手机社交娱乐操作对驾驶人视觉参数影响研究

为探究手机社交娱乐操作对驾驶人视觉参数的影响,基于模拟驾驶器和SmartEye 眼动仪设计模拟驾驶实验,采集驾驶人正常驾驶、拨打电话、收发信息等5种操作下视觉特征数据,应用拉依达准则和联合频数分布直方图进行数据归约.针对不同操作下视觉参数,应用箱形图和Kruskal-Wallis显著性检验,验证视觉参数评价指标的有效性.结果表明：随手机操作类型的不同,驾驶人水平和垂直视角标准差、视线离开道路时间百分比、扫视平均速度和眨眼持续时间呈显著性变化,可作为视觉参数影响的评价指标;观看视频和刷朋友圈操作对驾驶人视觉影响程度明显高于拨打电话和收发短信操作.     

To fully automate or not? Investigating demands and willingness to pay for autonomous vehicles based on automation levels

We review the literature on the motivators of consumers’ purchasing decisions regarding autonomous vehicles (AVs), focusing on environmental awareness, vehicle types, concerns about accidents, and merits. The willingness to buy (WTB) and willingness to pay (WTP) for AVs have been extensively studied. However, it is imperative to broaden the outlook and consolidate the existing knowledge base. Although the WTB and WTP for AVs have been studied, automation-level preferences have been underresearched. Based on more than 150,000 observations, we construct a choice model and calculate the WTP for each level of automation. Our results show that there is a disparity between people's WTB and WTP according to the automation level. In particular, we find that the coefficients regarding FAVs’ benefits and accidents significantly exceed those related to environmental concerns. Such results indicate that practical benefits and concerns influence AV demand and WTP to a greater extent than environmental awareness. Our model results indicate a disparity between WTB and WTP for AVs that mostly derives from the different types of environmental concerns, concerns about accidents, and perceived benefits from these automobiles. Our results urge policies according to which governments and companies closely examine consumers who exhibit WTB-WTP disparities. Issues relating to demand patterns, WTP, and a suitable policy framework are discussed.     

基于灰色二次指数平滑的QAR参数关联度预测

由于QAR参数波动的后果具有潜在性和滞后性,因此分析人员容易忽视其危险性而使其发展为不利事件。针对该问题,提出运用QAR参数关联度分析的方法,建立QAR参数波动与事件之间的对应关系;并构建基于二次指数平滑-灰色关联法的关联度预测模型,进一步明确QAR参数波动与事件之间关系的动态变化趋势。运用实际数据对该模型进行验证,表明结论符合实际,该模型科学合理,可以用于QAR参数分析过程。     

Sea state estimation based on vessel motion responses: Improved smoothness and robustness using Bézier surface and L1 optimization

Floating structures oscillate in waves, where these wave-induced motions may be critical for various marine operations. An important consideration is thereby given to the sea states at the planning and operating stages for an offshore project. The most important information extracted from a sea state is the directional wave spectrum, indicating wave direction, significant wave height, and wave spectrum peak period. Among several available methods of measuring and estimating the directional wave spectrum, the wave buoy analogy technique based on vessel motion responses is an in situ and almost real-time solution without extra costs of devices. If the forms of the wave spectra are not predefined in the estimation, the method is called a nonparametric approach. Its most remarkable advantage is the flexible form, but the smoothness should be regulated. After the discrete Fourier transform has been applied to the measured vessel motions, smoothing is necessary. However, this process results in disturbed vessel cross-spectra and a lowpass characteristic of the windowing function. This paper presents a nonparametric approach for directional wave spectrum estimation based on vessel motion responses. It introduces novel smoothness constraints using Bézier surface and includes a more robust estimate using L1 optimization. Both techniques are applied to the wave buoy analogy for the first time. Numerical simulations are conducted to verify the proposed algorithm.     

基于BP网络的球鼻首参数化优化

本文以某集装箱船为研究对象,对降速航行后的球鼻首进行优化。采用Catia建立船体三维模型,为了产生不同形状的球鼻首,选取球鼻特征参数来描述其基本结构;采用拉丁超立方试验抽样方法得到12组不同形状的球鼻首,提出运用非线性拟合能力较强的BP网络构建球鼻首参数和阻力系数之间的关系模型;采用遗传算法对训练后的网络进行极值寻优。结果显示,优化船型的阻力系数显著降低,说明该方法对球鼻首的优化有一定的借鉴意义。     

A generic data assimilation framework for vehicle trajectory reconstruction on signalized urban arterials using particle filters

