Prof. Songhua HU
Institute Massachusetts Institute of Technology Date & Time Date: 21 Nov. 2024 Time: 11:00-12:00 Zoom ID: 898 654 4121 Passcode: 123456
Climate change and population growth pose unprecedented challenges to the transportation system. Meanwhile, the proliferation of crowdsensing techniques, such as mobile phones, vehicles, social media, and cameras, has generated vast spatiotemporal data for understanding human activities and their interaction with the external environment. Effectively managing such massive, multi-structured spatiotemporal data, extracting valuable travel information, and tailoring solutions to various transportation issues are more crucial than ever. In this talk, I will first delve into my previous research on using raw location-based service data from ~150 million mobile phones across the US to build an end-to-end data-driven travel demand model. I will introduce a set of customized spatiotemporal artificial intelligence (AI) frameworks designed for forecasting travel patterns in citywide and broader contexts. These frameworks are embedded into classical travel demand models, functioning at both individual and aggregated levels under both recurrent and non-recurrent conditions. Expanding on this foundational framework and incorporating additional crowdsourced data, I will then discuss various time-sensitive and multidisciplinary collaborations in areas such as transportation planning, emergency response, public health, social equity, and decarbonization, for broader and longer-lasting impacts.
Dr. Songhua Hu is a postdoctoral researcher at the MIT Senseable City Lab. He holds a Ph.D. degree in civil engineering (transportation) from the University of Maryland, College Park. His research centers on modeling human travel using digital footprints crowdsourced from cellphones, vehicles, social media, cameras, etc. Based on customized spatiotemporal AI, network science, cloud computing, and advanced statistics, he aims to monitor, learn, and forecast how each person (vehicle) moves in the city, and to understand how these movements interact with the mobility system, health system, urban environment, and the whole society. In all these topics, his work considers both recurrent and non-recurrent (pandemic, disaster, extreme weather, etc.) situations. His research has contributed to projects funded by NSF, NIH, USDOT, and USDOE, resulting in 28 journal papers published in PNAS, Transportation Research Part A/C/D, etc. He is a recipient of the 2023 University of Maryland CEE Best Doctoral Research Award and several best paper awards from international conferences.
来源：智能交通学域
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