Modeling sea level rise scenarios and their effects on smart mobility infrastructure using GIS

• The model developed using GIS is applicable for simulating sea level rise impacts on coastal areas. • Elevation data with 11 m spatial resolution provides more precise results compared to 30 m resolution. • Yas Island consistently emerges as the most vulnerable to sea level rise across all resolution levels. • Roads and drop-off stations experience significant impacts starting at the 4-meter sea level rise scenario. As climate change progresses, monitoring the impact of rising sea levels has become a critical issue for coastal communities. This is particularly relevant for autonomous vehicles (AVs), which depend on favorable weather conditions to function optimally. This challenge is significant in the Middle East, where events like the flash floods in 2023 in the UAE have occurred. In response, TXAI launched an AV taxi service operating in a specific touristic area of Abu Dhabi. In this study, a storm surge model is applied to four sea level rise scenarios in ArcGIS Pro to assess their potential future impact on the TXAI deployment area. Two elevation data at 11 and 30 m of spatial resolutions are compared, using the same methodology, to observe the impact of the data accuracy on the results. By comparing them, the study explores the necessity of using higher resolution data for this type of analysis. Statistics are then computed to evaluate the significance of the results and assess the potential impact of sea level rise scenarios on the road network and drop off stations. Findings indicate that the transportation network would be moderately impacted under the first three scenarios, while a 4-meter sea level rise scenario would severely affect key areas, including drop-off stations. These findings highlight the importance of conducting such studies to develop effective adaptive strategies by stakeholders and policymakers. They also contribute to the broader scientific community’s understanding of the complex interplay between climate change, urban infrastructure, and autonomous vehicle systems.