Advances in communication and automation technology have given rise to new mobility technologies and platforms. From ridesharing and mobility-as-a-service to autonomous vehicles and air taxis, MIT has been leading the way on developing the necessary technology and infrastructure for the future of mobility.
The research labs and faculty working in this area are shown below. You can see a full listing of the people and labs involved with the MIT Mobility Initiative by navigating to the people page and the labs page.
Aerospace Controls Laboratory
The Aerospace Controls Laboratory researches topics related to autonomous systems and control design for aircraft, spacecraft, and ground vehicles. Areas of theoretical research include decision-making under uncertainty, path planning, activity and task assignment, estimation and navigation, robust, adaptive, and model predictive control, and machine learning methods.
City Form Lab
The City Form Lab at MIT focuses on urban design, planning and real-estate research. We develop new software tools for researching city form; use cutting-edge spatial analysis and statistics to investigate how urban form and land-use developments affect urban mobility and business location choices; and develop creative design and policy solutions for contemporary urban challenges. By bringing together multi-disciplinary urban research expertise and excellence in design, we develop context sensitive and timely insight about the role of urban form in affecting the quality of life in 21st century cities. CFL involves inter-disciplinary researchers and students interested in urban design, planning, transportation, spatial analysis and decision-making.
City Science Group
Founded in 1985, the MIT Media Lab is one of the world’s leading research and academic organizations. Unconstrained by traditional disciplines, Media Lab designers, engineers, artists, and scientists strive to create technologies and experiences that enable people to understand and transform their lives, communities, and environments. As part of the MIT Media Lab, the City Science research group proposes that new strategies must be found to create the places where people live and work in addition to the mobility systems that connect them, in order to meet the profound challenges of the future.
Electric Aircraft Initiative
The MIT Electric Aircraft Initiative draws together efforts across MIT aimed at long-term research on electric aircraft. Research spans fundamental propulsion technology development for small drones through to overall aircraft configuration assessment for all-electric commercial aircraft. The focus is on the very long term: technologies that could result in near-silent propulsion and low or no emissions. You can learn more about the overall research areas or read our publications.
The MIT Energy Initiative is MIT's hub for energy research, education, and outreach, connecting faculty, students, and staff to develop the knowledge, technologies, and solutions that will deliver clean, affordable, and plentiful sources of energy. Their mission is to develop low- and no-carbon solutions that will efficiently, affordably, and sustainably meet global energy needs while minimizing environmental impacts, dramatically reducing greenhouse gas emissions, and mitigating climate change.
Engineering Systems Laboratory
A part of the MIT Department of Aeronautics and Astronautics, the Engineering Systems Laboratory (ESL) studies the underlying principles and methods for designing complex socio-technical systems that involve a mix of architecture, technologies, organizations, policy issues and complex networked operations. Their focus is on aerospace and other systems critical to society such as product development, manufacturing and large scale infrastructures.
Intelligent Transportation Systems Lab
The MIT Intelligent Transportation Systems (ITS) Lab was established in 1990 by Professor Moshe Ben-Akiva. Since its inception, the ITS Lab has conducted numerous studies of transportation systems and developed network modeling and simulation tools. The lab's areas of research include discrete choice and demand modeling techniques, activity-based models, freight transport modeling, and data collection methods for behavioral modeling. Today, lab members are located at MIT's Cambridge campus and its first research center outside of Cambridge: the Singapore-MIT Alliance for Research and Technology (SMART) Centre.
JTL Urban Mobility Lab
The JTL Urban Mobility Lab at MIT brings behavioral science and transportation technology together to shape travel behavior, design mobility systems, and improve transportation policies. They apply this framework to managing automobile ownership and usage, optimizing public transit planning and operation, promoting active modes of walking and cycling, governing autonomous vehicles and shared mobility services, and designing multimodal urban transportation systems.
Laboratory for Aviation and the Environment
The Laboratory for Aviation and the Environment is a research lab in the MIT Department of Aeronautics & Astronautics. The team is interdisciplinary, covering expertise in Aeronautical, Mechanical and Chemical Engineering, Atmospheric Science and Economics.
Lincoln Laboratory - Transportation
The Transportation mission area at Lincoln Lab develops technology to enhance transportation safety and efficiency, supporting government sponsors in several domains, including flight safety and collision avoidance, unmanned aircraft systems, advanced air mobility, air transportation simulation, air traffic control and air traffic management, environmental impact of air traffic, weather sensing for air traffic control, aviation cyber security, and military logistics.
Megacity Logistics Lab
The Megacity Logistics Lab brings together business, logistics, and urban planning perspectives to develop appropriate technologies, infrastructures, and policies for sustainable urban logistics operations. Their work aims to promote new urban delivery models, from unattended home delivery solutions to smart locker systems, to click & collect services, to drone delivery. They are pushing the limits of existing logistics network designs as future city logistics networks need to support omni-channel retail models, smaller store formats, increased intensity of deliveries, coordinate multiple transshipment points, engage a wider range of vehicle technologies - including electric and autonomous vehicles - and support complex inventory balancing and deployment strategies.
