Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/2580
Title: A stochastic diagnostic model for subway stations
Authors: Semaan, Nabil 
Zayed, Tarek
Affiliations: Department of Civil and Environmental Engineering 
Keywords: Stochastic modeling
Monte Carlo Simulation
Subjects: Subway stations
Performance
Issue Date: 2010
Part of: Tunnelling and underground space technology
Volume: 25
Issue: 1
Start page: 32
End page: 41
Abstract: 
Performance of subway stations is a critical problem that faces public transit authorities worldwide. Although replacing subway stations is very expensive, the Société de Transport de Montreal (STM) and most transit authorities lack planning strategies because they do not have deterioration models for their infrastructure. The presented research in this paper assists in developing a stochastic Global Station Diagnosis Model (GSDM). The GSDM identifies and evaluates the weights of different functional condition criteria for subway stations. It also utilizes the Preference Ranking Organization Method of Enrichment Evaluation (PROMETHEE) integrated with the Multi-Attribute Utility Theory (MAUT) and Monte Carlo simulation in order to determine a stochastic Global Diagnosis Index (GDI). Data were collected from experts through questionnaires and interviews. A case study of subway stations from the STM network is selected to implement the designed model. Results show that the GDI for the case study stations ranges from 5.6 to 7.8 with a 95% probability. Performing sensitivity analysis, the 'Alarm and Security criterion is found to be the most effective criterion on the GDI. This research is relevant to industry practitioners and researchers since it provides a stochastic diagnostic tool for subway stations.
URI: https://scholarhub.balamand.edu.lb/handle/uob/2580
DOI: 10.1016/j.tust.2009.08.002
Ezproxy URL: Link to full text
Type: Journal Article
Appears in Collections:Department of Civil and Environmental Engineering

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