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    Machine learning speeds up vehicle routing

    Waiting for a holiday package to be delivered? There’s a tricky math problem that needs to be solved before the delivery truck pulls up to your door, and MIT researchers have a strategy that could speed up the solution.

    The approach applies to vehicle routing problems such as last-mile delivery, where the goal is to deliver goods from a central depot to multiple cities while keeping travel costs down. While there are algorithms designed to solve this problem for a few hundred cities, these solutions become too slow when applied to a larger set of cities.

    To remedy this, Cathy Wu, the Gilbert W. Winslow Career Development Assistant Professor in Civil and Environmental Engineering and the Institute for Data, Systems, and Society, and her students have come up with a machine-learning strategy that accelerates some of the strongest algorithmic solvers by 10 to 100 times.

    The solver algorithms work by breaking up the problem of delivery into smaller subproblems to solve — say, 200 subproblems for routing vehicles between 2,000 cities. Wu and her colleagues augment this process with a new machine-learning algorithm that identifies the most useful subproblems to solve, instead of solving all the subproblems, to increase the quality of the solution while using orders of magnitude less compute.

    Their approach, which they call “learning-to-delegate,” can be used across a variety of solvers and a variety of similar problems, including scheduling and pathfinding for warehouse robots, the researchers say.

    The work pushes the boundaries on rapidly solving large-scale vehicle routing problems, says Marc Kuo, founder and CEO of Routific, a smart logistics platform for optimizing delivery routes. Some of Routific’s recent algorithmic advances were inspired by Wu’s work, he notes.

    “Most of the academic body of research tends to focus on specialized algorithms for small problems, trying to find better solutions at the cost of processing times. But in the real-world, businesses don’t care about finding better solutions, especially if they take too long for compute,” Kuo explains. “In the world of last-mile logistics, time is money, and you cannot have your entire warehouse operations wait for a slow algorithm to return the routes. An algorithm needs to be hyper-fast for it to be practical.”

    Wu, social and engineering systems doctoral student Sirui Li, and electrical engineering and computer science doctoral student Zhongxia Yan presented their research this week at the 2021 NeurIPS conference.

    Selecting good problems

    Vehicle routing problems are a class of combinatorial problems, which involve using heuristic algorithms to find “good-enough solutions” to the problem. It’s typically not possible to come up with the one “best” answer to these problems, because the number of possible solutions is far too huge.

    “The name of the game for these types of problems is to design efficient algorithms … that are optimal within some factor,” Wu explains. “But the goal is not to find optimal solutions. That’s too hard. Rather, we want to find as good of solutions as possible. Even a 0.5% improvement in solutions can translate to a huge revenue increase for a company.”

    Over the past several decades, researchers have developed a variety of heuristics to yield quick solutions to combinatorial problems. They usually do this by starting with a poor but valid initial solution and then gradually improving the solution — by trying small tweaks to improve the routing between nearby cities, for example. For a large problem like a 2,000-plus city routing challenge, however, this approach just takes too much time.

    More recently, machine-learning methods have been developed to solve the problem, but while faster, they tend to be more inaccurate, even at the scale of a few dozen cities. Wu and her colleagues decided to see if there was a beneficial way to combine the two methods to find speedy but high-quality solutions.

    “For us, this is where machine learning comes in,” Wu says. “Can we predict which of these subproblems, that if we were to solve them, would lead to more improvement in the solution, saving computing time and expense?”

    Traditionally, a large-scale vehicle routing problem heuristic might choose the subproblems to solve in which order either randomly or by applying yet another carefully devised heuristic. In this case, the MIT researchers ran sets of subproblems through a neural network they created to automatically find the subproblems that, when solved, would lead to the greatest gain in quality of the solutions. This process sped up subproblem selection process by 1.5 to 2 times, Wu and colleagues found.

    “We don’t know why these subproblems are better than other subproblems,” Wu notes. “It’s actually an interesting line of future work. If we did have some insights here, these could lead to designing even better algorithms.”

