Big Data, Visualization and Media
New sensors and the rise of digital communication has created an explosion in the amount of travel data generated. MIT is investigating new ways of storing, analyzing and synthesizing this data to enable informed decisions by transportation operators and policy makers. Some examples are leveraging cell phone records to predict future travel patterns and using smart card data to design more efficient bus routes.
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.
Professor of Landscape Architecture and Urban Planning
Environmental Planning and Management, Healthy Communities and Active Living, Transportation and Mobility, Urban Design
Director of the MIT Megacity Logistics Lab; Director of the MIT CAVE Lab
Multi-tier Distribution Network Design, Urban Logistics, Last-Mile Delivery, Urban Freight Policy, Data Analytics and Visualization
Associate Professor of Technology and Urban Planning
Semi-formal Transportation, Urban Information, Technology, Media Design, Data Action, Urban Design, Data Visualization and Privacy
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.
Civic Data Design Lab
The Civic Data Design Lab works with data to understand it for public good. They seek to develop alternative practices which can make the work they do with data and images richer, smarter, more relevant, and more responsive to the needs and interests of citizens traditionally on the margins of policy development. In this practice they experiment with and develop data visualization and collection tools that allow us to highlight urban phenomena. Their methods borrow from the traditions of science and design by using spatial analytics to expose patterns and communicating those results, through design, to new audiences.
Computational and Visual Education (CAVE) Lab
The CAVE lab provides students, researchers, and decision makers with a more intuitive understanding of and access to quantitative methods to support strategic design, tactical planning and operational decision problems in the supply chain and logistics domain and related fields. Based on a newly created physical lab space at MIT CTL equipped with state-of-the-art visualization technology, the lab is developing interactive visual interfaces to data and analytical tools, addressing complex supply chain and logistics problems.
The lab enables research advances in three major domains:
Development, improvement and application of traditional quantitative methods in supply chain, logistics, and transportation decision making (network design, distribution systems, inventory management, risk management, etc.)
Adaptation and application of advanced data science methods (machine learning, network science, etc.) to large and diverse datasets to characterize, understand, predict, and improve the performance of complex supply networks, transportation and logistics systems
Behavioral analysis of human decision making in supply chain management, transportation and logistics in light of interactive visualization being used as a tool to communicate, analyze, and manipulate context- and problem-related information
Connection Science Living Labs
With its novel "Living Labs" paradigm for research in the field, MIT Connection Science brings together interdisciplinary experts to develop, deploy, and test - in actual living environments - new technologies and strategies for safe, trusted, data sharing. MIT is well positioned to take a leadership role in demonstrating not only how organizations can leverage data in the future, but how we collect, manage, and use personal information, from setting appropriate privacy policies to demonstrating systems that can implement it in practice.
Quest for Intelligence
MIT Quest addresses two fundamental questions: How does human intelligence work, in engineering terms? And how can we use our understanding of human intelligence to build smarter machines for the benefit of society? As part of our mission, we are developing customized AI tools for non-AI researchers, which could accelerate progress in many fields. We see an opportunity to achieve a deeper understanding of intelligence through the kind of basic research that leads to unexpected breakthroughs. We aspire for our new knowledge and newly built tools to serve the public good, in our nation and around the world.
Transportation Systems Modeling
Introduces basic concepts of transportation systems modeling, data analysis and visualization techniques. Covers fundamental analytical and simulation-based methodologies. Topics include time-space diagrams, cumulative plots, queueing theory, network science, data analysis, and their applications. Provides students with an understanding of the current challenges and opportunities in different areas of transportation.
Comparative Land Use and Transportation Planning
Focuses on the integration of land use and transportation planning, drawing from cases in both industrialized and developing countries. Reviews underlying theories, analytical techniques, and the empirical evidence of the land use-transportation relationship at the metropolitan, intra-metropolitan, and micro-scales. Also covers the various ways of measuring urban structure, form, and the "built environment." Develops students' skills to assess relevant policies, interventions and impacts.
Spatial Database Management and Advanced Geographic Information Systems
Extends the computing and geographic information systems (GIS) skills developed in 11.520 to include spatial data management in client/server environments and advanced GIS techniques. First half covers the content of 11.523, introducing database management concepts, SQL (Structured Query Language), and enterprise-class database management software. Second half explores advanced features and the customization features of GIS software that perform analyses for decision support that go beyond basic thematic mapping. Includes the half-term GIS project of 11.524 that studies a real-world planning issue.
Public Transportation Analytics and Planning
Students will gain experience processing, visualizing, and analyzing urban mobility data, with special emphasis on models and performance metrics tailored to scheduled, fixed-route transit services. The evolution of urban public transportation modes and services, as well as interaction with emerging on-demand services, will be covered. Instructors and guest lecturers from industry will discuss both methods for data collection and analysis, as well as organizational, policy, and governance constraints on transit planning. In assignments, students will practice using spatial database, data visualization, network analysis, and other software to shape recommendations for transit that effectively meets the future needs of cities.
Visual Navigation for Autonomous Vehicles
Covers the mathematical foundations and state-of-the-art implementations of algorithms for vision-based navigation of autonomous vehicles (e.g., mobile robots, self-driving cars, drones). Topics include geometric control, 3D vision, visual-inertial navigation, place recognition, and simultaneous localization and mapping. Provides students with a rigorous but pragmatic overview of differential geometry and optimization on manifolds and knowledge of the fundamentals of 2-view and multi-view geometric vision for real-time motion estimation, calibration, localization, and mapping. The theoretical foundations are complemented with hands-on labs based on state-of-the-art mini race car and drone platforms. Culminates in a critical review of recent advances in the field and a team project aimed at advancing the state-of-the-art.
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.
Statistics, Computation and Applications
Hands-on analysis of data demonstrates the interplay between statistics and computation. Includes four modules, each centered on a specific data set, and introduced by a domain expert. Provides instruction in specific, relevant analysis methods and corresponding algorithmic aspects. Potential modules may include medical data, gene regulation, social networks, finance data (time series), traffic, transportation, weather forecasting, policy, or industrial web applications. Projects address a large-scale data analysis question. Students taking graduate version complete additional assignments. Limited enrollment; priority to Statistics and Data Science minors and to juniors and seniors.