Against the backdrop of accelerating global climate change and urbanization processes, urban transportation systems are confronting increasingly complex multi-hazard risks. Spatiotemporal big data, characterized by its high precision and information density, has demonstrated growing significance in transportation system resilience studies. Nevertheless, the current comprehension of the evolutionary trajectory of spatiotemporal big data applications in this domain remains fragmented. In this context, our study conducts a systematic review of global research, elucidating the practical implementations of spatiotemporal big data in transportation system resilience studies. The investigation reveals that multi-source big data with high spatiotemporal resolution has not only catalyzed methodological innovations in resilience assessment but has also potential to facilitate a paradigm shift in the field - transitioning from macro-scale to micro-scale analyses, from static evaluations to dynamic monitoring approaches, and from post-disaster emphasis to comprehensive lifecycle investigations. Journal of Geo-Information Science has published the study's results.