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Chenfeng Xiong, PhD - Villanova University. Villanova, PA, US

Chenfeng Xiong, PhD

Assistant Professor, Civil and Environmental Engineering | Villanova University


Dr. Xiong conducts AI and big-data research on human mobility, and its relations with policies, environment, & public health.


Areas of Expertise (7)

Transportation Engineering

Transportation Planning

Transportation Economics

Transportation and Environment

Big Data

Human Mobility

Electric Vehicle and Hydrogen Vehicle


Dr. Chenfeng Xiong is an Assistant Professor in the Department of Civil and Environmental Engineering at Villanova University. He specializes in Transportation Engineering and Planning. Dr. Xiong’s research is focused on transportation systems analysis using transportation big-data sources and computing techniques, and advanced AI and Machine Learning algorithms and applications of such analysis on studies of human mobility, transportation policies, environmental impacts, and public health. He has published over 60 peer-reviewed journal papers. In addition to his outstanding research accomplishments and publications, Dr. Xiong has profound experience in research projects, successfully leading and/or coordinating numerous research projects funded by US DOE, Federal Highway Administration, Federal Transit Administration, NSF, NIH and Maryland DOT.

Affiliations (4)

  • American Society of Civil Engineers (ASCE) : Member, 2021 - Present
  • Transportation Research Board : Member, 2010 - Present
  • PLOS One : Academic Editor
  • Journal of Transportation Research Records : Handling Editor

Select Media Appearances (2)

How the Baltimore Bridge Collapse Could Affect Philadelphia’s Port and Your Commute

The Philadelphia Inquirer  online


Just two tunnels remain for interstate traffic through Baltimore and its moatlike harbor to the Chesapeake Bay and south, said Chenfeng Xiong, an assistant engineering professor at Villanova University specializing in transportation. ”They’ll be swamped,” Xiong said, speaking of the Fort McHenry Tunnel, which carries I-95 traffic, and the Baltimore Harbor Tunnel, which carries I-895 traffic. They can be clogged even in normal conditions. ”There’s going to be death-grip congestion there,” he said, delaying travel to and from Philadelphia, increasing costs for the trucks that supply the region and service its massive warehouses.

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Ocean City reopened, and crowds came. Now experts warn coronavirus outbreaks could follow

Delmarva Now  online


Ocean City welcomed about 456,000 visitors over the course of Memorial Day weekend, Xiong said. On Saturday and Sunday alone, 122,000 people came from areas outside Washington D.C., Maryland and Virginia. “Based on my research I’m quite concerned about Ocean City,” Xiong said. “I found a growing correlation between external travel and confirmed cases of COVID-19 in places that have reopened in the United States.”

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Research Grants (2)

Enhancing Mobility Innovation: A Software-Based Emissions and Equity Credits for Public Transportation System

Federal Transit Administration $1,000,000


Integrating Human Mobility Analysis with Epidemics Dynamics Modeling for Pandemic Tracking, Prediction, and Prevention

NIH $1,100,000


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Select Academic Articles (5)

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


2020 Accurately estimating human mobility and gauging its relationship with virus transmission is critical for the control of COVID-19 spreading. Using mobile device location data of over 100 million monthly active samples, we compute origin–destination travel demand and aggregate mobility inflow at each US county from March 1 to June 9, 2020. Then, we quantify the change of mobility inflow across the nation and statistically model the time-varying relationship between inflow and the infections. We find 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. Moreover, our simultaneous equations analysis highlights the dynamics in a positive relationship between mobility inflow and the number of infections during the COVID-19 onset. This relationship is found to be increasingly stronger in partially reopened regions. Our study provides a quick reference and timely data availability for researchers and decision makers to understand the national mobility trends before and during the pandemic. The modeling results can be used to predict mobility and transmissions risks and integrated with epidemics models to further assess the public health outcomes.

