An application of a rank ordered probit modeling approach to understanding level of interest in autonomous vehicles

Surveys of behavior could benefit from information about people’s relative ranking of choice alternatives. Rank ordered data are often collected in stated preference surveys where respondents are asked to rank hypothetical alternatives (rather than choose a single alternative) to better understand their relative preferences. Despite the widespread interest in collecting data on and modeling people’s preferences for choice alternatives, rank-ordered data are rarely collected in travel surveys and very little progress has been made in the ability to rigorously model such data and obtain reliable parameter estimates. This paper presents a rank ordered probit modeling approach that overcomes limitations associated with prior approaches in analyzing rank ordered data. The efficacy of the rank ordered probit modeling methodology is demonstrated through an application of the model to understand preferences for alternative configurations of autonomous vehicles (AV) using the 2015 Puget Sound Regional Travel Study survey data set. The methodology offers behaviorally intuitive model results with a variety of socio-economic and demographic characteristics, including age, gender, household income, education, employment and household structure, significantly influencing preference for alternative configurations of AV adoption, ownership, and shared usage. The ability to estimate rank ordered probit models offers a pathway for better utilizing rank ordered data to understand preferences and recognize that choices may not be absolute in many instances.     

建成环境对城市停车需求影响的非线性模型

为精细化把握城市建设项目在微观空间尺度下的停车需求规律，从空间视角探究停车需 求与建成环境之间的关系。通过高峰小时建筑物单位面积的停车生成数表征停车需求，以土地 利用混合度、路网密度、公交服务水平等9个因子描述建成环境，分别构建建成环境对停车需求影 响的普通最小二乘(Ordinary Least Squares，OLS)模型与梯度提升迭代决策树(Gradient Boosting Decision Tree，GBDT)模型。以保定市主城区停车调查数据中的商业类配建停车场为对象，基于 停车调查数据、兴趣点数据(Point of Interst，POI)、道路网络数据等多源异构数据进行模型实证分 析。结果表明，考虑非线性效应的GBDT模型比OLS模型具有更好的拟合度。从影响贡献度来 看，配建指标(18.92%)与区位(15.23%)是影响停车需求的最重要建成环境因素，交叉口密度 (5.19%)贡献度最小；在非线性关系方面，建成环境因子与停车需求均具有非线性关系与阈值效 应，除交叉口密度及人口密度与停车需求呈现U型关系，其余因素与停车需求的关系整体上保持 正相关或负相关。     

中低速磁浮列车悬浮控制系统可靠性分析及优化

针对中低速磁浮列车的悬浮控制系统建立了可靠性模型,分析了其故障率、平均故障间隔时间等可靠性指标及其故障形式,找出可靠性的薄弱点.针对薄弱点进行了相应的优化,进一步提升了系统的可靠性.评估分析了各项优化在救援故障、晚点故障以及一般故障等实际运营上的优化效果.     

不同滑带角度滑坡作用下隧道衬砌结构受力特征

随着越来越多的隧道工程穿越滑坡区，滑坡与隧道相互作用过程的研究尤为重要. 为研究不同滑带角度滑坡对隧道衬砌结构受力的影响，以大（同）准（格尔）铁路南坪隧道为例，采用室内模型试验、数值模拟的方法，对0°、10°、20°、30°、40°、50°不同滑带角度条件下滑坡推力作用下隧道衬砌结构受力的影响特征及变化规律进行研究. 研究结果表明:滑带角度越小，隧道变形越大，作用在隧道衬砌结构上的弯矩、剪力及土压力越大，并在拱脚处出现最大值，形成隧道拱结构左右受力不对称特征，呈现偏压现象；通过计算隧道拱结构左右两侧的竖向偏压应力比显示，在拱肩位置且滑带为0时，偏压应力比为1.17，随着滑带角度的增大，隧道衬砌拱结构左右应力差越来越小，趋于平衡拱；在拱脚位置，偏压应力比随滑带角度的增大而逐渐增大，隧道衬砌拱结构左右两侧所受应力差越来越大，趋向于偏压隧道，最小偏压比和最大偏压比分别为1.08、1.87.      

