As a futuristic transportation system, personal mobility service has experienced rapid increases in service users. Particularly in Seoul, large users naturally led to more traffic accidents. However, relatively few literatures analyzed the traffic accidents because of limited data availability and relatively short service time. This study investigates personal mobility traffic accidents in Seoul in urban socio-environmental perspectives. Negative binomial regression with Eigenvector spatial filtering model is applied to handle spatial autocorrelation in the traffic accident data. Several models are applied, and the best model is derived and analyzed. This study contributes to literature as understanding Seoul’s urban population, transportation, and social index in relation with the personal mobility traffic accidents.