Intelligent transportation systems: an impact analysis for Michigan

Are Intelligent Transportation Systems (ITS) affecting transportation industry only? ITS are currently perceived as a contribution to transportation industry only; however, by quantitatively analyzing the economic impact of ITS on the state of Michigan, this work attempts to answer the posed question. The quantitative economic analysis is carried out through the well‐established Leontief's Input–Output (I‐O) model. This model is employed to establish ITS effects on each industry by detailing RIMS II I‐O tables for Michigan constructed from the national I‐O tables. Major savings by ITS identified as reduced time delays and fuel cost savings are quantitatively simulated thereby generating an overall cost reduction factor which is incorporated in Michigan I‐O tables to modify their characteristics. ITS impact on each industry in I‐O tables is achieved by maximizing effects on certain selected industries. Impact multipliers that are customary macro‐economic measures for I‐O analysis are then calculated for all the aggregated industries. Multipliers comparison for the three cases namely before ITS implementation, conventional improvement methods, and after ITS implementation is evaluated. These values suggest greater economic benefits that may be achieved by statewide implementation of the ITS. Copyright © 2010 John Wiley & Sons, Ltd.     

Methods of analysis for vehicle soak time data

Vehicle soak time, the duration of time a vehicle’s engine is at rest prior to being started, and its distribution function are important transportation activity data inputs for mobile emissions inventory estimation due to their impacts on vehicle start and evaporative emissions. This paper provides vehicle emission researchers with an overview of statistical analysis methods relevant to analyzing vehicle soak time data. Many of these methods are already in use in emissions research and have appeared in the literature. These methods are reviewed and further details regarding the implementation and interpretation of these methods are provided. Statistical methods relevant to the analysis of soak time data that have yet to appear in the emissions literature, including kernel density estimation and generalized linear models, are also introduced. Advantages and disadvantages of the methods are compared and theoretical justification is provided. Issues of correlated observations and censored data are discussed. General guidelines for the analysis of soak time data, such as stratification by start type and geographical region, are established. Finally, a subset of the statistical methods discussed is used to analyze the US Environmental Protection Agency’s 3-city data.     

Computer modeling and validations of steel gear heat treatment processes using commercial software DANTE

The heat treatment process of a gear made of AISI 9310 carburized steel is modeled using the commercial heat treatment simulation software DANTE. Both carburization and quenching processes affect the residual stress distribution and distortion of heat-treated parts, which are important to service quality and fatigue life. DANTE/VCARB is used to design the boost/diffuse schedule of a vacuum carburization process. Oil quenching is modeled following the vacuum carburization process. Thermal gradient and phase transformation are two main sources of distortion and residual stresses in quenched parts. The relation of the carbon distribution, thermal gradient, and phase transformations during quenching is studied through the gear modeling example. Because of geometry, the residual stress distribution after quenching is non-uniform along the gear surface. In general, the root fillet has higher residual compression than either the root or tooth face locations after traditional oil quenching of carburized gears. The predicted residual stresses from the oil quenching model are imported into a single tooth fatigue bending model. The gear stresses under bending load indicate the possible cracking locations during the bending fatigue test. The importance of heat treatment residual stresses during gear design is pointed out, and it is commonly ignored in the gear design and manufacturing industry.     

汽车氙灯测试方法：相关性研究

1 背景 在1989年以前,汽车行业没有一个专门的氙灯加速老化测试方法的国际标准.1989年,汽车工程学会(SAE)发布了J1960"水冷式氙灯汽车外饰件加速暴露测试"和SAE J1885"水冷式氙灯汽车内饰件加速暴露测试"两项汽车材料耐候性测试标准.当时,SAE J1960和J1885有利于汽车制造商统一测试条件.但是,这些老化测试标准基于特定型号的设备构造,指定了某生产商的两种型号设备.     

The relationship between transportation perceptions and behavior over time

The aim of this paper is to discuss cross-lagged panel analysis in terms of the causal inferences it generates about the relationship of beliefs about modes and mode choice behavior. Frequencies of use of the single-occupant automobile (SOA), bus and carpool, as well as beliefs about each of the modes, were collected from a sample of central business district commuters at two points in time. The belief variables for each mode were summed to form composite measures and were corrected for unreliability due to measurement error.Perceptions of each mode and the frequency of its use were analyzed for influences operating over time. A time interval was assumed to exist during which the variables causally operated on each other. It was assumed that the time necessary for an individual to change modes based on his perception was equivalent to the interval required for a person to alter perceptions based on his experience. The causal structure relating the two variables was also assumed to be stable over time. An additional assumption was required to distinguish between third variable effects, or spuriousness, and dual causation: if a third variable were to be causing the relationship, it would be operating at a relatively constant rate over time.A strong causal relationship was found to be operating between beliefs about SOA and bus and use of those modes over time. The relationship is mutually causative; beliefs determine behavior and behavior reinforces and changes perceptions. Analysis of the carpool data indicated that the causal structure had changed over time and could not be analyzed with this technique. In general, support is evidenced for an adaptation or learning process interpretation of the relationship between beliefs and mode choice behavior.