Chenfeng Xiong, PhD

Assistant Professor, Civil and Environmental Engineering | Villanova University

Villanova, PA, UNITED STATES

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

Areas of Expertise (7)

Big Data

Electric Vehicle and Hydrogen Vehicle

Human Mobility

Transportation and Environment

Transportation Engineering

Transportation Economics

Transportation Planning

Biography

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

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 (2)

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

Federal Transit Administration $1,000,000

2023-2025

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

NIH $1,100,000

2022-2026

Select Academic Articles (7)

Understanding factors influencing user engagement in incentive-based travel demand management program

Transportation Research Part A: Policy and Practice

Songhua 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 Board

Jina 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 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.

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.

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

PNAS

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.

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

2020 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 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.