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Designing vehicle-sharing networks

Graduate student Tianli Zhou works to make transportation systems more efficient.
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Graduate student Tianli Zhou (left) during a lab meeting. Zhou is studying how to make transportation systems more efficient, particularly through vehicle-sharing services.
Graduate student Tianli Zhou (left) during a lab meeting. Zhou is studying how to make transportation systems more efficient, particularly through vehicle-sharing services.
Photo: Alan Silfen

The proliferation of smartphones, vehicle-sharing apps, and traffic sensors has amounted to a wealth of data that can be used to provide insight for increasing the efficiency and sustainability of transportation networks.

Such data is particularly valuable to graduate students like Tianli Zhou, a PhD candidate in the Interdepartmental Program in Transportation in the Department of Civil and Environmental Engineering, who uses the information to design vehicle-sharing services.

“Car sharing became more popular in the last decade, so a lot of data has accumulated over the years,” Zhou says. “So the main questions are how do you help the practitioners of car-sharing services, and also the city planners, to design a better car-sharing system?"

With a background in industrial engineering, Zhou didn’t work on transportation systems until he was a junior at Tsinghua University in Beijing. There, Zhou worked closely with Professor Hai Jiang SM ’04, PhD ’06 to create an offline transportation itinerary planning app. Zhou received the Award for Exceptional Performance in Student Research Training Program at Tsinghua University for the app, and the app won second prize in the 2012 AutoNavi China Location Based Service Challenge. Zhou credits this experience with introducing him to the field of transportation.

“I think transportation is one of the most important topics in future urban contexts,” Zhou says. “Traffic congestion and the resulting air pollution are huge issues in many cities worldwide and I want to do something to mitigate this problem.”

For his master’s thesis at MIT — completed with Chancellor and Ford Professor of Engineering Cynthia Barnhart and Carolina Osorio, an associate professor of civil and environmental engineering — Zhou studied data from Hubway, the Boston area’s bicycle-sharing system, to see how bike sharing could be used to supplement the public transportation network and attract more individuals to use multiple modes of transportation for their trips or commutes.

Now, Zhou is working with Osorio and fellow graduate student Evan Fields, a PhD candidate in MIT’s Operations Research Center, to study data from the car-sharing company Zipcar. The project is funded by Ford.

For the project, Zipcar provided the researchers with “high resolution,” or extremely detailed, reservation data from Boston over a two-year period. Among the data was anonymized information on the location of preferred rental vehicles, reservation times, and the times users picked up and returned the vehicles.

Using this information, Zhou and Fields infer demand for vehicles and develop algorithms to inform best practices for car-sharing services and to make such services more convenient for users.

“Some researchers make assumptions about this type of data. We make a lot fewer assumptions and use the high resolution data to inform our work,” Zhou says. “We call this a ‘data-driven method,’ because we can use this data to make direct, evidence-based suggestions.”

Fields considers this data-driven method a highlight of the research project.

“This is a really fun project to work on because of the data we have from Zipcar; it’s so rich and complete. We have all of the reservation data from Boston for a two-year period, so we can see everything,” Fields says. “We can ask all types of questions like, ‘Do people like to use Zipcar on the weekends?’ or ‘Do people want long trips?’ We can look in the data and see the answers, and we know the data we get back is the truth. It is so rare to be able to write a query and see what happens for real.”

By creating and using a smart sampling strategy on the historical car reservation data provided by Zipcar, a simulator proposed by Fields can model the operation of the two-way car-sharing system. Such simulation can effectively replicate the real-life Zipcar fleet utilization rate and is used to infer true demand for the car-sharing service.

While most simulation methods proposed by previous studies do not scale-up to address car-sharing network design problems for large cities, Zhou proposed a new algorithm, with Fields’ high resolution data-driven simulator embedded, that allows the team to look at the greater Boston area. This algorithm produces suggestions based on the inferred demand, such as where Zipcar should locate its cars for the upcoming month.

“A large-scale analysis allows us to identify synergies with other mobility services provided throughout the city,” Osorio says. “In particular, we are currently investigating how car-sharing services can complement public transportation services to improve transportation accessibility across the city.”

The suggestions from the algorithm also have potential to both increase revenue for vehicle-sharing services and to make them more conveniently located for individual use. For example, by placing a certain number of cars in a specific area, with the preferred vehicles, and thus meeting user demand, individuals may be more likely to utilize these services, Zhou says.

“Helping Zipcar achieve their goals is helpful for everybody, particularly if Zipcar can help fill in gaps in the accessibility of a city, such as places where the T [subway system] doesn’t go,” Fields says. “If we can suggest where to locate the vehicles and simultaneously increase profit, that’s good for Zipcar. It keeps them around and incentivizes operations in Boston, but also provides transportation to the city.”

Zhou and Fields have submitted their Boston findings and algorithms to an academic journal, and have recently begun to apply the algorithm to similar Zipcar data from Manhattan.

Both researchers are advised by Osorio, whose research group's projects include traffic optimization, autonomous mobility, and vehicle sharing. Previous work in Osorio’s lab has focused on the sustainability benefits of optimizing traffic light monitoring; a 2015 study found that changing the timing of stoplights in urban areas could reduce greenhouse gas emissions.

In recent years, the group has developed models and algorithms to enable high-resolution mobility data, such as that of the car sharing project, to be used to optimize the design and the operations of mobility systems at the scale of full cities and metropolitan regions.

“Vehicle sharing is a way to enhance the sustainability of our transportation system. People don’t have to have their own vehicles, including both bikes and cars. Also, in the current car-sharing industry, there tend to be more compact cars, so it’s better for the environment,” Zhou says. “These aspects of my project makes me feel that I can have an impact on society, and that’s what interested me in this kind of research.”

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