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This paper provides a day-to-day analysis of the reliability of commuting time and trip scheduling under the Advanced Traveler Information System (ATIS). A simple network with parallel routes and bottleneck congestion is used to simulate the departure time and route choice decisions of commuters to minimize total travel time and scheduling delay cost. There are two major factors influencing the decisions of drivers in their departure time and route choices: their accumulated travel experience and information provided by ATIS. A simple experiment is carried for investigating trip-scheduling reliability of this network system.

The polynomial dimensional decomposition (PDD) method is a popular tool to establish a surrogate model in several scientific areas and engineering disciplines. The selection of appropriate truncated polynomials is the main topic in the PDD. In this paper, an easy-to-implement adaptive PDD method with a better balance between precision and efficiency is proposed.

First, the original random variables are transformed into corresponding independent reference variables according to the statistical information of variables. Second, the performance function is decomposed as a summation of component functions that can be approximated through a series of orthogonal polynomials. Third, the truncated maximum order of the orthogonal polynomial functions is determined through the nonlinear judgment method. The corresponding expansion coefficients are calculated through the point estimation method. Subsequently, the performance function is reconstructed through appropriate orthogonal polynomials and known expansion coefficients.

Several examples are investigated to illustrate the accuracy and efficiency of the proposed method compared with the other methods in reliability analysis.

The number of unknown coefficients is significantly reduced, and the computational burden for reliability analysis is eased accordingly. The coefficient evaluation for the multivariate component function is decoupled with the order judgment of the variable. The proposed method achieves a good trade-off of efficiency and accuracy for reliability analysis.

This study reviews the use of meta-analysis in accounting research. We categorize the meta-analytic research into five topics: financial reporting, auditing, corporate governance and accounting quality, management accounting, and miscellaneous topics. Further, we classify the studies by the meta-analysis technique employed: Hunter et al. (1982), Hunter and Schmidt (2000), Lipsey and Wilson (2001), and Stouffer’s approach. We identify 27 meta-analytical studies over the period 1985–2014 with financial reporting (auditing) topics representing seven (six) of these studies. Our review highlights that meta-analytic methods are being applied and accepted, more frequently, to answer complex questions concerning the moderating effects of country-level variables, such as national culture, economic conditions, and institutional characteristics, on various associations of interest.