Chenfeng Xiong

Chenfeng Xiong

Assistant Professor of Transportation Engineering

Publications

📄 Publications

I have authored:

These works span a wide range of topics including:

The most cited and representative studies are listed below.
A complete publication list is available upon request.

📚 You can also explore my full portfolio on
my Google Scholar profile.

✍️ Selected Publications

Mobile device data reveal the dynamics in a positive relationship between human mobility and COVID-19 infections

Published in Proceedings of National Academy of Sciences (PNAS), 2020

U.S. county level human mobility were measured from individual level passively collected data and linked to a panel data model for modeling and predicting SARS-CoV-2 pandemic in the U.S.

Recommended citation: Xiong, et. al., (2020). "Mobile device data reveal the dynamics in a positive relationship between human mobility and COVID-19 infections." Proceedings of National Academy of Sciences (PNAS). 117 (44) 27087-27089. https://www.pnas.org/doi/full/10.1073/pnas.2010836117

An integrated and personalized traveler information and incentive scheme for energy efficient mobility systems

Published in Transportation Research Part C: Emerging Technologies, 2020

This paper studied an integrated incentivization mechanism that realisticly models subjects` behavioral response to different types of incentives and optimizes the incentive allocation with a control optimizer.

Recommended citation: Xiong et al., (2010). "An integrated and personalized traveler information and incentive scheme for energy efficient mobility systems." Transportation Research Part C: Emerging Technologies. 113, 57-73. https://www.sciencedirect.com/science/article/pii/S0968090X18317364

A high-order hidden Markov model and its applications for dynamic car ownership analysis

Published in Transportation Science, 2018

This paper extends the dynamically formulated hidden Markov models to a high-order hidden Markov model (HO-HMM) formulation. In the HO-HMM, the Markovian assumption that the future states (interpreted as the states of preferences or attitudes) depend only on the current state has been relaxed. Instead, the HO-HMM generalizes that the future states will depend on a number of states occurring beforehand.

Recommended citation: Xiong et. al., (2018). "A high-order hidden Markov model and its applications for dynamic car ownership analysis." Transportation Science. 52 (6), 1365-1375. https://pubsonline.informs.org/doi/abs/10.1287/trsc.2017.0792