With trajectory data, a complete microscopic and macroscopic picture of traffic flow operations can be obtained. However, trajectory data are difficult to observe over large spatiotemporal regions—particularly in urban contexts—due to practical, technical and financial constraints. The next best thing is to estimate plausible trajectories from whatever data are available. This paper presents a generic data assimilation framework to reconstruct such plausible trajectories on signalized urban arterials using microscopic traffic flow models and data from loops (individual vehicle passages and thus vehicle counts); traffic control data; and (sparse) travel time measurements from whatever source available. The key problem we address is that loops suffer from miss- and over-counts, which result in unbounded errors in vehicle accumulations, rendering trajectory reconstruction highly problematic. Our framework solves this problem in two ways. First, we correct the systematic error in vehicle accumulation by fusing the counts with sparsely available travel times. Second, the proposed framework uses particle filtering and an innovative hierarchical resampling scheme, which effectively integrates over the remaining error distribution, resulting in plausible trajectories. The proposed data assimilation framework is tested and validated using simulated data. Experiments and an extensive sensitivity analysis show that the proposed method is robust to errors both in the model and in the measurements, and provides good estimations for vehicle accumulation and vehicle trajectories with moderate sensor quality. The framework does not impose restrictions on the type of microscopic models used and can be naturally extended to include and estimate additional trajectory attributes such as destination and path, given data are available for assimilation.     

沉管隧道地震响应分析

文章采用田村重四郎和冈本舜三提出的沉埋隧道地震响应分析的数学模型,对南京长江越江隧道(沉管段)方案进行了地震响应的纵向受力分析。分析中采用了隧址处100年超越概率为2%的人工合成地震加速度,考虑了不同管段的联结方式和不同的计算参数,对初步设计中沉管段结构的安全性进行了论证,所提供的数据曾为设计单位所采纳[1]。     

清管器在输油管道中的运动规律研究

在分析清管物理模型的基础上,建立了清管器前段塞流动的特征参数计算模型、动态数学模型以及相应的数值计算方法,并进行了数值模拟。利用数学模拟方法可以计算清管过程中管线的压力分布,利用压力分布可以跟踪清管球在管线内的运行,这为混输管路的运行管理提供了理论依据。     

Towards a generic benchmarking platform for origin–destination flows estimation/updating algorithms: Design,demonstration and validation

Estimation/updating of Origin–Destination (OD) flows and other traffic state parameters is a classical, widely adopted procedure in transport engineering, both in off-line and in on-line contexts. Notwithstanding numerous approaches proposed in the literature, there is still room for considerable improvements, also leveraging the unprecedented opportunity offered by information and communication technologies and big data. A key issue relates to the unobservability of OD flows in real networks – except from closed highway systems – thus leading to inherent difficulties in measuring performance of OD flows estimation/updating methods and algorithms. Starting from these premises, the paper proposes a common evaluation and benchmarking framework, providing a synthetic test bed, which enables implementation and comparison of OD estimation/updating algorithms and methodologies under “standardized” conditions. The framework, implemented in a platform available to interested parties upon request, has been flexibly designed and allows comparing a variety of approaches under various settings and conditions. Specifically, the structure and the key features of the framework are presented, along with a detailed experimental design for the application of different dynamic OD flow estimation algorithms. By way of example, applications to both off-line/planning and on-line algorithms are presented, together with a demonstration of the extensibility of the presented framework to accommodate additional data sources.     

Validating and improving public transport origin–destination estimation algorithm using smart card fare data

Smart card data are increasingly used for transit network planning, passengers’ behaviour analysis and network demand forecasting. Public transport origin–destination (O–D) estimation is a significant product of processing smart card data. In recent years, various O–D estimation methods using the trip-chaining approach have attracted much attention from both researchers and practitioners. However, the validity of these estimation methods has not been extensively investigated. This is mainly because these datasets usually lack data about passengers’ alighting, as passengers are often required to tap their smart cards only when boarding a public transport service. Thus, this paper has two main objectives. First, the paper reports on the implementation and validation of the existing O–D estimation method using the unique smart card dataset of the South-East Queensland public transport network which includes data on both boarding stops and alighting stops. Second, the paper improves the O–D estimation algorithm and empirically examines these improvements, relying on this unique dataset. The evaluation of the last destination assumption of the trip-chaining method shows a significant negative impact on the matching results of the differences between actual boarding/alighting times and the public transport schedules. The proposed changes to the algorithm improve the average distance between the actual and estimated alighting stops, as this distance is reduced from 806 m using the original algorithm to 530 m after applying the suggested improvements.