Evaluating impacts of coastal flooding on the transportation system using an activity-based travel demand model: a case study in Miami-Dade County, FL

Recent climatic disasters have shown the vulnerability of transportation infrastructures against natural hazards. To understand the risk of coastal hazards on urban travel activities, this study presents an activity-based modeling approach to evaluate the impacts of storm surge on the transportation network under sea-level rise in Miami-Dade County, FL. A Markov-Chain Monte Carlo (MCMC) based algorithm is applied to generate population attributes and travel diaries in the model simulation. Flooding scenarios in 2045 are developed based on different adaptation standards under the 100-year storm surge and population projections are from the land-use conflict identification strategy (LUCIS) model. Our analysis indicates that about 29.3% of the transportation infrastructure, including areas of the US No. 1 highway, roadways in the south and southwest of the county, and bridges connecting Miami Beach area, will be damaged under the storm surge when a low-level adaptation standard is chosen. However, the high-level adaptation standard will reduce the vulnerable infrastructures to 12.4%. Furthermore, the total increased travel time of the low-level adaptation standard could be as high as twice of that in the high-level adaptation standard during peak morning hours. Our model results also reveal that the average increased travel time due to future storm surge damage ranges between 14.2 and 62.8 min per trip.

Automated summary

This study evaluates the vulnerability of transportation networks in Miami-Dade County to storm surges under sea-level rise using an activity-based modeling approach. Results show that choosing a low-level adaptation standard could lead to about 29.3% of transportation infrastructure being damaged, while a high-level adaptation standard reduces this to 12.4%. The total increased travel time under the low-level adaptation standard could be twice as high as that under the high-level adaptation standard.