He doesn’t quite remember the exact moment he became fascinated by transportation, but what he does remember were the frequent trips with his father to San Francisco, the hustle and bustle of city life, trolleys with their clanging bells, the buses passing by.

“When I was a kid, my dad used to take me to San Francisco where he grew up, and we’d go around the city on the trains and buses, so maybe that was part of it,” said Matthew W. Bhagat-Conway, an assistant professor at the University of North Carolina at Chapel Hill. “I don’t really remember, but it’s something I have been interested in for a long time.”

Experiencing how humans and other entities interact in physical spaces followed him through post-graduate studies and into graduate school where he converted his fascination of cities and transportation into knowledge. He received an undergraduate degree in geography from the University of California at Santa Barbara and a master’s degree in the same area from Arizona State University (ASU). He then completed a doctoral degree at ASU in the School of Geographical Sciences and Urban Planning.

Bhagat-Conway specializes in analysis and forecasting of travel behavior, or how people make choices about how to get around. Within this specialization, he splits his time between applied research and methodological research.

“In my applied research, I use survey and sensor data to understand the decision-making processes and evaluate the implications of them for transportation outcomes,” he said. “For example, I am currently involved in a project collecting and using during-COVID survey data to evaluate how transportation might change post-COVID. We have found that many people expect to continue to telecommute post-COVID, potentially leading to decreased travel demand at peak hours.”

He is also analyzing roadway sensor data to find out if peak travel demand is spreading out now since more people are choosing to work flexible schedules. He says so far, it appears that they are.

As for methodological research, Bhagat-Conway is currently working on using high performance computing to improve the speed of estimating models that are commonly used in travel behavior analysis. Creating methodologies is something that is not new to him. Prior to graduate school, Bhagat-Conway worked in consulting developing algorithms to estimate the impacts of transit plans on the ability of people to access opportunities, such as jobs or parks. His other interests include zoning and land-use policy and how it affects transportation.

Bhagat-Conway is leading a STRIDE Project K5 titled A Better Understanding of Shopping Travel in the U.S. His project focuses on how shopping in the United States is changing as more shopping is done online rather than in-person.

“We’re not really sure what the environmental effects of this are,” he said. “On the one hand, delivery requires driving bigger and more polluting trucks around, but on the other hand, each truck can serve many homes. A lot of researchers have modeled the environmental impact of delivery services, but to get a full picture of whether this is an improvement or not, we also need to know the environmental effects of in-person shopping.”

Bhagat-Conway says that understanding this issue is complicated because a lot of shopping occurs on the way to or from the place of origin. He says that the way this is analyzed is usually to either count up the miles traveled between the previous location and the shopping location or compute round-trip distances from home. 

“Someone who has a 10-mile commute home from work and stops at the grocery store on the way might have actually traveled much less than 10 miles to go shopping,” he said. “If the store is actually directly on the way, that shopping trip might not have generated any additional travel.”

The STRIDE project is expected to generate a product that will help estimate how much people are traveling when they choose to go shopping. It will provide aggregates by region and by type of shopping. The product is also expected to assist policymakers in making decisions about delivery services. For example, whether to deliver through loading zones with a favorable tax treatment, or to focus instead on brick-and-mortar retail investments.