Valuing of attributes influencing the attractiveness of suburban train service in Mumbai city: A stated preference approach

The paper presents valuing of qualitative and quantitative travel attributes influencing the attractiveness of suburban train service in Mumbai city, India. A stated preference experiment is designed to capture the data of sub-urban train mode choice behavior. The behavioral data are analyzed using different modeling techniques such as multinomial logit (MNL) and mixed logit (ML). In ML model, the random parameters are assumed to follow constrained triangular distribution, where mean equals its spread. The decomposition of preference heterogeneity around the mean estimate of random parameter is also investigated using ML model. The study shows the influence of headway time and train ride time associated with a particular crowding level (expressed in density of standing passengers/m²) in choosing the sub-urban train mode by calculating their willingness-to-pay (WTP) values and highlights the importance of WTP for addressing policy issues in the reduction of in-vehicle crowding level. The present study documents new findings of the effect of crowding level on train ride time in the context of a developing country and suggests some important directions for future suburban train transport crowding valuation research.     

Analysis of the effectiveness of control measures to mitigate road dust emissions in a regional network

The effectiveness of control measures to reduce road dust emissions is analyzed using a year’s data of road dust emissions collected with a mobile sampling platform and a survey of road maintenance practices in the Lake Tahoe Basin of Nevada and California US. Attributes such as sweeping practices, anti-icing, shoulder improvement, pavement condition, trackout, and abrasive material from road segments were analyzed with a feature subset selection algorithm. Street sweeping was found to be an effective means of controlling dust emissions from roads. Road dust from dirty tertiary roads served as a continuous source of suspendable material for adjacent high-speed roads in the winter time. To be most effective, emission control strategies require that not only primary roads, but all roads be swept after snow storms to recover applied abrasive material.     

船型参数对骑浪/横甩薄弱性衡准影响研究

骑浪/横甩是IMO船舶第二代完整稳性的五种失效模式之一,是一种基于概率的稳性衡准,制定规范的目的是更有效地保障船舶在实海域中的航行安全,确保不发生稳性失效情况.本研究中,以IMO有关成员国提出的最新版骑浪/横甩薄弱性衡准草案为基础,基于自主开发的骑浪/横甩薄弱性衡准校核软件,针对围网渔船开展了样船计算和比较分析,研究IMO正在制定的骑浪/横甩薄弱性衡准方法对该类船型的适用情况,分析船型参数对骑浪/横甩薄弱性衡准的影响,为我国参与国际法规制定,提出针对骑浪/横甩薄弱性衡准的提案提供技术支撑和依据.     

AFE变频驱动技术在科考船上的应用研究

为获得优越的操纵性能,科考船往往采用大功率变频装置来驱动电力推进系统。与此同时,科考船电站装机容量普遍较小,电力推进系统负荷占比高,变频装置的引入会引起电网电源品质的下降,严重时会影响其他电气设备的正常工作。对此,应结合船舶实际情况,将作为上述问题解决方案的有源前端(AFE)技术,纳入到统一的工程应用研究范畴,掌握工作原理,摸清技术优势,对于潜在的技术风险点,需提出针对性解决措施。     

勇担“交通强国、铁路先行”历史使命全面提升铁路对经济社会发展的服务保障水平

全面总结了党的十八大以来,在以习近平同志为核心的党中央坚强领导下,铁路安全稳定、改革发展取得的显著成绩,以及服务经济社会发展、增强人民群众获得感所作的重要贡献;系统阐述了铁路总公司坚持以习近平新时代中国特色社会主义思想为指导,深入贯彻落实党的十九大精神,勇担"交通强国、铁路先行"历史使命,全面提升铁路服务经济社会发展保障能力的务实举措。     

潮汐河口淹没丁坝群坝田淤积特征及成因分析

潮汐河口淹没丁坝群坝田水动力条件复杂多变,坝田复杂的水流结构决定了坝田内的泥沙淤积过程及淤积形态。以长江口北槽丁坝群形成坝田为例,分析坝田形成后淤积形态及淤积速率特征和规律。分析结果表明:随着时间推移,坝田内2 m等深线逐渐向距坝头0. 2倍坝长处靠近,5 m等深线逐渐向坝头处靠近;坝田的初始容积与冲淤平衡时的平衡容积呈对数关系;在丁坝间距为3～4倍坝长时,坝田内淤积分布最为均衡;坝田内淤积速率的拐点均出现在坝田形成后5～6 a。另外,从工程影响淤积丁坝布置参数方面探讨坝田内淤积特征的成因。     

混凝土联锁块机械化生产及蒸汽养护技术

针对混凝土联锁块现有预制工艺机械化程度低,施工质量受人为和天气影响较大而无法满足大规模生产需求的问题,进行混凝土联锁块机械化生产及蒸汽养护关键技术研究,通过对各生产环节进行机械化改进和采用蒸汽养护技术,并使各工序高效合理衔接,研制出一整套混凝土联锁块机械化生产工艺。新型生产工艺相对于传统工艺,不仅大幅提高了施工质量及生产效率,同时节省了人工成本,而且由于整个生产过程均可在厂房内完成,节省场地且不受恶劣天气影响,降低成本并缩短工期,能够适应施工中混凝土联锁块大规模生产的需求。     

BIM技术在大型集装箱码头工程设计中的应用

上海国际航运中心洋山深水港四期工程是国内首个全自动化集装箱码头,以此为依托,针对BIM技术在集装箱码头工程设计中应用的问题进行总图、水工、机械等相关专业BIM技术应用的研究,将BIM技术与传统二维设计模式相融合,采用欧特克BIM系列软件以及探索者BIM协同平台,得出全自动化集装箱码头BIM总体设计技术路线,重点对BIM协同设计平台的应用流程以及设计相关专业的BIM应用方法进行探讨,为全自动化集装箱码头从传统二维设计向基于BIM技术全生命周期设计应用的转变提供参考。     

Optimal Threshold Policies for Robust Data Center Control

With the simultaneous rise of energy costs and demand for cloud computing, efficient control of data centers becomes crucial. In the data center control problem, one needs to plan at every time step how many servers to switch on or off in order to meet stochastic job arrivals while trying to minimize electricity consumption. This problem becomes particularly challenging when servers can be of various types and jobs from different classes can only be served by certain types of server, as it is often the case in real data centers. We model this problem as a robust Markov decision process (i.e., the transition function is not assumed to be known precisely). We give sufficient conditions (which seem to be reasonable and satisfied in practice) guaranteeing that an optimal threshold policy exists. This property can then be exploited in the design of an efficient solving method, which we provide. Finally, we present some experimental results demonstrating the practicability of our approach and compare with a previous related approach based on model predictive control.     

基于Q-learning 的定制公交跨区域路径规划研究

考虑城市大客流通勤者跨区域出行需求，结合城市公交线网中乘客出行密集、客流走向规律等特点，提出一种跨区域定制公交的搭乘方案. 通过改进的Q-learning 模型对公交线路进行优化，为城市通勤者提供更加便捷和高效的出行服务. 通过综合路段拥堵状态、乘客需求及居民小区位置，设定了Q-learning 强化学习的奖惩函数，提升定制公交区域路径的直线系数、满载率、通行时间. 结果表明，所提出的改进方法能够降低通勤者跨区域通行的旅行时间，有效提高髙峰时段定制公交线网的通行效率.