    Surprising speed-up

    Wu and colleagues were surprised by how well the approach worked. In machine learning, the idea of garbage-in, garbage-out applies — that is, the quality of a machine-learning approach relies heavily on the quality of the data. A combinatorial problem is so difficult that even its subproblems can’t be optimally solved. A neural network trained on the “medium-quality” subproblem solutions available as the input data “would typically give medium-quality results,” says Wu. In this case, however, the researchers were able to leverage the medium-quality solutions to achieve high-quality results, significantly faster than state-of-the-art methods.

    For vehicle routing and similar problems, users often must design very specialized algorithms to solve their specific problem. Some of these heuristics have been in development for decades.

    The learning-to-delegate method offers an automatic way to accelerate these heuristics for large problems, no matter what the heuristic or — potentially — what the problem.

    Since the method can work with a variety of solvers, it may be useful for a variety of resource allocation problems, says Wu. “We may unlock new applications that now will be possible because the cost of solving the problem is 10 to 100 times less.”

    The research was supported by MIT Indonesia Seed Fund, U.S. Department of Transportation Dwight David Eisenhower Transportation Fellowship Program, and the MIT-IBM Watson AI Lab. More

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    One autonomous taxi, please

    If you don’t get seasick, an autonomous boat might be the right mode of transportation for you. 

    Scientists from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Senseable City Laboratory, together with Amsterdam Institute for Advanced Metropolitan Solutions (AMS Institute) in the Netherlands, have now created the final project in their self-navigating trilogy: a full-scale, fully autonomous robotic boat that’s ready to be deployed along the canals of Amsterdam. 

    “Roboat” has come a long way since the team first started prototyping small vessels in the MIT pool in late 2015. Last year, the team released their half-scale, medium model that was 2 meters long and demonstrated promising navigational prowess. 

    This year, two full-scale Roboats were launched, proving more than just proof-of-concept: these craft can comfortably carry up to five people, collect waste, deliver goods, and provide on-demand infrastructure. 

    The boat looks futuristic — it’s a sleek combination of black and gray with two seats that face each other, with orange block letters on the sides that illustrate the makers’ namesakes. It’s a fully electrical boat with a battery that’s the size of a small chest, enabling up to 10 hours of operation and wireless charging capabilities. 

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    Autonomous Roboats set sea in the Amsterdam canals and can comfortably carry up to five people, collect waste, deliver goods, and provide on-demand infrastructure.

    “We now have higher precision and robustness in the perception, navigation, and control systems, including new functions, such as close-proximity approach mode for latching capabilities, and improved dynamic positioning, so the boat can navigate real-world waters,” says Daniela Rus, MIT professor of electrical engineering and computer science and director of CSAIL. “Roboat’s control system is adaptive to the number of people in the boat.” 

    To swiftly navigate the bustling waters of Amsterdam, Roboat needs a meticulous fusion of proper navigation, perception, and control software. 

    Using GPS, the boat autonomously decides on a safe route from A to B, while continuously scanning the environment to  avoid collisions with objects, such as bridges, pillars, and other boats.

    To autonomously determine a free path and avoid crashing into objects, Roboat uses lidar and a number of cameras to enable a 360-degree view. This bundle of sensors is referred to as the “perception kit” and lets Roboat understand its surroundings. When the perception picks up an unseen object, like a canoe, for example, the algorithm flags the item as “unknown.” When the team later looks at the collected data from the day, the object is manually selected and can be tagged as “canoe.” 

    The control algorithms — similar to ones used for self-driving cars — function a little like a coxswain giving orders to rowers, by translating a given path into instructions toward the “thrusters,” which are the propellers that help the boat move.  

    If you think the boat feels slightly futuristic, its latching mechanism is one of its most impressive feats: small cameras on the boat guide it to the docking station, or other boats, when they detect specific QR codes. “The system allows Roboat to connect to other boats, and to the docking station, to form temporary bridges to alleviate traffic, as well as floating stages and squares, which wasn’t possible with the last iteration,” says Carlo Ratti, professor of the practice in the MIT Department of Urban Studies and Planning (DUSP) and director of the Senseable City Lab. 

