Desheng Zhang is a Computer Science Ph.D candidate at the University of Minnesota. His research is uniquely built upon 10TB urban data from 10 kinds of urban systems, including cellphone, smartcard, taxi, bus, truck, subway, bike, personal vehicle, electric vehicle, and road networks in 7 cities across 3 continents with 30 million urban residents involved.
Desheng designs and implements large-scale data-driven models and real-world services to address urban sustainability challenges. Desheng has published more than 20 papers, featuring 10 first-author papers in premium Computer Science conferences, e.g., MobiCom, SenSys, IPSN, ICCPS, SIGSPATIAL, ICDCS, RTSS, and 6 best paper/thesis/poster awards.
For the first time ever, we have more people living in urban areas than rural areas. Based on this inevitable urbanization, my research aims to address sustainability challenges related to urban mobility (e.g., energy consumption and traffic congestion) by data-driven applications with a Cyber-Physical-Systems approach (CPS, also known as a broader term for Internet of Things), which is a new information paradigm integrating communication, computation and control in real time.
In this talk, I will focus on CPS related to large-scale urban transportation systems, e.g., taxi, bus, subway, and smart payment systems, along with cellphone networks. I will first show how the mobility data from these systems can be collaboratively utilized to capture urban mobility in real time, which addresses overfitting issues of existing mobility models driven by single-source data. Then I will show how the captured real-time mobility can be used to design a practical service, i.e., mobility-driven ridesharing, to provide positive feedback to transportation systems themselves, e.g., reducing energy consumption and traffic congestion. Finally, I will present real-world impact of my research and some future work about transportation CPS.