信号控制交叉口绿灯间隔时间的设计方法

采用停车视距原理,阐述了机动车绿灯间隔时间在信号控制交叉口的作用,提出了机动车绿灯间隔时间的计算方法,并通过1个简单算例进行说明。最后,给出了绿灯间隔时间在黄灯和全红之间的分配方式。     

机动车相位固定最小绿灯时间计算方法

面向典型的十字形信号控制交叉口,建立了具有8个机动车相位和4个行人相位的双环相位结构,采用一种半环分析方法,讨论了可能存在的相位组合方案和相位切换方案,定义了起始相位和末尾相位两种机动车相位属性,分析了相位切换过程的基本规律。建立了具有不同属性的机动车相位的固定最小绿灯时间与相位结构、行人相位绿灯时间、绿灯间隔时间的数值关系。以相位结构、最小行人过街时间和绿灯间隔时间为主要依据,离线计算得到机动车相位固定最小绿灯时间。控制时段内,自适应控制策略可以根据当前信号周期内的机动车相位属性,为其动态分配固定最小绿灯时间。算例分析表明,该方法具有较好的可操作性。     

Uncertainty in traffic forecasts: literature review and new results for The Netherlands

This paper provides a review of transport model applications that not only provide a central traffic forecast (or forecasts for a few scenarios), but also quantify the uncertainty in the traffic forecasts in the form of a confidence interval or related measures. Both uncertainty that results from using uncertain inputs (e.g. on income) and uncertainty in the model itself are treated. The paper goes on to describe the methods used and the results obtained for a case study in quantifying uncertainty in traffic forecasts in The Netherlands.

多元数据下的公交站点客流不确定性分析

利用长期收集的公交站点IC卡刷卡数据,基于区间不确定性理论,提出公交上下车站点区间不确定性客流推导方法.首先,将公交IC卡数据和GPS数据与公交站点信息相融合,确定IC卡刷卡的上车站点及其区间不确定性客流;然后,对公交刷卡行为进行分析,考虑乘客个体出行特征、乘客出行距离和站点吸引权重,提出下车站点客流推导概率模型及相应算法,通过区间数处理方法,得到下车站点客流区间值.最后,以2015年11月13日~12月25日深圳市21路公交为期6周的IC卡刷卡数据和GPS数据为例进行实例分析.     

AC-25粒度分布对级配离析的影响

在AC-25级配范围内, 通过调整拌合楼各热料仓比例生产出7种级配的沥青混合料, 采用贝雷法将AC-25级配组成划分为0~0.3、0.3~1.18、1.18~4.75、4.75~13.2、13.2~31.5mm5个区间, 运用灰色关联法计算了不同级配的各区间集料含量与级配离析的灰色关联度。分析结果表明: 灰色关联度可以较好地反映AC-25级配区间集料含量对级配离析的影响程度, 5个区间4.75~13.2、0.3~1.18、0~0.3、1.18~4.75、13.2~31.5mm的关联度依次减小; 4.75~13.2mm区间集料含量与级配离析的灰色关联度最大, 该区间集料含量的变化对最终路面级配变异会产生较大影响, 设计时可通过调整该种集料含量以获得较好的抗离析级配; 1.18~4.75mm和13.2~31.5mm粒径区间的集料含量与级配离析的灰色关联度偏小, 说明粗集料和细集料中的较粗部分对级配离析的影响较小; 各级配区间集料含量与级配变异程度的二次回归相关系数大于0.6, 说明集料间交互作用对最终离析程度影响较大, 仅考虑单个筛孔通过率对沥青混合料性能的影响, 有可能会消弱集料级配的离析特征, 应从不同粒级组合角度分析沥青混合料级配的抗离析性能。     

基于安全车距实时标定的列车间隔动态控制

分析了列车间隔与其跟驰行为的关系, 利用Petri网形式化建模工具描述了当高速列车跟驰稳态被破坏时, 列车间隔的动态控制。面向CTCS-4级列车运行控制系统, 运用数值分析方法建立了全速域范围内最小安全车距随后车当前速度变化的拟合函数, 并运用该拟合函数进行列车跟驰行为质量评估, 进而构建了基于跟驰行为评估的列车间隔动态控制模型, 并对该模型进行了仿真验证。仿真结果表明: 列车跟驰系统从速度为200km·h^-1、列车间隔为5 849.18m的安全、高效跟驰稳态运行到速度为380km·h^-1的跟驰稳态期间, 列车间隔的动态控制能够通过后车的行为调整得到实现, 且当速度为380km·h^-1的跟驰稳态实现时, 列车间隔仅比安全车距大358.00m, 说明新的安全、高效跟驰稳态已经建立; 当前车紧急停车时, 后车在控制律的作用下采取因应措施, 安全、高效、平稳地减速运行, 直至完全停车。仿真结果验证了控制方法的有效性和可行性, 能够实现列车安全、高效跟驰运行。     