    Roboat, by design, is also versatile. The team created a universal “hull” design — that’s the part of the boat that rides both in and on top of the water. While regular boats have unique hulls, designed for specific purposes, Roboat has a universal hull design where the base is the same, but the top decks can be switched out depending on the use case.

    “As Roboat can perform its tasks 24/7, and without a skipper on board, it adds great value for a city. However, for safety reasons it is questionable if reaching level A autonomy is desirable,” says Fabio Duarte, a principal research scientist in DUSP and lead scientist on the project. “Just like a bridge keeper, an onshore operator will monitor Roboat remotely from a control center. One operator can monitor over 50 Roboat units, ensuring smooth operations.”

    The next step for Roboat is to pilot the technology in the public domain. “The historic center of Amsterdam is the perfect place to start, with its capillary network of canals suffering from contemporary challenges, such as mobility and logistics,” says Stephan van Dijk, director of innovation at AMS Institute. 

    Previous iterations of Roboat have been presented at the IEEE International Conference on Robotics and Automation. The boats will be unveiled on Oct. 28 in the waters of Amsterdam. 

    Ratti, Rus, Duarte, and Dijk worked on the project alongside Andrew Whittle, MIT’s Edmund K Turner Professor in civil and environmental engineering; Dennis Frenchman, professor at MIT’s Department of Urban Studies and Planning; and Ynse Deinema of AMS Institute. The full team can be found at Roboat’s website. The project is a joint collaboration with AMS Institute. The City of Amsterdam is a project partner. More

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    Deep learning helps predict traffic crashes before they happen

    Today’s world is one big maze, connected by layers of concrete and asphalt that afford us the luxury of navigation by vehicle. For many of our road-related advancements — GPS lets us fire fewer neurons thanks to map apps, cameras alert us to potentially costly scrapes and scratches, and electric autonomous cars have lower fuel costs — our safety measures haven’t quite caught up. We still rely on a steady diet of traffic signals, trust, and the steel surrounding us to safely get from point A to point B. 

    To get ahead of the uncertainty inherent to crashes, scientists from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Qatar Center for Artificial Intelligence developed a deep learning model that predicts very high-resolution crash risk maps. Fed on a combination of historical crash data, road maps, satellite imagery, and GPS traces, the risk maps describe the expected number of crashes over a period of time in the future, to identify high-risk areas and predict future crashes. 

    Typically, these types of risk maps are captured at much lower resolutions that hover around hundreds of meters, which means glossing over crucial details since the roads become blurred together. These maps, though, are 5×5 meter grid cells, and the higher resolution brings newfound clarity: The scientists found that a highway road, for example, has a higher risk than nearby residential roads, and ramps merging and exiting the highway have an even higher risk than other roads. 

    “By capturing the underlying risk distribution that determines the probability of future crashes at all places, and without any historical data, we can find safer routes, enable auto insurance companies to provide customized insurance plans based on driving trajectories of customers, help city planners design safer roads, and even predict future crashes,” says MIT CSAIL PhD student Songtao He, a lead author on a new paper about the research. 

    Even though car crashes are sparse, they cost about 3 percent of the world’s GDP and are the leading cause of death in children and young adults. This sparsity makes inferring maps at such a high resolution a tricky task. Crashes at this level are thinly scattered — the average annual odds of a crash in a 5×5 grid cell is about one-in-1,000 — and they rarely happen at the same location twice. Previous attempts to predict crash risk have been largely “historical,” as an area would only be considered high-risk if there was a previous nearby crash. 

    The team’s approach casts a wider net to capture critical data. It identifies high-risk locations using GPS trajectory patterns, which give information about density, speed, and direction of traffic, and satellite imagery that describes road structures, such as the number of lanes, whether there’s a shoulder, or if there’s a large number of pedestrians. Then, even if a high-risk area has no recorded crashes, it can still be identified as high-risk, based on its traffic patterns and topology alone. 

    To evaluate the model, the scientists used crashes and data from 2017 and 2018, and tested its performance at predicting crashes in 2019 and 2020. Many locations were identified as high-risk, even though they had no recorded crashes, and also experienced crashes during the follow-up years.

