recognition rate with RBF are 99% and 62%, and the corresponding results with MLP are 97% and 54%, respectively.