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Interview With an Expert


This month, we sat down with Dr. Chenfeng Xiong, a civil engineer working as an Assistant Professor at Villanova University and conducting infrastructural research for the federal government. He explained how his research can assist in optimizing gateway ports, improving internal intermodal transportation, and even measuring and limiting carbon emissions.

What’s the scope of transportation research going on at Villanova University?

Since it’s a multidisciplinary topic, we have so many different people from so many different departments touching the intersection of infrastructure and transportation. In the Department of Civil Engineering, we have researchers looking directly into infrastructure, pavements and materials, and how engineering developments impact the environment. Outside of that, we have experts in psychology looking at driving behavior, experts in public health investigating the impact infrastructure can have on wellbeing, and data scientists come in as well to help manage all of the information. It’s really a growing community on our campus.

What has your research been focused on?

I’m working on all sorts of transportation problems. My background is really in data—I work on Big Data, multidisciplinary and multidimensional data—I draw evidence from smart phones, traffic cameras, road sensors, and surveys to learn about multimodal passenger and freight transportation patterns. In my previous research with the University of Maryland at College Park, our passive data collection and our analysis of that data into traffic movement was the only submission accepted and adopted by the US Department of Transportation.

Can you expand on some of that traffic analysis and what your team was looking for?

Between features of modern vehicles, expanded tracking in trucking and distribution, and the wide variety of navigation apps and services, there are many location pins to work from. On a given day, we could study how many people are travelling from New York to D.C., or we could investigate long distance freight travel and see how many trucks are travelling between Dallas Fort Worth and Houston. Just one location pin isn’t helpful but aggregating everything into a national database lets us make projections and really understand the transportation landscape.

How can we practically apply that investigation to trucking operations and the overall supply chain?

We can use that information to learn from the past. We can measure truck volumes and tonnage of cargoes moving between any origin and destination pair in the US and beyond to discover the current status quo. At the same time, we can use that information—since it’s continuous and seamlessly collected in near real-time conditions—to determine a lot of what-if questions. As a simple example, we can study how volumes reacted during the pandemic, what happens when people are working from home and relying on e-commerce instead of brick-and-mortar retailers, and how many trucks are on the road under those conditions. Eventually, we can use that information to help make predictions.

What is one outside-the-box example of transportation research you’ve come across since entering the space?

One of my colleagues at the University of Maryland was studying ways to solve maritime routing issues. She developed a specific focus on travelling through the North Pole when glaciers and other ice structures were melted enough to allow it. She concluded that, for certain origin and destination port pairings, carriers could burn less fuel and save time by using those routes.

Is there any one conclusion you’ve found from your research or that you’ve learned while studying this sector so far that you’d like to highlight?

In 2021, we saw some of the lowest levels of traffic congestion ever around the world. Based on our data, we realized there was only a ten percent decrease in overall traffic from before the pandemic. That ten percent decrease caused a fifty to sixty percent reduction in congestion. That’s because congestion trends are non-linear. If you look at a place like Washington, D.C. or New York City, and you were to decrease the number of cars on the road by just one percent, there would be a much larger benefit to overall traffic congestion.

How can that impact our industry?

That same principle can apply to the port landscape, as well. If we use our data models to identify the most congested ports and create ways to reduce demand or operational difficulty in those places, then we would see a maximum benefit. That kind of investigation can also help optimize the allocation of government funding and let us get the most bang for our buck out of that support. Obviously, this kind of measurement and infrastructural improvement can take time, but it eventually it has the potential to pay huge dividends for the transportation industry.


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