PPP模式下的勘察设计工作探讨——以昆明市轨道交通5号线为例

PPP模式是目前城市轨道交通建设中选择较多的模式,此模式有利于缓解地方政府的财政压力、创新公共基础设施投融资模式、提高项目建设运行效率。昆明市轨道交通5号线工程是财政部公示的第二批PPP示范项目,也是国家发改委和交通运输部重点推进项目。本文以5号线为例,从勘察设计角度出发,分析了PPP模式下勘察设计的合同框架、管理界面、相关专题、变更等,提出了一些实际操作中较好的做法,归纳了一些较好的实践经验,以期为类似项目提供有益参考。     

城市轨道交通车辆基地站场勘测内容和方法研究

车辆基地是城市轨道交通系统的重要组成部分,针对目前车辆基地勘测设计过程中普遍重设计轻勘测、缺乏系统的站场勘测内容与方法的现状,通过对行业、企业相关勘测规范的理解和归纳,结合自己勘测设计实践,系统论述车辆基地站场勘测内容和方法,主要包括车辆基地基线的测设和应用、地形测量、站场横断面测绘、专项调查与测绘等几方面,掌握车辆基地站场勘测内容和方法将为车辆基地设计与施工、节约用地、降低投资等方面发挥重要的作用。     

船舶吃水对水深测量的影响浅析

在水深地形测量工作中,有很多不稳定因素极易造成水深数据误差.其中测量船舶吃水不准确也是造成误差的原因之一,因此测深船的大小以及船型的选择很重要.根据实际工作经验,对各种测量船舶吃水变化引起的水深测量误差的原因进行分析,并提出各种情况下减少水深测量误差的方法,以提高水深测量精度.     

综合物探技术在石油平台场址灾害调查中的应用

海洋物探技术是海洋调查的主要作业手段,在海洋石油平台的安全施工需要方面发挥重要作用.通过实例,介绍石油平台场址调查的主要作业范围、使用设备、以及主要结论,包括描述与分析预定平台位置及周围的、与平台就位、预定管线路由铺设施工有关的工程地质现象.     

公路货物运输抽样调查中样本量的确定

在分析我国现行公路货物运输抽样调查方案的基础上,结合分层随机抽样中样本量的确定方法,以广东省为例,对公路货物运输抽样中的样本量进行测算,可进一步验证公路货物运输抽样调查中样本量确定的科学性,同时能为交通运输行业或其他行业类似的抽样调查提供有益的实践经验.     

交通调查者行为效应分析

交通调查者的行为对获取真实、有效的交通运输系统原始数据起着关键性作用.通过对调查者行为的影响因素进行分析,依据行为学理论解释交通调查者的行为机理,并提出交通调查者的行为效应模型,具有十分重要的意义.     

Enhancing stated preference surveys in transportation research: the contribution of geographic information systems

In this paper we argue that visualization, data management and computational capabilities of geographic information systems (GIS) can assist transportation stated preference research in capturing the contextual complexity of many transportation decision environments by providing respondents with maps and other spatial and non-spatial information in graphical form that enhance respondents' understanding of decision scenarios. We explore the multiple inherent contributions of GIS to transportation stated preference data collection and propose a framework for a GIS-based stated preference survey instrument. We also present the design concepts of two survey prototypes and their GIS implementation for a sample travel mode choice problem.     

兰州市五人制足球运动开展现状调查分析

本文运用文献资料法、专家访谈法、问卷调查法、数理统计法对兰州市五人制足球运动的开展规模、教练员情况、场地设施、社会参与人群状况等问题进行调查分析,提出了针对性建议,旨在为进一步推动兰州市五人制足球的健康快速发展,同时促进兰州市足球运动的普及与提高尽一份力。     

Evolution of the household vehicle fleet: Anticipating fleet composition, PHEV adoption and GHG emissions in Austin, Texas

In today’s world of volatile fuel prices and climate concerns, there is little study on the relationship between vehicle ownership patterns and attitudes toward vehicle cost (including fuel prices and feebates) and vehicle technologies. This work provides new data on ownership decisions and owner preferences under various scenarios, coupled with calibrated models to microsimulate Austin’s personal-fleet evolution.Opinion survey results suggest that most Austinites (63%, population-corrected share) support a feebate policy to favor more fuel efficient vehicles. Top purchase criteria are price, type/class, and fuel economy. Most (56%) respondents also indicated that they would consider purchasing a Plug-in Hybrid Electric Vehicle (PHEV) if it were to cost $6000 more than its conventional, gasoline-powered counterpart. And many respond strongly to signals on the external (health and climate) costs of a vehicle’s emissions, more strongly than they respond to information on fuel cost savings.Twenty five-year simulations of Austin’s household vehicle fleet suggest that, under all scenarios modeled, Austin’s vehicle usage levels (measured in total vehicle miles traveled or VMT) are predicted to increase overall, along with average vehicle ownership levels (both per household and per capita). Under a feebate, HEVs, PHEVs and Smart Cars are estimated to represent 25% of the fleet’s VMT by simulation year 25; this scenario is predicted to raise total regional VMT slightly (just 2.32%, by simulation year 25), relative to the trend scenario, while reducing CO₂ emissions only slightly (by 5.62%, relative to trend). Doubling the trend-case gas price to $5/gallon is simulated to reduce the year-25 vehicle use levels by 24% and CO₂ emissions by 30% (relative to trend).Two- and three-vehicle households are simulated to be the highest adopters of HEVs and PHEVs across all scenarios. The combined share of vans, pickup trucks, sport utility vehicles (SUVs), and cross-over utility vehicles (CUVs) is lowest under the feebate scenario, at 35% (versus 47% in Austin’s current household fleet). Feebate-policy receipts are forecasted to exceed rebates in each simulation year.In the longer term, gas price dynamics, tax incentives, feebates and purchase prices along with new technologies, government-industry partnerships, and more accurate information on range and recharging times (which increase customer confidence in EV technologies) should have added effects on energy dependence and greenhouse gas emissions.