Analysis of three independent real-world driving studies: A data driven and expert analysis approach to determining parameters affecting fuel economy

It is well established that individual variations in driving style have a significant impact on vehicle energy efficiency. The literature shows certain parameters have been linked to good fuel economy, specifically acceleration, throttle use, number of stop/starts and gear change behaviours. The primary aim of this study was to examine what driving parameters are specifically related to good fuel economy using a non-homogeneous extended data set of vehicles and drivers over real-world driving scenarios spanning two countries. The analysis presented in this paper shows how three completely independent studies looking at the same factor (i.e., the influence of driver behaviour on fuel efficiency) can be evaluated, and, despite their notable differences in location, environment, route, vehicle and drivers, can be compared on broadly similar terms. The data from the three studies were analysed in two ways; firstly, using expert analysis and the second a purely data driven approach. The various models and experts concurred that a combination of at least one factor from the each of the categories of vehicle speed, engine speed, acceleration and throttle position were required to accurately predict the impact on fuel economy. The identification of standard deviation of speed as the primary contributing factor to fuel economy, as identified by both the expert and data driven analysis, is also an important finding. Finally, this study has illustrated how various seemingly independent studies can be brought together, analysed as a whole and meaningful conclusions extracted from the combined data set.     

基于ANFIS的特高压输电线路故障分类识别方法

提出了一种基于自适应神经模糊推理系统(ANFIS)的特高压输电线路故障分类识别方法,以分类识别10种常见的输电线路故障.该方法以故障后1个工频周期内故障电流分量的标准差和四分位距作为故障分类识别的特征量.分析了噪声和谐波对这2个特征量的影响;建立了基于ANFIS的故障分类识别模型.大量仿真试验表明:提出的故障分类识别方法能快速、准确地识别各类故障,并且不易受故障初始角、故障位置和过渡电阻的影响,对噪声、谐波、电流互感器传变特性及采样频率有良好的适应性,分类识别正确率能达到99.5%.     

单船动态风险评估模糊推理系统的设计与模拟

对在航船舶进行动态的风险评估,分析影响船舶安全航行的风险因素,构建了船舶风险评估模型,定义了各项风险因素的隶属函数,并参考专家经验建立模糊推理规则.按照建立的模型,在MATLAB平台上运用模糊推理系统构建风险评估系统,并应用该系统对在航船舶进行定量风险评估.实例证明,该系统能够合理地评估在航船舶的整体风险水平,为驾驶员决策提供参考,具有应用潜力.     

A bayesian dynamic linear model approach for real-time short-term freeway travel time prediction

This paper presents a Bayesian inference-based dynamic linear model (DLM) to predict online short-term travel time on a freeway stretch. The proposed method considers the predicted freeway travel time as the sum of the median of historical travel times, time-varying random variations in travel time, and a model evolution error, where the median is employed to recognize the primary travel time pattern while the variation captures unexpected supply (i.e. capacity) reduction and demand fluctuations. Bayesian forecasting is a learning process that revises sequentially the state of a priori knowledge of travel time based on newly available information. The prediction result is a posterior travel time distribution that can be employed to generate a single-value (typically but not necessarily the mean) travel time as well as a confidence interval representing the uncertainty of travel time prediction. To better track travel time fluctuations during non-recurrent congestion due to unforeseen events (e.g., incidents, accidents, or bad weather), the DLM is integrated into an adaptive control framework that can automatically learn and adjust the system evolution noise level. The experiment results based on the real loop detector data of an I-66 segment in Northern Virginia suggest that the proposed method is able to provide accurate and reliable travel time prediction under both recurrent and non-recurrent traffic conditions.     

“十三五”时期我国综合交通投资和运输形势分析

以综合交通运输行业发展中的建设投资、路网设施、旅客运输、货物运输方面统计数据指标为基础,通过分析总量指标在“十三五”时期的绝对值、增速,以及各交通运输方式结构指标变化情况,并与“十二五”时期相关指标变化进行对比分析,总结了“十三五”时期的综合交通建设投资、旅客运输和货物运输形势,表明“十三五”时期是综合交通运输从高速度增长向高质量发展的过渡阶段,具体表现为总量扩张趋缓、结构优化明显、升级动力减弱等三大特征,体现在交通投资和供给需求规模增速放缓,高快速交通方式的供需比重增加,交通生产投入的边际产出趋向递减等方面。     

基于模糊推理的跟驰安全距离控制算法及实现

车辆跟驰是普遍存在的交通现象之一。由于驾驶员在控制车辆过程中具有模糊的、不确定性的行为特征，难以对驾驶员的行为进行精确的数学描述，此外，为保证车辆行驶的安全，有必要对车辆跟驰时如何保持安全距离进行研究。基于此，提出基于模糊推理的车辆跟驰间距控制算法，并对其进行了仿真运算。仿真结果表明，用模糊推理模拟驾驶员的行为是可行的，并且通过模糊推理控制后车的速度，后车能够以安全距离跟随前车安全行驶。     

基于理性推理的观点演化模型

针对现实生活中个体观点形成中的理性推理过程,提出了一种基于DS（Dempster-Shafer）证据理论的观点更新规则,并在连续观点离散决策的框架下,对个体如何利用其他个体的意见和相关知识来形成自身意见的过程进行建模.实验仿真结果表明,群体中能够出现观点一致、分散等常见的舆论现象,观点演化过程中伴随着知识由确定性个体向不确定性个体的扩散,同时发现在规则网络中,意见领袖的观点的影响力在扩散过程中是逐渐减弱的,其影响范围与个体对知识不确定性的接受程度密切相关.     

基于模糊推理的潜艇舱室温度控制研究

针对复杂的具有不确定数学模型的潜艇舱室温度环境，利用模糊推理的设计思想，提出了一种参数自调整双层模糊控制器，用于舱室温度控制，并通过采用确定性模糊调整规则，简化了模糊控制器的设计，具有较强的适应性和鲁棒性。仿真结果表明，该控制器明显地改善了控制系统的性能。     

统计方法在沥青路面施工管理中的应用

文章介绍了统计方法在沥青路面施工质量动态管理中的应用,提出了用平均值-标准差控制图、平均值-极差控制图,对沥青路面施工过程中的检测数据进行动态分析,及时检查出施工中的变异性环节,使沥青路面的施工质量处于较为理想的受控状态。     

提高对公路统计工作的认识加强公路统计分析职能

从对公路统计工作的作用、特点，重点论述了在完成统计基础工作的同时加强统计分析的重要性。