A trip generation model has been developed using a time-use perspective, in which trips are generated in conjunction with out-of-home activities, and time spent traveling is another component of overall time use. The model jointly forecasts three sets of endogenous variables- (1) activity participation and (2) travel time (together making up total out-of-home time use) and (3) trip generation – as a function of household characteristics and accessibility indices. It is estimated with data from the Portland, Oregon  1994 Activity and Travel Survey. Results show that the basic model, which has ten endogenous time use and trip generation variables and thirteen exogenous variables, fits well, and all postulated relationships are upheld. Test show that the basic model, which divides activities into work and nonwork, can be extended to a three-way breakdown of subsistence, discretionary and obligatory activities. The model can also capture the effects of in-home work on trip chaining and activity participation. We use the model to explore the effects on time use and trip chaining of GIS-based and zone-based accessibility indices.  

This review is divided into three sections: technologies for monitoring physiological parameters; biosensors for chemical assays and wireless communications technologies including image transmissions. Applications range from monitoring high risk patients for heart, respiratory activity and falls to sensing levels of physical activity in military, rescue, and sports personnel. The range of measurements include, heart rate, pulse wave form, respiratory rate, blood oxygen, tissue pCO2, exhaled carbon dioxide and physical activity. Other feasible measurements will employ miniature chemical laboratories on silicon or plastic chips. The measurements can be extended to clinical chemical assays ranging from common blood assays to protein or specialized protein measurements (e.g., troponin, creatine, and cytokines such as TNF and IL6). Though the feasibility of using wireless technology to communicate vital signs has been demonstrated 32 years ago (1) it has been only recently that practical and portable devices and communications net works have become generally available for inexpensive deployment of comfortable and affordable devices and systems.

When government recognizes intellectual property (IP) rights, it is often viewed as sanctioning the existence of private “monopolies,” in contrast to the general antimonopoly policy manifested in the antitrust laws. And yet, on occasion IP law itself condemns conduct on the part of IP owners--or excuses otherwise infringing activity on the part of IP defendants--expressly for the purpose of promoting competition, even though antitrust law (if one were to apply it at all under analogous circumstances) would not find anticompetitive harm absent a more thorough analysis of whether the antitrust defendant possesses power over a well-defined market. Salient examples include the misuse doctrines in patent and copyright law; some applications of merger and fair use in copyright; and trademark law’s functionality doctrine. In this paper, I develop a theoretical explanation for this divergence between antitrust and IP. Specifically, I argue that in some limited contexts the expected social costs (including error costs) of ruling for IP defendants may be low in comparison with the expected anticompetitive harm from ruling for IP plaintiffs. As a result, it sometimes may be welfare-enhancing for IP courts to be less concerned than antitrust courts about the expected costs of “false positives,” that is, cases wrongly decided against the party defending the allegedly anticompetitive conduct. I further contend, however, that such cases probably are more common in the copyright than in the patent law context, and that even in copyright contexts courts should be cautious about casually inferring anticompetitive harm; but that the analysis provides a rationale for a relatively expansive definition of trademark functionality.

This research has two objectives. The first objective is to explore the use of the modeling tool called "latent structural equations" (structural equations with latent variables) in the general field of travel behavior analysis and the more specific field of dynamic analysis of travel behavior. The second objective is to apply a latent structural equation model in order to determine the causal relationships between income, car ownership, and mobility. 

Many transportation researchers might be unfamiliar with latent structural equation modeling, which is also known as "latent structural analysis," "causal analysis," and "soft modeling." However, most researchers will be quite familiar with techniques that are special cases of latent structural equations: e.g., conventional multiple regression and simultaneous equations, path analysis, and (confirmatory) factor analysis. Furthermore, recent advances in estimation techniques have made it possible to incorporate discrete choice variables and other non-normal variables in structural equations models. Thus, probit choice models (binomial, ordered, and multinomial) can be incorporated within the general model framework. 

The empirical analysis reported here involves dynamic travel demand data from the Dutch National Mobility Panel for the three years 1984 through 1986. All variables in the model, with the exception of income level in the first year, are endogenous: income is treated as an ordinal (four category) variable; car ownership is treated as either an ordinal (ordered probit) or a categorical (multinomial probit) choice variable; and mobility, in terms of car trips and public transport trips, is treated as two censored (tobit) continuous variables. The model fits the data well, but only scratches the surface of the potential of latent structural equation modeling with panel data. Some possible extensions are outlined.