Public transit agencies around the world are collecting more data than ever before, and they are turning to MIT to help them apply this data to improve operations and passenger outcomes. For decades, MIT has collaborated with some of the world's largest transit agencies to improve strategic, tactical and operational decisions.
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.
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.
The MIT Transit Lab leverages the value of large-scale, long-term research collaborations across transit agencies. Starting in 1992 under the leadership of Professor Nigel Wilson, the Lab has collaborated with metropolitan transit agencies and departments of transportation worldwide, developing and implementing technology for transit operations and planning. Past and ongoing research sponsors include the Chicago Transit Authority (CTA), Massachusetts Bay Transportation Authority (MBTA), Transport for London (TfL), and Mass Transit Railway (MTR, Hong Kong). These long-term engagements, in addition to projects with other transit agencies and international research centers, provide graduate students unique opportunities for applied research.
Transportation Systems Analysis: Performance & Optimization
Problem-motivated introduction to methods, models and tools for the analysis and design of transportation networks including their planning, operations and control. Capacity of critical elements of transportation networks. Traffic flows and deterministic and probabilistic delay models. Formulation of optimization models for planning and scheduling of freight, transit and airline systems, and their solution using software packages. User- and system-optimal traffic assignment. Control of traffic flows on highways, urban grids, and airspace.
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.
Transportation Systems Analysis: Demand and Economics
Covers the key principles governing transportation systems planning and management. Introduces the microeconomic concepts central to transportation systems. Topics include economic theories of the firm, consumer, and market, demand models, discrete choice analysis, cost models and production functions, and pricing theory. Applications to transportation systems - including congestion pricing, technological change, resource allocation, market structure and regulation, revenue forecasting, public and private transportation finance, and project evaluation - cover urban passenger transportation, freight, maritime, aviation, and intelligent transportation systems.
Theory and application of modeling and statistical methods for analysis and forecasting of demand for facilities, services, and products. Topics include: review of probability and statistics, estimation and testing of linear regression models, theory of individual choice behavior, derivation, estimation, and testing of discrete choice models (including logit, nested logit, GEV, probit, and mixture models), estimation under various sample designs and data collection methods (including revealed and stated preferences), sampling, aggregate forecasting methods, and iterative proportional fitting and related methods. Lectures reinforced with case studies, which require specification, estimation, testing, and analysis of models using data sets from actual applications.
Resilient Infrastructure Networks
Control algorithms and game-theoretic tools to enable resilient operation of large-scale infrastructure networks. Dynamical network flow models, stability analysis, robust predictive control, fault and attack diagnostic tools. Strategic network design, routing games, congestion pricing, demand response, and incentive regulation. Design of operations management strategies for different reliability and security scenarios. Applications to transportation, logistics, electric-power, and water distribution networks.
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.
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.
Urban Transportation Planning & Policy
The course examines urban transportation policymaking and planning, its relationship to social and environmental justice and the influences of politics, governance structures and human and institutional behavior. Through the lens of history and current events the course explores the pathway that led to today’s legacy infrastructure, legacy policies (and legacy thinking), how attitudes are influenced, and how change happens. The course will examine the tensions and potential synergies among transportation policy values of individual mobility, access, system efficiency and “sustainability”. Traditional planning methods will be assessed with a critical eye, and through that process students will learn how to approach transportation planning in a way that responds to contemporary needs and values, with an emphasis on transport justice.
Planning and policymaking will be discussed in relation to recent pandemic effects, which bring
an unprecedented level of uncertainty and complexity to the policy context. Among other
topics to be explored: the roles of the federal, state, and local government; analysis of current trends and pattern breaks; transport sector decarbonization; land use, placemaking, and sustainable mobility networks; the role of “mobility as a service”, and the implications of disruptive technology on personal mobility.
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.