Chenfeng Xiong
University of Maryland, College Park
99 Papers
316 Citations
Chenfeng Xiong is an academic researcher from University of Maryland, College Park. The author has contributed to research in topics: Travel behavior & Computer science. The author has an hindex of 21, co-authored 83 publications. Previous affiliations of Chenfeng Xiong include University of Maryland, Baltimore.
Chat about Author
Papers
Mobile device data reveal the dynamics in a positive relationship between human mobility and COVID-19 infections.
TL;DR: It is found that external travel to other counties decreased by 35% soon after the nation entered the emergency situation, but recovered rapidly during the partial reopening phase, and the dynamics in a positive relationship between mobility inflow and the number of infections during the COVID-19 onset are highlighted.
331
Joint optimization of vehicle trajectories and intersection controllers with connected automated vehicles: Combined dynamic programming and shooting heuristic approach
TL;DR: An efficient DP-SH (dynamic programming with shooting heuristic as a subroutine) algorithm for the integrated optimization problem that can simultaneously optimize the trajectories of CAVs and intersection controllers is proposed and a two-step approach is developed to effectively obtain near-optimal intersection and trajectory control plans.
237
Human mobility trends during the early stage of the COVID-19 pandemic in the United States.
TL;DR: The study suggests that the public mobility trends conform with the government message urging to stay home, and offers integrated perspectives and serves as evidence to raise public awareness and reinforce the importance of social distancing while assisting policymakers.
Examining spatiotemporal changing patterns of bike-sharing usage during COVID-19 pandemic
TL;DR: In this article, the authors examined the spatiotemporal evolution of bike-sharing usage across the COVID-19 pandemic and compared it with other modes of transport, finding that the proportion of commuting trips is substantially lower during the pandemic.
154
A big-data driven approach to analyzing and modeling human mobility trend under non-pharmaceutical interventions during COVID-19 pandemic.
TL;DR: In this article, the authors present a big-data-driven analytical framework that ingests terabytes of data on a daily basis and quantitatively assesses the human mobility trend during COVID-19.
154