Development of an Autonomous Flight Control System for Small Size Unmanned Helicopter Based on Dynamical Model

It is devoted to the development of an autonomous flight control system for small size unmanned helicopter based on dynamical model. At first, the mathematical model of a small size helicopter is described. After that simple but effective MTCV control algorithm was proposed. The whole flight control algorithm is composed of two parts: orientation controller based on the model for rotation dynamics and a robust position controller for a double integrator. The MTCV block is also used to achieve translation velocity control. To demonstrate the performance of the presented algorithm, simulation results and results achieved in real flight experiments were presented.     

基于SERVQUAL的网约车服务质量评价研究

随着“互联网+”的广泛应用以及网约车服务模式的合法化,使我国大城市网约车的服务质量问题愈发暴露出来。为提高网约车服务质量,根据SERVQUAL构建了网约车评价指标体系,并通过AHP研究方法和IPA分析分别对网约车服务影响因素和网约车服务评价指标进行了分析,从而提出网约车服务质量控制策略,对网约车服务质量改善具有一定的参考意义。     

驾驶人“感知-决策-操控”行为模型

为准确模拟驾驶人跟车行为,提出基于隐马尔可夫模型(Hidden Markov Model,HMM)的驾驶人“感知-决策-操控”行为模型。建立描述驾驶意愿的HMM模型,模拟驾驶人感知过程,获得期望的车间距;预测模块模拟驾驶人根据交通环境和自身生理、心理状态预测车辆未来轨迹,即决策过程;优化模块描述驾驶人为使预测的车辆轨迹跟踪上期望的车辆间距而采取的操控汽车的执行动作,即操控过程。上述3个模块的滚动过程实现了对驾驶人跟车行为的模拟。利用自然驾驶数据进行算例分析,结果表明,本文模型预测车间距平均误差仅为1.47%,证明了所建模型的有效性及准确性。本文为驾驶行为建模方法的理论研究和应用拓宽了思路。     

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。     

点汇聚系统的航空器污染物减排效应与机理

为研究点汇聚系统的环境效益及减排机理,采用考虑气象条件修正后的航空器性能、燃油 流量及污染物计算模型,设计了理想条件下非高峰时刻与实际运行的高峰时刻两种场景,对比分 析了航空器在点汇聚系统与标准进场程序中污染物(即HC、CO、NOX、SOX和PM)的排放情况,并 从飞行时间、燃油消耗与排放指数3个方面分析了点汇聚系统的减排机理、识别了减排关键因素。 研究发现：在非高峰时刻,点汇聚系统与标准进场程序的污染物排放总量分别为5.79 kg与7.17 kg, 点汇聚系统较标准进场程序共减少约19.25%污染物排放,对NOX、SOX和PM减排效果显著;在高 峰时刻,点汇聚系统与标准进场程序的污染物排放总量分别为290.01 kg与406.69 kg,点汇聚系 统较标准进场程序共减少28.69%污染物排放,其中NOX减排比例最高可达48.32%。结果表明： 无论是非高峰时刻还是高峰时刻,点汇聚系统都具有良好的环境效益,可有效减少污染物的排放 总量,且对NOX减排效果最佳;较短的飞行时间、较低的燃油流量是点汇聚系统体现减排优势的 关键驱动因素。     

新冠肺炎交通防控政策对长沙市人口流动的影响

为探究新冠肺炎疫情下交通防控政策对长沙市人口流动的影响,本文根据长沙市在新冠 肺炎疫情期间颁布的交通防控政策和疫情实时防控情况划分防控阶段,基于百度迁徙大数据,利 用双重差分模型,识别长沙市不同阶段的交通防控政策以及量化防控效果,分析交通防控政策对 长沙市人口流动的影响。结果显示,长沙市在交通管制阶段,平均人口迁出强度、平均人口迁入 强度及城市内部出行强度分别下降了83.68%、69.24%及59.74%,有效地控制了人口流动,降低了 疫情扩散危险。在交通恢复阶段,长沙市人口流动强度逐渐反弹,城市内部出行强度基本恢复到 2019年同期水平。本文研究结果显示了交通管制对疫情扩散限制的有效性,为常态化疫情防控 下精准防控政策和复工复产政策制定提供参考。     

收费公路资产证券化定价及发行方法研究

从数据驱动视角出发,本文探讨收费公路资产支持证券的结构化定价方法。首先,构建反 映交通量风险特征的收费公路通行收入自回归积分滑动平均模型;其次,基于瀑布式偿付结构和 到期收益率Nelson-Siegel模型计算不同等级证券发债规模与息票利率;最后结合成渝高速和渝 蓉高速现实数据进行仿真分析。研究发现,高速公路成熟期的通行收入具有尖峰厚尾特征,成长 期的通行收入更兼具显著的波动聚集效应。数据驱动的定价模型能够刻画通行收入的风险特 征,合理估计优先级的证券融资比例,比只考虑利率风险的现金流折现法更具有优势;从发行方 式来看,合并发行的融资结构显著优于单独发行。管理启示是改变以单个高速公路项目为基础 的特许经营权融资,通过绩优绩差项目的合并发行优化融资结构。     

少学时“机械零部件识图”课程的线上线下融合教学模式探讨

"机械零部件识图"是汽车类专业的一门必修专业基础课,本文基于现有"机械零部件识图"在线开放课程资源,探讨线上与线下交互融合教学模式在少学时"机械零部件识图"课程中的应用。     

Towards data-driven car-following models

Car following models have been studied with many diverse approaches for decades. Nowadays, technological advances have significantly improved our traffic data collection capabilities. Conventional car following models rely on mathematical formulas and are derived from traffic flow theory; a property that often makes them more restrictive. On the other hand, data-driven approaches are more flexible and allow the incorporation of additional information to the model; however, they may not provide as much insight into traffic flow theory as the traditional models. In this research, an innovative methodological framework based on a data-driven approach is proposed for the estimation of car-following models, suitable for incorporation into microscopic traffic simulation models. An existing technique, i.e. locally weighted regression (loess), is defined through an optimization problem and is employed in a novel way. The proposed methodology is demonstrated using data collected from a sequence of instrumented vehicles in Naples, Italy. Gipps’ model, one of the most extensively used car-following models, is calibrated against the same data and used as a reference benchmark. Optimization issues are raised in both cases. The obtained results suggest that data-driven car-following models could be a promising research direction.     

The relationship between land use and automobile travel utility: A Multiple Indicators Multiple Causes approach

The relationship between land use and the utility of automobile travel is examined by refining the utility concept, particularly by combining the microeconomic utility theory, which is concerned with the disutility of travel, and the perspective on the positive utility. A conceptual model is accordingly developed and then adjusted considering different purposes of travel. The purpose-specific models are tested through a Multiple Indicators Multiple Causes approach in Seoul, Korea, using datasets from a sample survey and geographic information systems. The major finding is that land use affects the utility mainly by changing synergy and affective utility rather than instrumental utility, which encompasses disutility variables. Among land use variables, the utility is found to be the most sensitive to the number of transit facilities for commuting and shopping travel and land use balance for leisure travel.