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Issue 5, Volume 4, May 2007
ISSN: 1109-9526
E-ISSN: 2224-2899







Title of the Paper:  Measuring Process Capability for Bivariate Non-Normal Process Using the Bivariate Burr Distribution


Authors: B. Abbasi, S. Ahmad, M. Abdollahian and P.Zeephongsekul

Abstract: As is well known, process capability analysis for more than one quality variables is a complicated and sometimes contentious area with several quality measures vying for recognition. When these variables exhibit non-normal characteristics, the situation becomes even more complex. The aim of this paper is to measure Process Capability Indices (PCIs) for bivariate non-normal process using the bivariate Burr distribution. The univariate Burr distribution has been shown to improve the accuracy of estimates of PCIs for univariate non-normal distributions (see for example, [7] and [16]). Here, we will estimate the PCIs of bivariate non-normal distributions using the bivariate Burr distribution. The process of obtaining these PCIs will be accomplished in a series of steps involving estimating the unknown parameters of the process using maximum likelihood estimation coupled with simulated annealing. Finally, the Proportion of Non- Conformance (PNC) obtained using this method will be compared with those obtained from variables distributed under the bivariate Beta, Weibull, Gamma and Weibull-Gamma distributions.

Keywords: Process Capability Index (PCI), bivariate Burr distribution, simulated annealing algorithm, non-normaldistribution, multivariate processes.

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Title of the Paper: A Car-Following Model for Intelligent Transportation Systems Management


Authors: Hsun-Jung Cho, Yuh-Ting Wu

Abstract: Intelligent Transportation Systems (ITS) needs traffic flow models to provide real time traffic
information and to analyze traffic properties. This study proposes a new microscopic traffic flow model to describe car-following process and to represent certain traffic flow phenomena. Driver individual maximum speed is considered to enable the model to reflect the external environment and driver characteristics. The proposed model can explain why speeds and spacing differ among drivers even when the driving conditions are identical. Illustrative simulations are presented. The simulation results indicate that the proposed model is explainable, and it can represent equilibrium and disequilibrium states of microscopic and macroscopic traffic, such as: stable traffic, unstable traffic, equilibrium speed-flow relationship, closing-in, shying-away, capacity drop, and traffic hysteresis. 

Keywords: Individual maximum speed; Traffic phenomena; Car-following; Driver characteristic; Equilibrium state; Disequilibrium state; Microscopic traffic simulation. 

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