Those of us who’ve idled at an endless red light or navigated a gridlocked intersection might be glad that Carolina Osorio, assistant professor of civil and environmental engineering, is researching ways to ease rush hour. She works with transportation agencies and private transportation stakeholders to optimize their planning and operations decisions, such as better-timed traffic lights and strategic placement of vehicle-sharing stations. The goal? Less congestion, more reliable travel times, more efficient fuel consumption, and fewer emissions—beneficial for the environment and a blessing for harried commuters.
Agencies and companies share their traffic data with Osorio; from it, she designs simulation-based optimization algorithms that address their particular challenges, like bottlenecks at centrally located intersections or extreme congestion during rush hour. These algorithms combine principles of probability theory, simulation, simulation-based optimization, statistics, traffic control, and traffic flow theory.
“Transportation agencies and transportation consultants often use traffic models, known as traffic simulators, of an urban area of interest to inform their planning and operations decisions, and together we define the optimization problem to be addressed,” she explains. “These simulators describe the network supply—such as infrastructure—and the network demand for a given time of day. For instance, they’ll determine, on a weekday morning, the expected number of trips that will originate at location A and terminate at location B.”
Her work is uniquely fine-tuned and predictive. She analyzes cities’ historical data on traffic flow in key areas but also considers detailed driver behavior, such as route decisions, based on potential changes like signal-light timing. After all, a better-timed traffic light might lure more drivers to the intersection. This way, she can get a wider view of traffic’s domino effect. The goal: improving congestion not just at one intersection but potentially citywide. Her research might change the timing of a red light at a crucial intersection, which is helpful for commuters at that particular spot, but it also assesses how drivers might behave based on that enhancement—helpful for managing flow across an urban area.
Using this broader, balanced technique, she’s working with the New York City Department of Transportation to develop a traffic-management strategy to mitigate bottlenecks that affect flow on the Queensboro Bridge. In a recent simulation case study based in Lausanne, Switzerland, Osorio optimized traffic signals in the city center to reduce commuters’ average travel time during rush hour by 34%. These ideas for large-scale optimization garnered her an Early Career Award from the National Science Foundation.
Osorio’s next frontier is vehicle sharing, helmed by companies like Zipcar. She’s currently collaborating with them to determine where to place car-sharing stations, enhancing a city’s preexisting infrastructure. She’s also exploring technology that explains how autonomous cars—consider them vehicles of the future—will interact with our infrastructure, and how that may impact congestion patterns, fuel efficiency, and emissions. It’s an exciting time in the field: “Car manufacturers are no longer solely focused on selling cars; rather they want to provide urban mobility services,” she says.
So does Osorio. She says MIT is a prime place to conduct research that actually benefits everyday citizens. “MIT is always dedicated to making a practical, tangible impact—in my case, toward making cities more livable. This drives me every day,” she says.
Well, actually, she doesn’t drive. The Harvard Square resident usually walks or bikes to work. Maybe someday.
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