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    The curse of variety in transportation systems

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    Cathy Wu has always delighted in systems that run smoothly. In high school, she designed a project to optimize the best route for getting to class on time. Her research interests and career track are evidence of a propensity for organizing and optimizing, coupled with a strong sense of responsibility to contribute to society instilled by her parents at a young age.

    As an undergraduate at MIT, Wu explored domains like agriculture, energy, and education, eventually homing in on transportation. “Transportation touches each of our lives,” she says. “Every day, we experience the inefficiencies and safety issues as well as the environmental harms associated with our transportation systems. I believe we can and should do better.”

    But doing so is complicated. Consider the long-standing issue of traffic systems control. Wu explains that it is not one problem, but more accurately a family of control problems impacted by variables like time of day, weather, and vehicle type — not to mention the types of sensing and communication technologies used to measure roadway information. Every differentiating factor introduces an exponentially larger set of control problems. There are thousands of control-problem variations and hundreds, if not thousands, of studies and papers dedicated to each problem. Wu refers to the sheer number of variations as the curse of variety — and it is hindering innovation.

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    “To prove that a new control strategy can be safely deployed on our streets can take years. As time lags, we lose opportunities to improve safety and equity while mitigating environmental impacts. Accelerating this process has huge potential,” says Wu.  

    Which is why she and her group in the MIT Laboratory for Information and Decision Systems are devising machine learning-based methods to solve not just a single control problem or a single optimization problem, but families of control and optimization problems at scale. “In our case, we’re examining emerging transportation problems that people have spent decades trying to solve with classical approaches. It seems to me that we need a different approach.”

    Optimizing intersections

    Currently, Wu’s largest research endeavor is called Project Greenwave. There are many sectors that directly contribute to climate change, but transportation is responsible for the largest share of greenhouse gas emissions — 29 percent, of which 81 percent is due to land transportation. And while much of the conversation around mitigating environmental impacts related to mobility is focused on electric vehicles (EVs), electrification has its drawbacks. EV fleet turnover is time-consuming (“on the order of decades,” says Wu), and limited global access to the technology presents a significant barrier to widespread adoption.

    Wu’s research, on the other hand, addresses traffic control problems by leveraging deep reinforcement learning. Specifically, she is looking at traffic intersections — and for good reason. In the United States alone, there are more than 300,000 signalized intersections where vehicles must stop or slow down before re-accelerating. And every re-acceleration burns fossil fuels and contributes to greenhouse gas emissions.

    Highlighting the magnitude of the issue, Wu says, “We have done preliminary analysis indicating that up to 15 percent of land transportation CO2 is wasted through energy spent idling and re-accelerating at intersections.”

    To date, she and her group have modeled 30,000 different intersections across 10 major metropolitan areas in the United States. That is 30,000 different configurations, roadway topologies (e.g., grade of road or elevation), different weather conditions, and variations in travel demand and fuel mix. Each intersection and its corresponding scenarios represents a unique multi-agent control problem.

    Wu and her team are devising techniques that can solve not just one, but a whole family of problems comprised of tens of thousands of scenarios. Put simply, the idea is to coordinate the timing of vehicles so they arrive at intersections when traffic lights are green, thereby eliminating the start, stop, re-accelerate conundrum. Along the way, they are building an ecosystem of tools, datasets, and methods to enable roadway interventions and impact assessments of strategies to significantly reduce carbon-intense urban driving.

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    Their collaborator on the project is the Utah Department of Transportation, which Wu says has played an essential role, in part by sharing data and practical knowledge that she and her group otherwise would not have been able to access publicly.

    “I appreciate industry and public sector collaborations,” says Wu. “When it comes to important societal problems, one really needs grounding with practitioners. One needs to be able to hear the perspectives in the field. My interactions with practitioners expand my horizons and help ground my research. You never know when you’ll hear the perspective that is the key to the solution, or perhaps the key to understanding the problem.”

    Finding the best routes

    In a similar vein, she and her research group are tackling large coordination problems. For example, vehicle routing. “Every day, delivery trucks route more than a hundred thousand packages for the city of Boston alone,” says Wu. Accomplishing the task requires, among other things, figuring out which trucks to use, which packages to deliver, and the order in which to deliver them as efficiently as possible. If and when the trucks are electrified, they will need to be charged, adding another wrinkle to the process and further complicating route optimization.

    The vehicle routing problem, and therefore the scope of Wu’s work, extends beyond truck routing for package delivery. Ride-hailing cars may need to pick up objects as well as drop them off; and what if delivery is done by bicycle or drone? In partnership with Amazon, for example, Wu and her team addressed routing and path planning for hundreds of robots (up to 800) in their warehouses.