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A big-data driven approach to analyzing and modeling human mobility trend under non-pharmaceutical interventions during COVID-19 pandemic

Transportation Research Part C: Emerging Technologies

2021 During the unprecedented coronavirus disease 2019 (COVID-19) challenge, non-pharmaceutical interventions became a widely adopted strategy to limit physical movements and interactions to mitigate virus transmissions. For situational awareness and decision-support, quickly available yet accurate big-data analytics about human mobility and social distancing is invaluable to agencies and decision-makers. This paper presents 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. Using mobile device location data of over 150 million monthly active samples in the United States (U.S.), the study successfully measures human mobility with three main metrics at the county level: daily average number of trips per person; daily average person-miles traveled; and daily percentage of residents staying home. A set of generalized additive mixed models is employed to disentangle the policy effect on human mobility from other confounding effects including virus effect, socio-demographic effect, weather effect, industry effect, and spatiotemporal autocorrelation. Results reveal the policy plays a limited, time-decreasing, and region-specific effect on human movement. The stay-at-home orders only contribute to a 3.5%-7.9% decrease in human mobility, while the reopening guidelines lead to a 1.6%-5.2% mobility increase. Results also indicate a reasonable spatial heterogeneity among the U.S. counties, wherein the number of confirmed COVID-19 cases, income levels, industry structure, age and racial distribution play important roles. The data informatics generated by the framework are made available to the public for a timely understanding of mobility trends and policy effects, as well as for time-sensitive decision support to further contain the spread of the virus.

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Mobile device location data reveal human mobility response to state-level stay-athome orders during the COVID-19 pandemic in the USA

Journal of The Royal Society Interface

One approach to delaying the spread of the novel coronavirus (COVID-19) is to reduce human travel by imposing travel restriction policies. Understanding the actual human mobility response to such policies remains a challenge owing to the lack of an observed and large scale dataset describing human mobility during the pandemic. This study uses an integrated dataset, consisting of anonymized and privacy-protected location data from over 150 million monthly active samples in the USA, COVID-19 case data and census population information, to uncover mobility changes during COVID-19 and under the stay-at-home state orders in the USA. The study successfully quantifies human mobility responses with three important metrics: daily average number of trips per person; daily average personmiles travelled; and daily percentage of residents staying at home. The data analytics reveal a spontaneous mobility reduction that occurred regardless of government actions and a ‘floor’ phenomenon, where human mobility reached a lower bound and stopped decreasing soon after each state announced the stay-at-home order. A set of longitudinal models is then developed and confirms that the states’ stay-at-home policies have only led to about a 5% reduction in average daily human mobility. Lessons learned from the data analytics and longitudinal models offer valuable insights for government actions in preparation for another COVID-19 surge or another virus outbreak in the future.

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Quantifying human mobility behaviour changes during the COVID-19 outbreak in the United States

Scientific Reports

2020 Since the first case of the novel coronavirus disease (COVID-19) was confirmed in Wuhan, China, social distancing has been promoted worldwide, including in the United States, as a major community mitigation strategy. However, our understanding remains limited in how people would react to such control measures, as well as how people would resume their normal behaviours when those orders were relaxed. We utilize an integrated dataset of real-time mobile device location data involving 100 million devices in the contiguous United States (plus Alaska and Hawaii) from February 2, 2020 to May 30, 2020. Built upon the common human mobility metrics, we construct a Social Distancing Index (SDI) to evaluate people’s mobility pattern changes along with the spread of COVID-19 at different geographic levels. We find that both government orders and local outbreak severity significantly contribute to the strength of social distancing. As people tend to practice less social distancing immediately after they observe a sign of local mitigation, we identify several states and counties with higher risks of continuous community transmission and a second outbreak. Our proposed index could help policymakers and researchers monitor people’s real-time mobility behaviours, understand the influence of government orders, and evaluate the risk of local outbreaks.

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Examining spatiotemporal changing patterns of bike-sharing usage during COVID-19 pandemic

Journal of Transport Geography

2021 The COVID-19 pandemic has led to a globally unprecedented change in human mobility. Leveraging two-year bike-sharing trips from the largest bike-sharing program in Chicago, this study examines the spatiotemporal evolution of bike-sharing usage across the pandemic and compares it with other modes of transport. A set of generalized additive (mixed) models are fitted to identify relationships and delineate nonlinear temporal interactions between station-level daily bike-sharing usage and various independent variables including socio-demographics, land use, transportation features, station characteristics, and COVID-19 infections. Results show: 1) the proportion of commuting trips is substantially lower during the pandemic; 2) the trend of bike-sharing usage follows an “increase-decrease-rebound” pattern; 3) bike-sharing presents as a more resilient option compared with transit, driving, and walking; 4) regions with more white, Asian, and fewer African-American residents are found to become less dependent on bike-sharing; 5) open space and residential areas exhibit less decrease and earlier start-to-recover time; 6) stations near the city center, with more docks, or located in high-income areas go from more increase before the pandemic to more decrease during the pandemic. Findings provide a timely understanding of bike-sharing usage changes and offer suggestions on how different stakeholders should respond to this unprecedented crisis.

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