Towards Sustainable Mobility Systems with Big Data Mining

Climate change and population growth pose unprecedented challenges to urban systems. Meanwhile, the proliferation of crowdsensing techniques, such as mobile phones, vehicles, and cameras, has generated vast spatiotemporal data for understanding human activities and their interaction with the urban environment. In this talk, I will first delve into my research on using raw location data collected from millions of mobile phones to estimate and forecast individual human mobility patterns. Building on this foundation, I will demonstrate how it can support mobility decarbonization. Specifically, I will introduce a scalable framework that integrates ubiquitous, multi-structured mobility data to estimate citywide on-road vehicle emissions with high spatiotemporal resolution, followed by an evaluation of various city-scale decarbonization strategies. Lastly, I will highlight opportunities for multidisciplinary collaboration in areas such as energy demand estimation, event response, and environmental exposure to drive broader, more lasting impacts.

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