新万福河复航工程总体设计方案

通过对起点和终点比选、梯级建设方案、沿线水利设施建设方案、水资源方案等多方面综合考虑,提出新万福河复航工程总体设计方案。该方案的实施对山东内河支线航道的发展具有重要的指导意义。     

集聚经济视角下轨道交通投资对城市系统均衡的影响

随着城镇化进程加快,城市规模增加所带来的集聚效应和交通拥堵等负外部性之间的矛盾越来越突出,优化城市交通基础设施建设是缓解该矛盾的重要举措。本文同时考虑了集聚经济和交通拥挤两种外部性,构建并求解了一个两区域城市空间结构模型,分三种情况探究了轨道交通的投资引入和道路容量的优化对城市规模和城市空间结构均衡的影响。结果表明,轨道交通的投资引入能够增加城市人口规模和社会福利,使城市结构更加紧凑。但是,随着集聚经济水平的提高,城市规划者应逐步完善公交配套设施,以降低公交拥挤成本,提高公交出行分担率;还应制定相应的福利政策,避免由于个体效用下降而导致人口流出。     

高速铁路基础设施检测监测体系技术路线研究

铁路基础设施是铁路运输的基础,加强检测监测工作,以全面掌握基础设施运用状态和变化规律,可为高铁预防性状态修和精准维修提供技术支撑.结合工电供融合、修程修制改革和铁路高质量发展等新要求,研究提出从国内外调研、需求分析、工电供专业系统优化、大数据平台设计、工电供专业融合、检测监测体系结构等6个环节开展高速铁路基础设施检测监...     

城市基础设施公私合作模式所有权归属的模型分析

基础设施公私合作(PPP)模式中项目的所有权归属问题一直是PPP模式实施的难点.所有权的归属影响了基础设施PPP模式的推进.通过模型分析认为,若基础设施资产所有权的激励效应大于风险成本,所有权归民营企业最优;若资产所有权的激励效应小于风险成本,所有权归国有最优.     

常用应急抢险装备的合理选择和配备

我国自然灾害频发,地震、洪水、泥石流等灾害极容易对道路,房屋等建筑造成巨大的损坏,从而对抢险过程直接或间接地增加了难度。为了高效遂行抢险任务和提高抢险能力,就有必要提高处理交通基础设施的能力。而抢险设备的选择和合理的配置能够大大提高抢险任务效率。一方面,对于不同的抢险任务,有着不同的作业内容,其中所需要的抢险器械也各不相同。另一方面,同一种工程机械又有着不同的型号,各自又具有独特的技术性能和适用范围。一种机械可以完成多种作业,而一种作业又可以使用多种工程机械。因此如何对工程机械的选择以及合理的配置是本文的重点。     

新能源汽车充电用户行为大数据分析及应用

随着新能源汽车行业快速发展,电动汽车充电功能及安全问题受到广泛关注。结合新能源汽车充电大数据可视化系统,从用户类型、车辆类型、充电方式、地域分布维度进行交叉分析,洞察用户充电行为统计分布规律;通过数据分析驱动充电安全设计,并结合数据资源和大数据分析技术,挖掘数据背后蕴藏的丰富价值,以改善当前新能源汽车充电问题和促进新能源汽车产业发展。     

Shared autonomous electric vehicle (SAEV) operations across the Austin,Texas network with charging infrastructure decisions

Shared autonomous vehicles, or SAVs, have attracted significant public and private interest because of their opportunity to simplify vehicle access, avoid parking costs, reduce fleet size, and, ultimately, save many travelers time and money. One way to extend these benefits is through an electric vehicle (EV) fleet. EVs are especially suited for this heavy usage due to their lower energy costs and reduced maintenance needs. As the price of EV batteries continues to fall, charging facilities become more convenient, and renewable energy sources grow in market share, EVs will become more economically and environmentally competitive with conventionally fueled vehicles. EVs are limited by their distance range and charge times, so these are important factors when considering operations of a large, electric SAV (SAEV) fleet.This study simulated performance characteristics of SAEV fleets serving travelers across the Austin, Texas 6-county region. The simulation works in sync with the agent-based simulator MATSim, with SAEV modeling as a new mode. Charging stations are placed, as needed, to serve all trips requested (under 75 km or 47 miles in length) over 30 days of initial model runs. Simulation of distinctive fleet sizes requiring different charge times and exhibiting different ranges, suggests that the number of station locations depends almost wholly on vehicle range. Reducing charge times does lower fleet response times (to trip requests), but increasing fleet size improves response times the most. Increasing range above 175 km (109 miles) does not appear to improve response times for this region and trips originating in the urban core are served the quickest. Unoccupied travel accounted for 19.6% of SAEV mileage on average, with driving to charging stations accounting for 31.5% of this empty-vehicle mileage. This study found that there appears to be a limit on how much response time can be improved through decreasing charge times or increasing vehicle range.     

How Do Governments Support the Development of Public Private Partnerships? Measuring and Comparing PPP Governmental Support in 20 European Countries

Abstract

Taking an institutional perspective, in this article we develop an index of the governmental support for public private partnership (PPP) — a ‘PPP Governmental Support Index’ (GSI) — which aims to measure the extent to which national governments provide an institutional framework that is either conducive or preventive for the introduction and diffusion of PPPs within transport infrastructure and other sectors. First, based on a substantive review of the literature, we define the elements of the PPP GSI, including the policy and political commitment regarding PPPs, the legal and regulatory framework, and the presence/absence of dedicated PPP-supporting arrangements. Second, we calculate the PPP GSI for 20 European countries, cluster them and compare similarities and differences in national governmental support of infrastructure PPPs. Third, we explore the potential link between national institutional index scores and infrastructure PPP activity in the 20 countries. Lastly, we discuss the potential and usefulness of the presented PPP GSI, as well as methodological limitations, and elaborate on how this index might be utilised to strengthen future comparative research on PPP in transport and other sectors.     

利用地下空间助力发展绿色建筑与绿色城市

当今世界,绿色发展已经成为一个重要趋势,绿色发展理念更是指导我国“十三五”规划的重要理念之一。建设城市地下空间是转变城市发展方式、治理“城市病”并建设绿色城市的主要着力点。首先,说明绿色建筑与绿色城市的概念,以及二者的关系。重点从节约土地、利用地热能、节水、绿色城市基础设施（包括绿色客运城市交通和城际交通,未来城市货运交通,绿色城市污水、雨洪蓄排系统,绿色城市垃圾集运和处理系统,智慧地下综合管廊,城市智慧行车系统）等方面,并结合国内外典型案例分析说明利用地下空间发展绿色建筑和绿色城市的理念、方法、构想。最后,结合实例强调地下空间开发规划的重要性、规划科学的重要性、开发与规划中的问题及规划的具体要求。     

A dynamic model for marginal cost pricing of port infrastructures

ABSTRACT

A dynamic model for marginal cost pricing of port infrastructures links costs to system performance by combining a power-law function with time-dependent queueing analysis. Additionally, the model incorporates the marginal cost of capacity, including the effects of economies of scale. This allows the calculation of the marginal cost price under a dynamic framework. The model accounts for nonlinear behaviour of port demand, which is sensitive to price and service levels. The effects over time of cost and service levels on the port’s operational performance are quantified. The proposed model allows determining the optimal timing for capacity investment. The model is a starting point for the application of marginal cost pricing to ports. However, for practical application of such pricing method it is necessary to apply a system’s approach, as productivity and costs must be assessed at the terminal’s component level. This should allow the derivation of a marginal cost function at the terminal’s component level.