驾驶过程中手机使用行为数据采集及分析

驾驶过程中使用手机的行为存在安全隐患,是当今导致交通安全事故的原因之一。文章借助于信息技术开发出采集使用手机行为数据的App,从真实数据出发,客观地分析驾驶员在开车过程中使用手机这一不良驾驶行为的覆盖程度及危险程度,并提出将手机使用行为作为驾驶风险评价因子。     

CATIA VBA二次开发在装配中批量搜索零件的应用

文章针对汽车开发过程中,经常需要对部分特定的零件进行批量选择用于校核或者作为报告用图,给出了装配中对指定的多个零件进行程序搜索选定的方法。在介绍VB编程语言及CATIA二次开发理论的基础上,给出程序自动搜索零件的基本流程,利用CATIA Automation调用Search方法,实现零件的批量搜索。通过实例验证,证明了CATIA批量搜索的优越性。结果表明,CATIA二次开发可以提高工作效率。     

体外预应力技术在牛首山河大桥加固中的应用

体外预应力技术是加固既有钢筋混凝土桥梁结构最有效的方法之一,本文结合南京机场高速牛首山河大桥体外预应力加固工程实例,介绍了这一技术的设计原则、施工工艺及其效果评价、检验方法等。     

汽车非确定性故障诊断模型研究

针对汽车非确定性故障诊断特点,基于模糊数学理论,提出了由概率统计法及专家优序法综合确定模糊隶属度的方法,结合对故障程度的描述,建立汽车非确定性故障诊断数学模型。以此为基础,有选择地借鉴模糊数学理论中的模式识别方法对故障进行诊断,最后以实例验证了该模型的实用性。     

不等保护喷泉码用于应答器报文编码的可行性研究

提出将具有不等差错保护特性数字喷泉码作为欧标高速铁路应答器报文编码方案.由于其不依赖重传、译码过程与编码符号到达译码端次序无关等特性非常适于应答器报文传输场景.针对欧标应答器报文长度特征,本文在不同码长条件下对基于权重的不等差错保护算法、拓展窗口喷泉码及复制窗口不等保护算法的性能进行对比分析,通过Matlab仿真得到3种算法在不同码长条件下的性能.验证采用基于喷泉码的不等差错保护方法作为铁路应答器长报文编码方案的可行性.     

CFD Analysis of Ship-to-Ship Hydrodynamic Interaction

A numerical study of ship-to-ship interaction forces is performed using a commercial CFD code, and the results are compared with experimental data and with the results of a panel method analysis. Two ship models have been used in the interaction forces analysis: a tug and a tanker, advancing parallel to each other with different lateral distances and two different values of the fluid depth. Computations are carried out with four different flow models: inviscid and viscous flow with the free surface modeled as a rigid wall and inviscid and viscous flow with the deformable free surface. A fair agreement was obtained with available experimental data and results obtained by panel method. The influence of viscosity in the computations is found to be comparatively weak, while the wavemaking effects may be important, at small magnitude of the horizontal clearance.     

Are we ready to embrace connected and self-driving vehicles? A case study of Texans

While connected, highly automated, and autonomous vehicles (CAVs) will eventually hit the roads, their success and market penetration rates depend largely on public opinions regarding benefits, concerns, and adoption of these technologies. Additionally, the introduction of these technologies is accompanied by uncertainties in their effects on the carsharing market and land use patterns, and raises the need for tolling policies to appease the travel demand induced due to the increased convenience. To these ends, this study surveyed 1088 respondents across Texas to understand their opinions about smart vehicle technologies and related decisions. The key summary statistics indicate that Texans are willing to pay (WTP) $2910, $4607, $7589, and $127 for Level 2, Level 3, and Level 4 automation and connectivity, respectively, on average. Moreover, affordability and equipment failure are Texans’ top two concerns regarding AVs. This study also estimates interval regression and ordered probit models to understand the multivariate correlation between explanatory variables, such as demographics, built-environment attributes, travel patterns, and crash histories, and response variables, including willingness to pay for CAV technologies, adoption rates of shared AVs at different pricing points, home location shift decisions, adoption timing of automation technologies, and opinions about various tolling policies. The practically significant relationships indicate that more experienced licensed drivers and older people associate lower WTP values with all new vehicle technologies. Such parameter estimates help not only in forecasting long-term adoption of CAV technologies, but also help transportation planners in understanding the characteristics of regions with high or low future-year CAV adoption levels, and subsequently, develop smart strategies in respective regions.     

How household transportation expenditures have evolved in Canada: a long term perspective

In recent years, increasing recognition of the challenges associated with global climate change and inequity in developed countries have revived researcher’s interest towards analyzing transportation related expenditure of households. The current research contributes to travel behaviour literature by developing an econometric model of household budgetary allocations with a particular focus on transportation expenditure. Towards this end, we employ the public-use micro-data extracted from the Survey of Household Spending (SHS) for the years 1997–2009. The proposed econometric modeling approach is built on the multiple discrete continuous extreme value model (MDCEV) framework. Specifically, in our analysis, the scaled version of the MDCEV model outperformed its other counterparts. Broadly, the model results indicated that a host of household socio-economic and demographic attributes along with the residential location characteristics affect the apportioning of income to various expenditure categories and savings. We also observed a relatively stable transportation spending behaviour over time. Additionally, a policy analysis exercise is conducted where we observed that with increase in health expenses and reduction in savings results in adjustments in all expenditure categories.     

Robust network pricing and system optimization under combined long-term stochasticity and elasticity of travel demand

Network pricing serves as an instrument for congestion management, however, agencies and planners often encounter problems of estimating appropriate toll prices. Tolls are commonly estimated for a single-point deterministic travel demand, which may lead to imperfect policy decisions due to inherent uncertainties in future travel demand. Previous research has addressed the issue of demand uncertainty in the pricing context, but the elastic nature of demand along with its uncertainty has not been explicitly considered. Similarly, interactions between elasticity and uncertainty of demand have not been characterized. This study addresses these gaps and proposes a framework to estimate nearest optimal first-best tolls under long-term stochasticity in elastic demand. We show first that the optimal tolls under the deterministic-elastic and stochastic-elastic demand cases coincide when cost and demand functions are linear, and the set of equilibrium paths is constant. These assumptions are restrictive, so three larger networks are considered numerically, and the subsequent pricing decisions are assessed. The results of the numerical experiments suggest that in many cases, optimal pricing decisions under the combined stochastic-elastic demand scenario resemble those when demand is known exactly. The applications in this study thus suggest that inclusion of demand elasticity offsets the need of considering future demand uncertainties for first-best congestion pricing frameworks.     

Activity involvement and time spent on computers for leisure: an econometric analysis on the American Time Use Survey dataset

Internet is capturing more and more of our time each day, and the increasing levels of engagement are mainly due to the use of social media. Time spent on social media is observed in the American Time Use Survey and recorded as leisure time on Personal Computer (PC). In this paper, we extend the traditional analysis of leisure activity participation by including leisure activities that require the use of a PC. We study the substitution effects with both in-home and out-of-home leisure activities and the time budget allocated to each of them. The modeling framework that includes both discrete alternatives and continuous decision variables allow for full correlation across the utility of the alternatives that are all of leisure type and the regressions that model the time allocated to each activity. Results show that there is little substitution effect between leisure with PC and the relative time spent on it, with in-home and out-of-home leisure episodes. Households with more children and full-time workers are more likely to engage in in-home and PC related leisure activities (especially during weekends). Increments in the travel time of social trips result in significant reductions in leisure time during weekdays.