Chenfeng Xiong, PhD
Assistant Professor, Civil and Environmental Engineering Villanova University
- Villanova PA
Dr. Xiong conducts AI and big-data research on human mobility, and its relations with policies, environment, & public health.
Social
Areas of Expertise
Biography
Affiliations
- 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
How the Baltimore Bridge Collapse Could Affect Philadelphia’s Port and Your Commute
The Philadelphia Inquirer online
2024-03-26
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.
Ocean City Reopened, and Crowds Came. Now Experts Warn Coronavirus Outbreaks Could Follow
Delmarva Now online
2020-06-05
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.”
Research Grants
Enhancing Mobility Innovation: A Software-Based Emissions and Equity Credits for Public Transportation System
Federal Transit Administration
2023-2025
Integrating Human Mobility Analysis with Epidemics Dynamics Modeling for Pandemic Tracking, Prediction, and Prevention
NIH
2022-2026
Select Academic Articles
Understanding factors influencing user engagement in incentive-based travel demand management program
Transportation Research Part A: Policy and PracticeSonghua Hu, Chenfeng Xiong, Ya Ji Eric, Xin Wu, Kailun Liu, Paul Schonfeld
2024
Modeling the Frequency of Pedestrian and Bicyclist Crashes at Intersections: Big Data-driven Evidence From Maryland
Transportation Research Record: Journal of the Transportation Research BoardJina Mahmoudi, Chenfeng Xiong, and Weiyu Luo
2023
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 Technologies2021
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.
Examining spatiotemporal changing patterns of bike-sharing usage during COVID-19 pandemic
Journal of Transport Geography2021
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.
Mobile device data reveal the dynamics in a positive relationship between human mobility and COVID-19 infections
PNAS2020
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.
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 Interface2020
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.
Quantifying human mobility behaviour changes during the COVID-19 outbreak in the United States
Scientific Reports2020
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.