    “Our model can generalize from one city to another by combining multiple clues from seemingly unrelated data sources. This is a step toward general AI, because our model can predict crash maps in uncharted territories,” says Amin Sadeghi, a lead scientist at Qatar Computing Research Institute (QCRI) and an author on the paper. “The model can be used to infer a useful crash map even in the absence of historical crash data, which could translate to positive use for city planning and policymaking by comparing imaginary scenarios.” 

    The dataset covered 7,500 square kilometers from Los Angeles, New York City, Chicago and Boston. Among the four cities, L.A. was the most unsafe, since it had the highest crash density, followed by New York City, Chicago, and Boston. 

    “If people can use the risk map to identify potentially high-risk road segments, they can take action in advance to reduce the risk of trips they take. Apps like Waze and Apple Maps have incident feature tools, but we’re trying to get ahead of the crashes — before they happen,” says He. 

    He and Sadeghi wrote the paper alongside Sanjay Chawla, research director at QCRI, and MIT professors of electrical engineering and computer science Mohammad Alizadeh, ​​Hari Balakrishnan, and Sam Madden. They will present the paper at the 2021 International Conference on Computer Vision. More

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    Last-mile routing research challenge awards $175,000 to three winning teams

    Routing is one of the most studied problems in operations research; even small improvements in routing efficiency can save companies money and result in energy savings and reduced environmental impacts. Now, three teams of researchers from universities around the world have received prize money totaling $175,000 for their innovative route optimization models.

    The three teams were the winners of the Amazon Last-Mile Routing Research Challenge, through which the MIT Center for Transportation & Logistics (MIT CTL) and Amazon engaged with a global community of researchers across a range of disciplines, from computer science to business operations to supply chain management, challenging them to build data-driven route optimization models leveraging massive historical route execution data.

    First announced in February, the research challenge attracted more than 2,000 participants from around the world. Two hundred twenty-nine researcher teams formed during the spring to independently develop solutions that incorporated driver know-how into route optimization models with the intent that they would outperform traditional optimization approaches. Out of the 48 teams whose models qualified for the final round of the challenge, three teams’ work stood out above the rest. Amazon provided real operational training data for the models and evaluated submissions, with technical support from MIT CTL scientists.

    In real life, drivers frequently deviate from planned and mathematically optimized route sequences. Drivers carry information about which roads are hard to navigate when traffic is bad, when and where they can easily find parking, which stops can be conveniently served together, and many other factors that existing optimization models simply don’t capture.

    Each model addressed the challenge data in a unique way. The methodological approaches chosen by the participants frequently combined traditional exact and heuristic optimization approaches with nontraditional machine learning methods. On the machine learning side, the most commonly adopted methods were different variants of artificial neural networks, as well as inverse reinforcement learning approaches.

    There were 45 submissions that reached the finalist phase, with team members hailing from 29 countries. Entrants spanned all levels of higher education from final-year undergraduate students to retired faculty. Entries were assessed in a double-blind review process so that the judges would not know what team was attached to each entry.

    The third-place prize of $25,000 was awarded to Okan Arslan and Rasit Abay. Okan is a professor at HEC Montréal, and Rasit is a doctoral student at the University of New South Wales in Australia. The runner-up prize at $50,000 was awarded to MIT’s own Xiaotong Guo, Qingyi Wang, and Baichuan Mo, all doctoral students. The top prize of $100,000 was awarded to Professor William Cook of the University of Waterloo in Canada, Professor Stephan Held of the University of Bonn in Germany, and Professor Emeritus Keld Helsgaun of Roskilde University in Denmark. Congratulations to all winners and contestants were held via webinar on July 30.