城市公共交通发展水平的属性数学识别模型

为了合理评价城市公共交通发展水平, 构建了城市公共交通发展水平评价指标体系与分级标准, 提出了属性数学识别模型。应用变异系数法确定评价指标的权重, 根据单指标的属性测度值与多指标的综合属性测度值, 利用置信度准则识别城市公共交通发展水平。评价结果表明: 青岛市公共交通发展水平属于较高等级, 与民意调查结果相吻合, 而且还可知青岛市的公交设施水平属于高级, 服务水平与效益水平属于中等, 因此, 该模型可行。     

Improving airline fuel efficiency via fuel burn prediction and uncertainty estimation

Reducing fuel consumption is a unifying goal across the aviation industry. One fuel-saving opportunity for airlines is the possibility of reducing discretionary fuel loading by dispatchers. In this study, we propose a novel discretionary fuel estimation approach that can assist dispatchers with better discretionary fuel loading decisions. Based on the analysis on our study airline, our approach is found to substantially reduce unnecessary discretionary fuel loading while maintaining the same safety level compared to the current fuel loading practice. The idea is that by providing dispatchers with more accurate information and better recommendations derived from flight records, unnecessary fuel loading and corresponding cost-to-carry could both be reduced. We apply ensemble learning techniques to improve fuel burn prediction and construct prediction intervals (PIs) to capture the uncertainty of model predictions. The upper bound of a PI can then be used for discretionary fuel loading. The potential benefit of this approach is estimated to be $61.5 million in fuel savings and 428 million kg of CO₂ reduction per year for our study airline. This study also builds a link between discretionary fuel estimation and aviation system predictability in which the proposed models can also be used to predict benefits from reduced fuel loading enabled by improved Air Traffic Management (ATM) targeting on improved system predictability.     

Consumer purchase intentions for electric vehicles: Is green more important than price and range?

In view of global warming and climate change, a transition from combustion to electric vehicles (EVs) can help to reduce greenhouse gas emissions and improve air quality. However, high acquisition costs and short driving ranges are considered to be main factors which impede the diffusion of EVs. Since electricity needs to be produced from renewable energy sources for EVs to be a true green alternative, the environmental performance of EVs is also presumed to be an important factor. This paper investigates the role of environmental performance compared to price value and range confidence regarding consumer purchase intentions for EVs. To develop our hypothesis, we interview 40 end-user subjects about their beliefs toward EVs. Then, we perform 167 test drives with a plug-in battery EV and conduct a survey with the participants to test the hypothesis. Results of a structural equation modeling support the hypothesis that the environmental performance of EVs is a stronger predictor of attitude and thus purchase intention than price value and range confidence.     

基于GM(1, 1)模型与相关向量机的轨道不平顺区间预测

为开展以预防修为主的养护维修作业,联合GM(1, 1)灰色模型与相关向量机(RVM)算法,提出一种预测轨道不平顺演化区间的GM(1, 1)-RVM组合模型;结合轨道质量指数(TQI)的振荡演变特性,通过二次-对数复合函数平滑优化和序列权重优化改进了GM(1, 1)模型,通过粒子群优化(PSO)算法对待优化参数进行搜索确定,并在此基础上计算点的预测值;构造以点预测值为输入,以TQI实测值为输出的样本特征映射模式,引入5折交叉验证环节优化与训练了RVM模型的组合核函数;通过GM(1, 1)模型与RVM模型间的输入-输出衔接机制集成了组合预测模型,并以某有砟线路中的2个区段为实例检验了轨道不平顺区间的预测效果。研究结果表明：与既有预测模型相比,改进GM(1, 1)-RVM组合模型可得到预测区间的均值和方差,从而将预测结果从单点数值扩充到预测区间;2个区段实例在外推区间上的点预测结果与TQI真实值相比,平均百分比误差分别为1.53%和4.67%,较支持向量回归(SVR)模型分别降低了0.58%和0.61%,较GM(1, 1)-反向传播神经网络(BPNN)模型分别降低了0.15%和1.87%;改进GM(1, 1)-RVM组合模型在90%、95%和99%三种置信度下的最大平均预测区间宽度分别为0.324 5、0.387 9和0.510 5 mm,最低预测区间覆盖率分别为91.67%、95.83%和95.83%,预测区间基本涵盖了外推区间内的TQI演化数据。可见,利用预测的均值和方差构造区间边界可有效把控轨道不平顺演变过程中的随机波动,为轨道不平顺预测提供了一种新思路。