    Every variation requires custom heuristics that are expensive and time-consuming to develop. Again, this is really a family of problems — each one complicated, time-consuming, and currently unsolved by classical techniques — and they are all variations of a central routing problem. The curse of variety meets operations and logistics.

    By combining classical approaches with modern deep-learning methods, Wu is looking for a way to automatically identify heuristics that can effectively solve all of these vehicle routing problems. So far, her approach has proved successful.

    “We’ve contributed hybrid learning approaches that take existing solution methods for small problems and incorporate them into our learning framework to scale and accelerate that existing solver for large problems. And we’re able to do this in a way that can automatically identify heuristics for specialized variations of the vehicle routing problem.” The next step, says Wu, is applying a similar approach to multi-agent robotics problems in automated warehouses.

    Wu and her group are making big strides, in part due to their dedication to use-inspired basic research. Rather than applying known methods or science to a problem, they develop new methods, new science, to address problems. The methods she and her team employ are necessitated by societal problems with practical implications. The inspiration for the approach? None other than Louis Pasteur, who described his research style in a now-famous article titled “Pasteur’s Quadrant.” Anthrax was decimating the sheep population, and Pasteur wanted to better understand why and what could be done about it. The tools of the time could not solve the problem, so he invented a new field, microbiology, not out of curiosity but out of necessity. More

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      in Environment

      3 Questions: What a single car can say about traffic

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      Vehicle traffic has long defied description. Once measured roughly through visual inspection and traffic cameras, new smartphone crowdsourcing tools are now quantifying traffic far more precisely. This popular method, however, also presents a problem: Accurate measurements require a lot of data and users.

      Meshkat Botshekan, an MIT PhD student in civil and environmental engineering and research assistant at the MIT Concrete Sustainability Hub, has sought to expand on crowdsourcing methods by looking into the physics of traffic. During his time as a doctoral candidate, he has helped develop Carbin, a smartphone-based roadway crowdsourcing tool created by MIT CSHub and the University of Massachusetts Dartmouth, and used its data to offer more insight into the physics of traffic — from the formation of traffic jams to the inference of traffic phase and driving behavior. Here, he explains how recent findings can allow smartphones to infer traffic properties from the measurements of a single vehicle.  

      Q: Numerous navigation apps already measure traffic. Why do we need alternatives?

      A: Traffic characteristics have always been tough to measure. In the past, visual inspection and cameras were used to produce traffic metrics. So, there’s no denying that today’s navigation tools apps offer a superior alternative. Yet even these modern tools have gaps.

      Chief among them is their dependence on spatially distributed user counts: Essentially, these apps tally up their users on road segments to estimate the density of traffic. While this approach may seem adequate, it is both vulnerable to manipulation, as demonstrated in some viral videos, and requires immense quantities of data for reliable estimates. Processing these data is so time- and resource-intensive that, despite their availability, they can’t be used to quantify traffic effectively across a whole road network. As a result, this immense quantity of traffic data isn’t actually optimal for traffic management.

      Q: How could new technologies improve how we measure traffic?

      A: New alternatives have the potential to offer two improvements over existing methods: First, they can extrapolate far more about traffic with far fewer data. Second, they can cost a fraction of the price while offering a far simpler method of data collection. Just like Waze and Google Maps, they rely on crowdsourcing data from users. Yet, they are grounded in the incorporation of high-level statistical physics into data analysis.

      For instance, the Carbin app, which we are developing in collaboration with UMass Dartmouth, applies principles of statistical physics to existing traffic models to entirely forgo the need for user counts. Instead, it can infer traffic density and driver behavior using the input of a smartphone mounted in single vehicle.

      The method at the heart of the app, which was published last fall in Physical Review E, treats vehicles like particles in a many-body system. Just as the behavior of a closed many-body system can be understood through observing the behavior of an individual particle relying on the ergodic theorem of statistical physics, we can characterize traffic through the fluctuations in speed and position of a single vehicle across a road. As a result, we can infer the behavior and density of traffic on a segment of a road.

      As far less data is required, this method is more rapid and makes data management more manageable. But most importantly, it also has the potential to make traffic data less expensive and accessible to those that need it.

      Q: Who are some of the parties that would benefit from new technologies?

      A: More accessible and sophisticated traffic data would benefit more than just drivers seeking smoother, faster routes. It would also enable state and city departments of transportation (DOTs) to make local and collective interventions that advance the critical transportation objectives of equity, safety, and sustainability.

      As a safety solution, new data collection technologies could pinpoint dangerous driving conditions on a much finer scale to inform improved traffic calming measures. And since socially vulnerable communities experience traffic violence disproportionately, these interventions would have the added benefit of addressing pressing equity concerns. 

