基于AHP-DEMATEL的高铁枢纽换乘效率关键因素研究

为了找出影响高铁枢纽换乘效率关键因素,科学合理高铁枢纽布局,提出一种基于AHPDEMATEL组合评价方法。以高铁枢纽为评价对象,通过构建具有层次结构的评价指标体系,采用层次分析法(AHP)确定指标初始权重,决策与试验评价实验室法(DEMATEL)求得各评价指标影响度、被影响度、中心度,从而确定各指标综合影响度。研究结果表明:其他交通衔接性、通道便捷性、设施供给度、枢纽组织有序度、停车场供需比、平均换乘时间、平均换乘距离、单位时间换乘量、绕行系数是影响高铁枢纽换乘效率的关键因素。     

物流企业碳排放总量与效率测算方法

采用碳排放系数法测算物流企业运输环节移动源燃烧碳排放量，定义消耗电量转换法测算仓储环节固定源燃烧碳排放量；选取资本存量、劳动力、能源消耗作为碳排放效率评价的投入指标，产值作为期望产出，碳排放量作为非期望产出指标；采用Super-SBM模型(Super-efficiency Slack-based Measurement Model)得到技术效率、纯技术效率和规模效率这3个指标值，用以评价物流企业碳排放效率，以顺丰股份有限公司2013—2020年的营运数据为例进行实证分析。结果显示：2013—2016年、2020年的技术效率均大于1，表示资源分配达到最优状态；2017—2019年均小于1，主要是因为规模效率值偏低，2017年起顺丰提升运输业务量，逐渐调整规模效率，2020年的技术效率有了提升，找到了相对合理的平衡点。2013—2020年的纯技术效率值均大于1，近些年顺丰不断寻求技术提升以提高生产效率水平。结果表明，所提物流企业碳排放量测算方法可行，采用的Super-SBM模型能有效评价物流企业碳排放效率。最后提出物流企业需要从创新生产技术、节能减排技术等方面提升碳排放效率的改进措施。     

汽车空调对可吸入颗粒物的过滤机理和效果分析

本文旨在分析空调系统对可吸入颗粒物的阻隔机理、过滤机理和过滤效果。介绍了空调系统CLEAN AIR功能将车内空气质量控制在最优状态的工作机制。在不同风速下测定了单效空调滤清器和双效空调滤清器在过滤前后空气中可吸入颗粒物的计数浓度,得到了不同条件下各空调滤清器对可吸入颗粒物的过滤效率。研究发现,单效空调滤清器的过滤效率较低,不同条件下的过滤效率均不高于50%。双效空调滤清器对可吸入颗粒物的过滤效果更好,过滤效率达到80%以上,过滤效率随着颗粒物粒径的增大而增大,随着风速的增大而减小。双效过滤器的过滤阻力越大,其对可吸入颗粒物的过滤效率越高,而采用静电驻极技术可以在不牺牲风量的情况下显著提高可吸入颗粒物的过滤效率。另外,在不同的环境下,使用过相同里程和时间的空调过滤器的过滤效果会有很大差异。因此,依据过滤器的堵塞程度选择合理的更换周期,对维持良好的过滤效果具有重要的意义。     

特大型综合交通枢纽智慧停车场方案研究

研究特大型综合交通枢纽智慧停车场实现方案,解决传统停车场运行效率低、反向寻车困难、车位资源利用率低、缴费方式单一、安全性较差、出行体验差等诸多问题。综合利用AI、5G、室内定位导航、云计算、大数据、移动支付等新兴技术,通过枢纽综合管理平台对各子系统进行深度集成,并接收智慧城市、智慧交通等外部信息,形成一个高度协同的智慧停车体系。枢纽智慧停车场建成后,可有效提升枢纽运行效率、缓解停车和交通压力、提升服务水平和出行便捷度、保护人民群众生命财产安全、促进社会经济发展。     

基于动态分流元胞传输模型的城市道路网络韧性评估

为全面评估重大扰动事件下城市道路网络抵御扰动并从扰动中快速恢复的能力，提出以改进元胞传输模型模拟路网流量分布状态，以韧性为测度指标的路网性能评估模型。针对传统元胞传输模型交叉口分流比例恒定的不足，明确考虑扰动事件影响期内因出行者调整路径可能导致的路径流量波动，构建出行决策行为与元胞流量传输的强耦合机制，提出一种新的动态分流元胞传输模型；基于动态分流元胞传输模型获得的路网性能参数，以路网效率为路网基础性能指标，构建反映扰动事件影响期内路网效率累积动态变化的韧性指标；并基于Sioux Falls网络开展算例研究。算例结果表明：相比传统元胞传输模型，提出的动态分流元胞传输模型通过设置交叉口动态分流参数，建立出行决策行为与元胞流量传输的动态耦合关系和路径流量变化与交叉口元胞分流比例的自洽机制，可准确描述路网实际流量分布状态；提出的基于路网效率的韧性指标可全面反映扰动事件发生后路网性能退化到恢复全过程的动态累积性能，直观展示路网抵御扰动并从扰动中恢复的能力，契合韧性内涵；韧性评估中若忽视出行决策行为潜在影响，将获得次优甚至明显偏离实际的方案或结果。     

