Driver Responses to Graphic-Aided Portable Changeable Message Signs in Highway Work Zones

Portable changeable message signs (PCMSs) have been employed in highway work zones as temporary traffic control devices. Various studies showed that adding graphics to PCMS messages can provide advantages to traditional text messages, such as increasing legibility and improving the understanding of elderly drivers. This article synthesizes the findings of a two-phase research project aimed to investigate driver responses to graphic-aided PCMSs. Different text and graphic-aided PCMSs representing roadwork and flagger were set up in the upstream of highway work zones, and speed data of more than 2,700 vehicles were collected with a series of five speed sensors to determine vehicle speed reduction. Nearly 1,000 onsite driver surveys were performed to identify driver preference on the added graphics. The results discovered that graphic-aided PCMSs reduced mean vehicle speed between 13% and 17% and reduced the speed of passenger cars and trucks significantly differently depending on their locations in work zone. The results indicated that all drivers correctly interpreted the flagger graphic and two work-zone graphics, and suggested that 52% to 71% of drivers preferred to see graphics in PCMS messages. The findings also revealed that driver age did not have a significant impact on driver preference on PCMS message format

Evaluating Value-at-Risk Models via Quantile Regressions

We propose an alternative backtest to evaluate the performance of Value-at-Risk (VaR) models. The presented methodology allows us to directly test the performance of many competing VaR models, as well as identify periods of an increased risk exposure based on a quantile regression model (Koenker & Xiao, 2002). Quantile regressions provide us an appropriate environment to investigate VaR models, since they can naturally be viewed as a conditional quantile function of a given return series. A Monte Carlo simulation is presented, revealing that our proposed test might exhibit more power in comparison to other backtests presented in the literature. Finally, an empirical exercise is conducted for daily S&P500 return series in order to explore the practical relevance of our methodology by evaluating five competing VaRs through four different backtests.

Factors influencing adoption of agroforestry among smallholder farmers in Zambia

Agroforestry technologies have been extensively researched and introduced to smallholder farmers in Zambia for over two decades. Despite the research and extension effort over this period, not many farmers have adopted these technologies. The purpose of this paper is to determine why agroforestry technologies are not being taken up by examining factors that influence the adoption of agroforestry practices. Based on data obtained from 388 farming households, statistical analysis show an association between adoption of both improved fallows and biomass transfer technologies with knowledge of the technology, availability of seed, and having the appropriate skills. In addition some household characteristics are found to be linked to the incidence of adoption. However, the strength of association between these variables is low, giving an indication that there might be other factors at play limiting agroforestry adoption. It is anticipated that these findings will point to other areas beyond the household and community level that need further exploration in order to understand factors limiting agroforestry adoption.Agroforestry adoption, smallholder farmers, limitations to adoption, chi-square tests of independence analysis, Zambia, Agricultural and Food Policy, Community/Rural/Urban Development, Crop Production/Industries, Environmental Economics and Policy, Land Economics/Use,

AutoBayes: A System for Generating Data Analysis Programs from Statistical Models

Data analysis is an important scientific task which is required whenever information needs to be extracted from raw data. Statistical approaches to data analysis, which use methods from probability theory and numerical analysis, are well-founded but difficult to implement: the development of a statistical data analysis program for any given application is time-consuming and requires substantial knowledge and experience in several areas. In this paper, we describe AutoBayes, a program synthesis system for the generation of data analysis programs from statistical models. A statistical model specifies the properties for each problem variable (i.e., observation or parameter) and its dependencies in the form of a probability distribution. It is a fully declarative problem description, similar in spirit to a set of differential equations. From such a model, AutoBayes generates optimized and fully commented C/C++ code which can be linked dynamically into the Matlab and Octave environments. Code is produced by a schema-guided deductive synthesis process. A schema consists of a code template and applicability constraints which are checked against the model during synthesis using theorem proving technology. AutoBayes augments schema-guided synthesis by symbolic-algebraic computation and can thus derive closed-form solutions for many problems. It is well-suited for tasks like estimating best-fitting model parameters for the given data. Here, we describe AutoBayes's system architecture, in particular the schema-guided synthesis kernel. Its capabilities are illustrated by a number of advanced textbook examples and benchmarks

Supplement use in sport: is there a potentially dangerous incongruence between rationale and practice?