    Top-performing teams may be interviewed by Amazon for research roles in the company’s Last Mile organization. MIT CTL will publish and promote short technical papers written by all finalists and might invite top-performing teams to present at MIT. Further, a team led by Matthias Winkenbach, director of the MIT Megacity Logistics Lab, will guest-edit a special issue of Transportation Science, one of the most renowned academic journals in this field, featuring academic papers on topics related to the problem tackled by the research challenge. More

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    Smarter regulation of global shipping emissions could improve air quality and health outcomes

    Emissions from shipping activities around the world account for nearly 3 percent of total human-caused greenhouse gas emissions, and could increase by up to 50 percent by 2050, making them an important and often overlooked target for global climate mitigation. At the same time, shipping-related emissions of additional pollutants, particularly nitrogen and sulfur oxides, pose a significant threat to global health, as they degrade air quality enough to cause premature deaths.

    The main source of shipping emissions is the combustion of heavy fuel oil in large diesel engines, which disperses pollutants into the air over coastal areas. The nitrogen and sulfur oxides emitted from these engines contribute to the formation of PM2.5, airborne particulates with diameters of up to 2.5 micrometers that are linked to respiratory and cardiovascular diseases. Previous studies have estimated that PM2.5  from shipping emissions contribute to about 60,000 cardiopulmonary and lung cancer deaths each year, and that IMO 2020, an international policy that caps engine fuel sulfur content at 0.5 percent, could reduce PM2.5 concentrations enough to lower annual premature mortality by 34 percent.

    Global shipping emissions arise from both domestic (between ports in the same country) and international (between ports of different countries) shipping activities, and are governed by national and international policies, respectively. Consequently, effective mitigation of the air quality and health impacts of global shipping emissions will require that policymakers quantify the relative contributions of domestic and international shipping activities to these adverse impacts in an integrated global analysis.

    A new study in the journal Environmental Research Letters provides that kind of analysis for the first time. To that end, the study’s co-authors — researchers from MIT and the Hong Kong University of Science and Technology — implement a three-step process. First, they create global shipping emission inventories for domestic and international vessels based on ship activity records of the year 2015 from the Automatic Identification System (AIS). Second, they apply an atmospheric chemistry and transport model to this data to calculate PM2.5 concentrations generated by that year’s domestic and international shipping activities. Finally, they apply a model that estimates mortalities attributable to these pollutant concentrations.

    The researchers find that approximately 94,000 premature deaths were associated with PM2.5 exposure due to maritime shipping in 2015 — 83 percent international and 17 percent domestic. While international shipping accounted for the vast majority of the global health impact, some regions experienced significant health burdens from domestic shipping operations. This is especially true in East Asia: In China, 44 percent of shipping-related premature deaths were attributable to domestic shipping activities.

    “By comparing the health impacts from international and domestic shipping at the global level, our study could help inform decision-makers’ efforts to coordinate shipping emissions policies across multiple scales, and thereby reduce the air quality and health impacts of these emissions more effectively,” says Yiqi Zhang, a researcher at the Hong Kong University of Science and Technology who led the study as a visiting student supported by the MIT Joint Program on the Science and Policy of Global Change.

    In addition to estimating the air-quality and health impacts of domestic and international shipping, the researchers evaluate potential health outcomes under different shipping emissions-control policies that are either currently in effect or likely to be implemented in different regions in the near future.

    They estimate about 30,000 avoided deaths per year under a scenario consistent with IMO 2020, an international regulation limiting the sulfur content in shipping fuel oil to 0.5 percent — a finding that tracks with previous studies. Further strengthening regulations on sulfur content would yield only slight improvement; limiting sulfur content to 0.1 percent reduces annual shipping-attributable PM2.5-related premature deaths by an additional 5,000. In contrast, regulating nitrogen oxides instead, involving a Tier III NOx Standard would produce far greater benefits than a 0.1-percent sulfur cap, with 33,000 further avoided deaths.

    “Areas with high proportions of mortalities contributed by domestic shipping could effectively use domestic regulations to implement controls,” says study co-author Noelle Selin, a professor at MIT’s Institute for Data, Systems and Society and Department of Earth, Atmospheric and Planetary Sciences, and a faculty affiliate of the MIT Joint Program. “For other regions where much damage comes from international vessels, further international cooperation is required to mitigate impacts.” More