A Simulation Method for Rail Transit Sign Optimization

Urban rail transit is a quasioperational project and its net cash inflow can hardly cover the investment expenditure. It is essential to determine an acceptable amount of government subsidy to ensure the financial viability of the PPP projects, so as to encourage the entry of the private partner. The partners involved in PPPs have common interests but conflict regarding the value of government subsidy. Considering the uncertainty characteristic by PPPs and information incompleteness in the decision-making process, this study presents a methodology to calculate the equitable subsidy ratio favored by both participants. This study divides the decision process into two steps. First, this study constructs a financial model and introduces an acceptable range of subsidy ratio by using the Monte Carlo simulation method. Second, this study uses the bargaining game theory to determine a particular subsidy ratio under incomplete information. To verify the applicability of the presented model, the researchers invoke an illustrative example for model validation. This research provides a referential and operational method for the government and private sectors to make government subsidy decisions for quasioperational projects.

Download Full-text

Research on Simulation of Evacuation in the Deep Buried Station of Urban Rail Transit

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.505-506.712 ◽

2014 ◽

Vol 505-506 ◽

pp. 712-718 ◽

Cited By ~ 1

Author(s):

Dei Wei Li ◽

Fang Lin Liu ◽

Yue Xin Wang ◽

Wei Teng Zhou

Keyword(s):

Safety Management ◽

Simulation Method ◽

Urban Rail Transit ◽

Rail Transit ◽

Evacuation Time ◽

Urban Rail ◽

Bottle Neck ◽

Station Safety

For the depth of deep buried station, the safety of the station and passengers should be seriously taken into consideration. This paper analyses the features of deep buried station and the influence of these features to the station safety management. With simulation method, the maximum optimal passenger capacity and evacuation time could be calculated, and bottle-neck in the station design could be found. From the comparison of the outcome between deep buried station and normal station, deep buried stations have longer evacuation time and lower optimal passenger capacity, which should be paid special attention to in the operation.

Download Full-text

Power of Modified Brown-Forsythe and Mixed-Model Approaches in Split-Plot Designs

Methodology ◽

10.1027/1614-2241/a000124 ◽

2017 ◽

Vol 13 (1) ◽

pp. 9-22 ◽

Cited By ~ 1

Author(s):

Pablo Livacic-Rojas ◽

Guillermo Vallejo ◽

Paula Fernández ◽

Ellián Tuero-Herrero

Keyword(s):

Repeated Measures ◽

Statistical Power ◽

Mixed Model ◽

Covariance Structure ◽

Simulation Method ◽

Future Research ◽

Repeated Measures Design ◽

Fixed And Random Effects ◽

Split Plot ◽

High Level

Abstract. Low precision of the inferences of data analyzed with univariate or multivariate models of the Analysis of Variance (ANOVA) in repeated-measures design is associated to the absence of normality distribution of data, nonspherical covariance structures and free variation of the variance and covariance, the lack of knowledge of the error structure underlying the data, and the wrong choice of covariance structure from different selectors. In this study, levels of statistical power presented the Modified Brown Forsythe (MBF) and two procedures with the Mixed-Model Approaches (the Akaike’s Criterion, the Correctly Identified Model [CIM]) are compared. The data were analyzed using Monte Carlo simulation method with the statistical package SAS 9.2, a split-plot design, and considering six manipulated variables. The results show that the procedures exhibit high statistical power levels for within and interactional effects, and moderate and low levels for the between-groups effects under the different conditions analyzed. For the latter, only the Modified Brown Forsythe shows high level of power mainly for groups with 30 cases and Unstructured (UN) and Autoregressive Heterogeneity (ARH) matrices. For this reason, we recommend using this procedure since it exhibits higher levels of power for all effects and does not require a matrix type that underlies the structure of the data. Future research needs to be done in order to compare the power with corrected selectors using single-level and multilevel designs for fixed and random effects.

Download Full-text