中国综合交通运输绿色效率的空间特征研究

将社会发展指数纳入综合交通运输效率评价指标体系，采用SBM(Slacks-Based Measure) 模型和Dagum基尼系数等分析方法，以中国30个省区为研究对象，测度综合交通运输绿色效率，并研究其空间特征。结果表明：中国综合交通运输绿色效率整体水平较低，区域差异较大，具有显著的空间非均衡性；综合交通运输绿色效率空间非均衡性具有明显的阶段性特征，研究前期区域差异逐渐扩大，后期呈缩小态势；中国综合交通运输绿色效率总体差异较大，区域内差异东部最小，中部次之，西部最大，东-中区域间差异与东-西区域间差异相差不大，但演化趋势此消彼长，中-西区域间差异始终居于最低位，区域内差异是造成总体区域差异的根源。最后，根据研究结论提出相关政策建议。     

基于e-航海的船舶智能航行

针对e-航海背景下的船舶绿色、低碳航行问题，本文提出一种基于非线性反馈的闭环增益成形算法。首先阐述了e-航海技术与船舶智能航行之间的共性，以大连海事大学无人船艇“蓝信”号为船舶模型，在保持已有的闭环增益成形算法不变的基础上，引入正弦函数驱动的非线性反馈代替原有的误差线性反馈。仿真结果表明，基于非线性反馈的闭环增益成型算法控制器更加节能，符合e-航海的要求；闭环增益成形算法控制器设计简单，工程中易于实现。     

Organizational adoption behavior of CO2-saving power train technologies: An empirical study on the German heavy-duty vehicles market

This study analyzes the preference structure of buyer groups that influences their willingness to select CO₂-saving power train technologies for medium-duty and heavy-duty vehicles (HDV). Based on the Technology–Organization–Environment framework for organizational adoption decision making and organizational buying criteria a theoretical construct was developed. Variables were validated in exploratory preliminary research and subsequently tested based on factor analysis using 27 survey items in a quantitative web-based study among 177 organizations operating HDV in Germany. Knowledge, experience, use and purchase consideration concerning alternative power train technologies and further measures to reduce fuel consumption were additionally queried. Based on a multiple linear regression analysis, key findings show that at the current stage of market maturity environmental attitude and corporate social responsibility exert the strongest significant influence on willingness to select CO₂-saving power train technologies. A hierarchical cluster analysis revealed six customer groups in order to yield behavioral market segmentation. Hereby it is shown that the performed transportation tasks do not determine the preference structures. Early adopting organizations are larger than average and driven by non-economic aspects as image or corporate social responsibility, whereas the mass market awaits lower purchasing prices. Crossing this chasm will be a major challenge for policymaker and manufacturers.     

Multi-criteria analysis of optimal signal plans using microscopic traffic models

Increasing concerns on environment and natural resources, coupled with increasing demand for transport, put lots of pressure for improved efficiency and performance on transport systems worldwide. New technology nowadays enables fast innovation in transport, but it is the policy for deployment and operation with a systems perspective that often determines success. Smart traffic management has played important roles for continuous development of traffic systems especially in urban areas. There is, however, still lack of effort in current traffic management and planning practice prioritizing policy goals in environment and energy. This paper presents an application of a model-based framework to quantify environmental impacts and fuel efficiency of road traffic, and to evaluate optimal signal plans with respect not only to traffic mobility performance but also other important measures for sustainability. Microscopic traffic simulator is integrated with micro-scale emission model for estimation of emissions and fuel consumption at high resolution. A stochastic optimization engine is implemented to facilitate optimal signal planning for different policy goals, including delay, stop-and-goes, fuel economy etc. In order to enhance the validity of the modeling framework, both traffic and emission models are fine-tuned using data collected in a Chinese city. In addition, two microscopic traffic models are applied, and lead to consistent results for signal optimization. Two control schemes, fixed time and vehicle actuated, are optimized while multiple performance indexes are analyzed and compared for corresponding objectives. Solutions, representing compromise between different policies, are also obtained in the case study by optimizing an integrated performance index.     

Non-parametric analyses of efficiency of airports

This paper estimates the efficiencies and productivity changes of 12 international airports in the Asia-Pacific region based on data from 1998 to 2006. We apply stochastic frontier analysis (SFA) to compute efficiency estimates and use the Malmquist productivity index (MPI) to analyze productivity changes. We use the SFA model with a translog-type production function after testing the statistical hypotheses. According to the results of the SFA hypothesis tests, airports have experienced a technological regression; the deviations from the efficiency frontier are caused more by technical inefficiency than random noise. However, the inefficiency was found to decrease. The MPI reveals a declining trend resulting more from technological change than from efficiency change, with a decrease in inefficiency. Taken together, both the hypothesis tests and the MPI not only provide consistent conclusions, but also suggest that airports should concentrate on technological progress.