BACKGROUND: Supplement use by athletes is complex and research supports the alarming notion of misinformed decisions regarding supplements. HYPOTHESIS: A frequent divergence between the type of supplements chosen by athletes and the rationale dictating the supplement use is hypothesized. Thus, a potentially dangerous incongruence may exist between rationale and practice. TESTING THE HYPOTHESIS: In the continued absence of reliable data on supplement use, an alternative approach of studying the reasons underlying supplement use in athletes is proposed to determine whether there is an incongruence between rationale and practice. Existing data from large scale national surveys can be used to investigate this incongruence. IMPLICATIONS OF THE HYPOTHESIS: In this report, analyses of distinctive patterns between the use and rationale for use of supplements among athletes are recommended to explore this potentially dangerous phenomenon

Project management and its relation to long-term project success : an empirically based theoretical framework.

Companies implement effective project management to successfully operate in turbulent market cycles and ensure the success of their endeavors. Project management is indispensable for most industrial sectors and is employed in a variety of for-profit and non-profit organizations. It can be considered as a management method that contributes value to a variety of organizations. Many practitioners and researchers have attempted to identify the causes of project failure, the factors of project success, and the criteria to gauge this success. There has been little agreement on what constitutes project success. In response to the widespread debate surrounding project success, several lists dealing with factors related to project success have been published. The lack of agreement on the definition of project success renders the quest to identify the factors that contribute to successful project implementation moot. Without knowing what constitutes success, we cannot know what contributes to it. Practitioners are interested in recommendations for implementing project success factors and the corrective or preventative actions that should be taken if the project fails to meet one or more project success criteria. Project management and related research are, therefore facing severe criticism for not fulfilling their contributory expectations within the management discipline. The purpose of this research is to identify relationships between the project management body of knowledge and short- and long-term project success. The project management body of knowledge includes nine knowledge areas: integration, scope, time, cost, quality, communication, risk, human resources, and procurement management and five project management process groups (initiating, planning, executing, monitoring, controlling, and closing process groups) (PMBoK, 2004), while project success is related to budget/cost, schedule, customer satisfaction, user satisfaction, stakeholder satisfaction, project team satisfaction, strategic contribution of the project, financial objectives, technical objectives, performance objectives, commercial benefit for contractors, commercial benefit for customer, scope, personal growth, customer approval, profitability, and sales. This study is based on a self-conducted survey of 163 members of the Project Management Institute / German Chapter from October 8, 2013 to January 31st, 2014, who are project managers, project coordinators, or project team members. The business areas included in the survey are computers / information technology, construction, engineering, education, government, health care, manufacturing, software development, and telecommunications. Pearson chi-square tests and Fisher\u27s exact tests were performed to examine whether relationships exist between the project management body of knowledge and project success (short-term and long-term project success). The study revealed significant evidence of relationships between the outputs of the project management body of knowledge and short- and long-term project success. The study revealed also that project success depends on the project type, project size and project business area. The main contributions of this dissertation are: (a) an empirically based investigation of the relationship between outputs of the management processes and the project judgment criteria; (b) a closing of the existing gap in the literature regarding the link between factors that contribute to project success and ways to measure it (in previous studies project success criteria and success factors have been investigated in isolation); (c) a holistic analysis of the project management body of knowledge by providing an organized view of the outputs of each project management process that could influence short- and long-term project resulting outcomes; and (d) a framework for the analysis and improvement of project outcomes by using the theory of constraints

VAR, SVAR and SVEC Models: Implementation Within R Package vars

The structure of the package vars and its implementation of vector autoregressive, structural vector autoregressive and structural vector error correction models are explained in this paper. In addition to the three cornerstone functions VAR(), SVAR() and SVEC() for estimating such models, functions for diagnostic testing, estimation of a restricted models, prediction, causality analysis, impulse response analysis and forecast error variance decomposition are provided too. It is further possible to convert vector error correction models into their level VAR representation. The different methods and functions are elucidated by employing a macroeconomic data set for Canada. However, the focus in this writing is on the implementation part rather than the usage of the tools at hand.