基于杨木木屑改性的隧道工程透水砂浆性能研究

为研制出一种既能适用于初期支护基面找平又具有一定透水能力的砂浆，将杨木木屑加入砂浆试块中对其进行改性。通过对杨木木屑的降解特性进行分析，并将不同掺量的杨木木屑加入砂浆试块中，改变其养护方式，对其体积密度、孔隙率、吸水率、抗压强度等基本性能指标及微观结构进行测试和分析。由试验结果可知： 1）加入杨木木屑后普通砂浆试块的孔隙率和吸水率均提升了2～3倍，但同时也会使砂浆试块的体积密度和抗压强度降低； 2）内掺碱并加碱溶液养护方式的孔隙率和吸水率最高，体积密度和抗压强度最低。对砂浆试块吸水率和抗压强度进行线性拟合后发现其吸水率和抗压强度呈负相关关系。研究结果表明，通过杨木木屑提升砂浆透水性能是可行的，但是杨木木屑掺量和养护方式对砂浆其他性能的影响还有待进一步研究。     

数据驱动跟驰模型综述

车辆跟驰模型是被交通科学与交通工程领域广泛认可的微观交通流模型，是交通流理论 的基础。近年来，信息感知与获取、大数据、人工智能等技术快速发展，推动了数据驱动跟驰模型 的快速发展。数据驱动跟驰模型，是以真实的车辆行驶数据为基础，利用数据科学与机器学习等 理论和方法，通过样本数据的训练、学习、迭代、进化，挖掘车辆跟驰行为的内在规律。本文系统 回顾了数据驱动跟驰模型在过去20余年的发展历程以及由神经网络和深度学习带动的两次研究 热潮，归纳了基于传统机器学习理论的跟驰模型、基于深度学习的跟驰模型、模型与数据混合驱 动的跟驰模型3类数据驱动跟驰模型，并分别介绍了其中的典型代表。分析数据源发现，尽管各 种高精度轨迹数据不断涌现，目前研究仍多使用美国于2006年发布的Next Generation Simulation (NGSIM)高精度车辆轨迹数据，模型的可移植性和泛化能力值得思考与研究。提出关于模型输 入、输出的3个问题：如何考虑更多驾驶行为变量，是否有必要考虑更多行为变量，现有输入、输出 是否可替换。在模型测试与验证方面，发现并讨论了目前测试不充分、对比不完整、缺少统一测 试集与测试标准等问题。最后，探讨了数据驱动跟驰模型原创性与成功的关键因素等问题。期 望通过本文的梳理，帮助研究者更好地了解数据驱动跟驰模型的过去与现状，促进相关研究的快 速发展。     

新时代我国内河水运高质量发展思路

新时代，我国交通发展步入全面建设交通强国的新阶段，内河水运的结构性短板愈发凸显。2020—2035年是我国内河水运发展的关键期，面临补齐短板和高质量发展的双重要求。分析当前我国内河水运发展基础及存在的问题，研判新时代经济、产业发展新趋势对内河水运的影响和要求，解析新时代大力推动我国内河水运高质量发展的意义和内河水运高质量发展的内涵，并针对新时代我国内河水运如何实现高质量发展提出对策与建议，为全面建成人民满意、保障有力、世界前列的现代化内河航运体系提供参考。     

Improving the coexistence of offshore wind farms and shipping: an international comparison of navigational risk assessment processes

The continued growth and evolution of the offshore wind industry, and the emergence of other novel marine uses such as wave and tidal generators, have upped the ante for spatial planners, as well as consenting and approval authorities in various coastal states. These stakeholders rely on processes such as navigational risk assessments (NRAs) to balance safety and efficiency requirements and to make optimal decisions over use of space. Given the increasingly complex and crowded seascape, however, there are some apprehensions about potential shortcomings in these NRA processes. There is also some concern that these inadequacies may lead to unsafe or inefficient marine spatial use. To understand how NRA processes can be improved further, a literature review is conducted, followed by a survey of respondents who are involved in the planning, consenting and/or approval of offshore wind farms across seven different countries. A summary of the NRA processes in these seven countries is presented, and several shortcomings are identified. Based on the findings of the survey, a list of recommendations is presented to enhance existing NRA processes—and to improve the coexistence of shipping and offshore wind farms (OWFs).     

自校正极点配置解耦控制器

本文提出的多变量自校正控制器，完成了对控制系统的闭环及极点配置和动态解耦，且能使系统对阶跃给定值实现稳态无差跟踪。本算法适用于最小相位系统、非最小相位系统以及各道时延不等的系统。数字仿睦证实了本算法的良好性能。     

Energy-saving reputation method for wireless sensor networks

Reputation mechanism has been recently introduced into wireless sensor networks (WSNs) to overcome the deficiencies incurred by cryptography alone. Unlike most current reputation mechanisms that are based on binomial distribution and to some extent, do not give due attention to the aspect of energy constraint of WSNs, this research deduces and proves the feasibility that negative binomial distribution can well be used in the reputation update with distinctive energy-saving features. Comparison tests with respect to the energy consumption in terms of reputation computing frequencies are done between the traditional reputation method and the one in this study. Results show that our method can save more energy for the reputation update and thus can better meet the power constraints of WSNs.