成品油税费改革对西部道路客货运输的影响及对策

国家实行成品油税费改革,给西部道路客货运输业的发展带来了机遇和挑战。文章从道路运输管理部门和道路客货运输企业的角度阐述成品油税费改革对行业管理与企业发展的机遇和挑战,并结合实际提出了应对策略与建议。     

大体积混凝土凝固过程中的温度控制

进行大体积混凝土施工时必须根据混凝土水化热的具体情况,配备相应的监控系统—混凝土温度测试系统,对大体积混凝土凝固过程中的水化热进行实时温度检测,并对凝固过程进行全程检测和控制,采取相应的控制措施。     

基于机器视觉的车辆检测与参数识别研究进展

近年来公路交通运输快速增长,交通车辆的快速准确检测与识别对智能交通系统和交通基础设施运维具有重要意义.随着机器视觉和深度学习技术的迅速发展及其在目标检测领域的广泛应用,车辆目标检测和参数识别也取得新的突破.该文从车辆参数的识别方法和应用研究两方面梳理了机器视觉和深度学习在车辆检测与参数识别领域的研究现状、最新研究成果和...     

汽车动理学模型综述

随着汽车动态仿真的发展,产生了不同复杂程度的汽车动理学模型,主要分为集中参数模型和多体模型。在简要说明汽车动理学发腰过程的基础上,介绍了用集中参数模型法建立的动力传动系模型、路面模型以及研究汽车平顺性和操纵稳定性的整车模型,技术介绍了多体模型以及用有限元法和动态子结构法建立的仿直模型,最后提出了汽车动理学模型的发展趋势。     

基于改进粒子群算法的PMSM多参数辨识

针对标准粒子群优化算法对永磁同步电机多参数辨识精度低与收敛慢的问题,设计了一种自适应自治群组粒子群优化算法进行辨识,并在Matlab/Simulink中搭建参数辨识模型.仿真结果表明:改进后的算法对永磁同步电机多参数辨识的整体精度更高,收敛速度更快.     

油库安全阀的选用参数计算和确定方法

阐述了油库常用安全阀排量、口径、开启压力的计算和确定方法,重点分析了油库和机场特定部位安全阀开启压力和公称通径的计算过程和考虑要素,并说明了安全阀定购和使用中的注意事项.     

Port authorities view state coastal management programs

Abstract

An estuarine sanctuary is a store of public values due to the ecological, cultural, recreational, aesthetic, historic, and economic services provided by the preserve. These values may be expected to increase over time because of (1) growth in the population demanding estuarine services, (2) growth in the willingness to pay for such services, and (3) growth in the actual quality and quantity of services which are provided by a protected natural system. Thus an estuarine sanctuary will be more valuable to future generations than to current generations. When weighing the cost and benefits of a preservation or development decision, a preservation decision which seems currently undesirable may in fact be socially beneficial when growth in the value of estuarine services is included.     

浅谈公路工程估算编制的新规则

新的《公路工程基本建设项目投资估算编制办法》和《公路工程估算指标》自2012年1月1日正式实施。取代1996编办和指标,针对估算编制的新规则,分析总结新旧差异,为以后估算编制工作提供指导。     

二灰稳定钢渣路用性能试验研究

运用填充理论进行钢渣的级配和石灰、粉煤灰最佳比例的研究,通过对不同级配、不同二灰掺配比例的混合料路用性能比较,提出了一种最佳比例的二灰稳定钢渣混合料,其钢渣构成承重骨架,二灰密实地填充于其空隙中并裹覆其表面.用电镜等手段对其进行了微观分析；用抗压强度和回弹模量评价其力学性能.此项研究对利用钢渣铺路具有一定的借鉴作用.     

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.