Rapid emergence of shared electric scooter (e-scooter) services has posed new challenges to road safety issues over the last few years as a serious worldwide public concern. Previous studies have investigated e-scooter accidents from multiple perspectives. However, research gaps still exist in understanding the role of infrastructure-related factors in e-scooter accidents, especially in the city of Zurich. The overarching aim of this thesis is to investigate and model the relationship between the characteristics of traffic infrastructure and electric scooter accidents. To address the lack of knowledge of electric scooter safety issues, a spatial-temporal analysis was first conducted for an overview of the pattern of accident distribution. Curb extraction was achieved by applying the Segment Anything Model to Google Street View images as a supplement to existing infrastructure data. A comprehensive dataset including curb variables, infrastructure entropy, and traffic transport was constructed. With random pseudo points being generated, a correlation between e-scooter accidents and traffic infrastructure was eventually determined by regression analysis. Results from this study indicate a strong correlation exists between the presence of e-scooter accidents and traffic infrastructure features such as speed limit and the presence of curbs. Significant variables related to the severity of electric scooter accidents were determined, including distance to road and curb width type. Although there are limitations in data size, coverage, quality, and approaches. Overall, this study offers insights into e-scooter safety, introduces an analysis process to extract infrastructure from street view images, and confirms the important influence of traffic infrastructure on e-scooter accidents
