A Two-Stage Investment Decision-Making Model for Urban Rail Transit Drainage Renovation

Climate change is the main cause of frequent extreme weather and natural disasters. Therefore, effective climate adaptation strategies for urban rail transit (URT) should be adopted to cope with extreme precipitation events (EPEs). This study proposes a decision-making model based on climate change...

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Bibliographic Details
Published inSystems (Basel) Vol. 11; no. 6; p. 280
Main Authors Wang, Tao, Liu, Bingsheng, Liu, Shimeng, Zhang, Kuan, Ma, Mingyue
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.06.2023
Subjects
China
Climate change
Decision analysis
Decision making
Disaster management
Disasters
Drainage
Empirical analysis
Extreme weather
Flooding
Floods
Geographic information systems
Global temperature changes
Infrastructure
Light rail transit
Natural disasters
optimal investment timing
optimal renovation sequence
Precipitation
Rain
Renovation & restoration
Runoff
Simulation
Soils
Urban rail
urban rail transit
China
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Summary:Climate change is the main cause of frequent extreme weather and natural disasters. Therefore, effective climate adaptation strategies for urban rail transit (URT) should be adopted to cope with extreme precipitation events (EPEs). This study proposes a decision-making model based on climate change for drainage renovation, which consists of an optimal renovation sequence model and an optimal investment timing model. This study analyzes the inundation risk of each station and its node importance in the URT network and then uses a multi-attribute decision analysis (MADA) to determine the optimal renovation sequence. This study also uses a real options pricing approach to calculate the value of an option in order to defer the renovation project and determine the optimal investment timing. Then, the Beijing Urban Rail Transit (BURT) is taken as an example to conduct an empirical analysis of the proposed model. Considering the uncertainty of climate change and the complexity of the URT network, the model can obtain the optimal renovation sequence and the investment timing of each station, which is expected to provide a decision-making tool for urban governments to formulate an optimal plan that strengthens the prevention of flooding disasters.
ISSN:2079-8954
2079-8954
DOI:10.3390/systems11060280