      There would also be an environmental benefit. DOTs could mitigate vehicle emissions by identifying minute deviations in traffic flow. This would present them with more opportunities to mitigate the idling and congestion that generate excess fuel consumption.  

      As we’ve seen, these three challenges have become increasingly acute, especially in urban areas. Yet, the data needed to address them exists already — and is being gathered by smartphones and telematics devices all over the world. So, to ensure a safer, more sustainable road network, it will be crucial to incorporate these data collection methods into our decision-making. More

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      in Energy

      Crossing disciplines, adding fresh eyes to nuclear engineering

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      Sometimes patterns repeat in nature. Spirals appear in sunflowers and hurricanes. Branches occur in veins and lightning. Limiao Zhang, a doctoral student in MIT’s Department of Nuclear Science and Engineering, has found another similarity: between street traffic and boiling water, with implications for preventing nuclear meltdowns.

      Growing up in China, Zhang enjoyed watching her father repair things around the house. He couldn’t fulfill his dream of becoming an engineer, instead joining the police force, but Zhang did have that opportunity and studied mechanical engineering at Three Gorges University. Being one of four girls among about 50 boys in the major didn’t discourage her. “My father always told me girls can do anything,” she says. She graduated at the top of her class.

      In college, she and a team of classmates won a national engineering competition. They designed and built a model of a carousel powered by solar, hydroelectric, and pedal power. One judge asked how long the system could operate safely. “I didn’t have a perfect answer,” she recalls. She realized that engineering means designing products that not only function, but are resilient. So for her master’s degree, at Beihang University, she turned to industrial engineering and analyzed the reliability of critical infrastructure, in particular traffic networks.

      “Among all the critical infrastructures, nuclear power plants are quite special,” Zhang says. “Although one can provide very enormous carbon-free energy, once it fails, it can cause catastrophic results.” So she decided to switch fields again and study nuclear engineering. At the time she had no nuclear background, and hadn’t studied in the United States, but “I tried to step out of my comfort zone,” she says. “I just applied and MIT welcomed me.” Her supervisor, Matteo Bucci, and her classmates explained the basics of fission reactions as she adjusted to the new material, language, and environment. She doubted herself — “my friend told me, ‘I saw clouds above your head’” — but she passed her first-year courses and published her first paper soon afterward.

      Much of the work in Bucci’s lab deals with what’s called the boiling crisis. In many applications, such as nuclear plants and powerful computers, water cools things. When a hot surface boils water, bubbles cling to the surface before rising, but if too many form, they merge into a layer of vapor that insulates the surface. The heat has nowhere to go — a boiling crisis.

      Bucci invited Zhang into his lab in part because she saw a connection between traffic and heat transfer. The data plots of both phenomena look surprisingly similar. “The mathematical tools she had developed for the study of traffic jams were a completely different way of looking into our problem” Bucci says, “by using something which is intuitively not connected.”

      One can view bubbles as cars. The more there are, the more they interfere with each other. People studying boiling had focused on the physics of individual bubbles. Zhang instead uses statistical physics to analyze collective patterns of behavior. “She brings a different set of skills, a different set of knowledge, to our research,” says Guanyu Su, a postdoc in the lab. “That’s very refreshing.”

      In her first paper on the boiling crisis, published in Physical Review Letters, Zhang used theory and simulations to identify scale-free behavior in boiling: just as in traffic, the same patterns appear whether zoomed in or out, in terms of space or time. Both small and large bubbles matter. Using this insight, the team found certain physical parameters that could predict a boiling crisis. Zhang’s mathematical tools both explain experimental data and suggest new experiments to try. For a second paper, the team collected more data and found ways to predict the boiling crisis in a wider variety of conditions.

      Zhang’s thesis and third paper, both in progress, propose a universal law for explaining the crisis. “She translated the mechanism into a physical law, like F=ma or E=mc2,” Bucci says. “She came up with an equally simple equation.” Zhang says she’s learned a lot from colleagues in the department who are pioneering new nuclear reactors or other technologies, “but for my own work, I try to get down to the very basics of a phenomenon.”

      Bucci describes Zhang as determined, open-minded, and commendably self-critical. Su says she’s careful, optimistic, and courageous. “If I imagine going from heat transfer to city planning, that would be almost impossible for me,” he says. “She has a strong mind.” Last year, Zhang gave birth to a boy, whom she’s raising on her own as she does her research. (Her husband is stuck in China during the pandemic.) “This, to me,” Bucci says, “is almost superhuman.”

      Zhang will graduate at the end of the year, and has started looking for jobs back in China. She wants to continue in the energy field, though maybe not nuclear. “I will use my interdisciplinary knowledge,” she says. “I hope I can design safer and more efficient and more reliable systems to provide energy for our society.” More