Mobility Systems Center
The Mobility Systems Center, an MIT Energy Initiative Low-Carbon Energy Center, brings together MIT's extensive expertise in mobility research to understand current and future trends in global passenger and freight mobility. Approaching mobility from a socio-technical perspective, we identify key challenges, understand potential trends, and analyze the societal and environmental impact of new mobility solutions. Through developing, maintaining, and applying a set of state-of-the-art scientific tools for the mobility sector, the Center aims to assess future mobility transformations from a technological, economic, environmental, and socio-political perspective. Executive Director: Randall Field
Resilient Infrastructure Systems Lab
The Resilient Infrastructure Systems Lab seeks to improve the robustness and security of critical infrastructure systems by developing tools to detect and respond to incidents, both random and adversarial and by designing incentive mechanisms for efficient infrastructure management. They are working on the problems of cyber-physical security, failure diagnostics and incident response, network monitoring and control, and demand management in real-world infrastructures. They mainly focus on cyber-physical infrastructure systems for electric power, transportation, and urban water and natural gas networks.
Robust Robotics Group
The research goals of the Robust Robotics Group are to build unmanned vehicles that can fly without GPS through unmapped indoor environments, robots that can drive through unmapped cities, and to build social robots that can quickly learn what people want without being annoying or intrusive. Such robots must be able to perform effectively with uncertain and limited knowledge of the world, be easily deployed in new environments and immediately start autonomous operations with no prior information. They specifically focus on problems of planning and control in domains with uncertain models, using optimization, statistical estimation and machine learning to learn good plans and policies from experience.
Transportation Research Design
Seminar dissects ten transportation studies from head to toe to illustrate how research ideas are initiated, framed, analyzed, evidenced, written, presented, criticized, revised, extended, and published, quoted and applied. Students design and execute their own transportation research.
Decarbonizing Urban Mobility
This summer’s extreme weather and the just-released IPCC report have brought renewed attention to the urgent need to drive global carbon dioxide emissions to zero by 2050. Transportation is the single largest source of those emissions in the United States, and a major source globally. What combination of policy, technology, behavior change, and investment is best positioned to accelerate the decarbonization of urban mobility? A new course from MIT Mobility Initiative and DUSP Prof. Jinhua Zhao and transportation & climate change professional Andrew Salzberg will grapple with this question, drawing from the latest research and industry trends.
Mobility Ventures: Driving Innovation in Transportation Systems
This course is designed for students who aspire to shape the future of mobility. The course explores technological, behavioral, policy and systems-wide frameworks for innovation in transportation systems, complemented with case studies across the mobility spectrum, from autonomous vehicles to urban air mobility to last-mile sidewalk robots. Students will interact with a series of guest lecturers from CEOs and other business and government executives who are actively reshaping the future of mobility.
Theory and application of probabilistic techniques for autonomous mobile robotics. Topics include probabilistic state estimation and decision making for mobile robots; stochastic representations of the environment; dynamic models and sensor models for mobile robots; algorithms for mapping and localization; planning and control in the presence of uncertainty; cooperative operation of multiple mobile robots; mobile sensor networks; application to autonomous marine (underwater and floating), ground, and air vehicles.
Focuses on architectural and mobility interventions that respond to changing patterns of living, working, and transport. Emphasizes mass-customized housing, autonomous parking, charging infrastructure, and shared-use networks of lightweight electric vehicles (LEVs). Students work in small teams and are lead by researchers from the Changing Places group. Projects focus on the application of these ideas to case study cities and may include travel. Invited guests from academia and industry participate. Repeatable for credit with permission of instructor.
Robotics: Science and Systems
Presents concepts, principles, and algorithmic foundations for robots and autonomous vehicles operating in the physical world. Topics include sensing, kinematics and dynamics, state estimation, computer vision, perception, learning, control, motion planning, and embedded system development. Students design and implement advanced algorithms on complex robotic platforms capable of agile autonomous navigation and real-time interaction with the physical word. Students engage in extensive written and oral communication exercises.
Data Science and Machine Learning for Supply Chain Management
Introduces data science and machine learning topics in both theory and application. Data science topics include database and API connections, data preparation and manipulation, and data structures. Machine learning topics include model fitting, tuning and prediction, end-to-end problem solving, feature engineering and feature selection, overfitting, generalization, classification, regression, neural networks, dimensionality reduction and clustering. Covers software packages for statistical analysis, data visualization and machine learning. Introduces best practices related to source control, system architecture, cloud computing frameworks and modules, security, emerging financial technologies and software process. Applies teaching examples to logistics, transportation, and supply chain problems.