Ready to design the transportation systems of the future? Acquire the cutting-edge strategies you need by exploring cutting-edge traffic simulation models, demand modeling methods, and related analytical techniques. Over the course of five days, you’ll delve into the latest research from MIT’s Intelligent Transportation Systems Lab and learn to translate real-time data into real-world results that mitigate traffic congestion and other transportation challenges.
Transportation and mobility literally shape our society and economy as they allow people to engage in activities and freight to be moved along the production-consumption chain. Transportation technologies are undergoing simultaneous and disruptive innovations. This revolution - counted as the seventh in history - will change the landscape for anyone engaged in transportation systems analysis, operation and design. Autonomous vehicles, electric mobility, AI-enabled transportation, vehicle-to-vehicle (V2V) communication, and on-demand services are transforming mobility into a more user-centric, sustainable, and connected experience. From a planning perspective, these disruptions challenge traditional approaches to transportation system design, requiring integration of land use, infrastructure investments, and policy frameworks that promote equitable and sustainable outcomes. From an operational perpective, Intelligent Transportation Systems (ITS) have historically played a key role in dynamic traffic management, network optimization, and personalized traveler guidance. They must now adapt to incorporate AI-driven analytics, predictive modeling, and connected vehicle systems in ways that align with broader urban planning strategies.
Practitioners, engineers, researchers, and data scientists working in government, industry or academia are all potential audiences for the Course. Interested professionals include AI developers in the automotive and transportation services sectors, as well as urban science and urban environmental analysts. We highlight that since transportation is a dimension integrated into complex spatial systems of cities, regions, and countries, analysts working in such systems also belong to the Course's potential audience. For all these professionals, the course will provide a unique opportunity to be updated on major changes in services, navigation, and energy technologies, as well as the methodologies needed to analyze, operate, and design them.
This course offers a comprehensive exploration of transportation modeling and simulation techniques, with an emphasis on Smart Mobility, AI, and machine learning applications. Participants will delve into advanced traffic simulation models (microscopic, mesoscopic, and macroscopic), discrete choice modeling for travel behavior, and machine learning techniques for predictive analysis. The course addresses key themes such as managing on-demand and user-centric mobility, predicting and mitigating traffic congestion, and simulating future transportation systems, including connected and automated vehicles. It also covers green mobility, focusing on the adoption of electric vehicles, decarbonization strategies, and integrating active and micro-mobility options. By incorporating case studies and applications of big data, the course examines integrated transportation systems, including dynamic traffic assignment methods, land-use interactions, and innovations in sustainable mobility. Participants will gain insights into the societal and environmental implications of emerging technologies while exploring their transformative potential for transportation systems.
- Understand how to apply fundamental methods for modeling and analyzing transportation systems
- Analyze individual mobility-related choices
- Design and evaluate the operations of transportation networks and mobility services
- Implement and analyze different modeling and solution methods to study future transportation problems and solutions
- Anticipate future challenges of green, automated, user-centric, and AI-driven solutions in transportation and apply advanced methods to assess its impact on future policies
Engage in practical, real-world applications through interactive case studies during lectures
Core Competencies
- Applying fundamental methods for modeling, analyzing, and optimizing transportation systems across various scales (microscopic, mesoscopic, and macroscopic).
- Analyzing and predicting individual mobility-related choices using discrete choice modeling, behavioral analysis, and other relevant technologies
- Designing, evaluating, and optimizing the operations of transportation networks, and mobility services, ensuring efficient and sustainable performance
- Implementing and analyzing various modeling approaches and solution methods to assess future transportation challenges and evaluate potential solutions
- Anticipating future challenges posed by green, automated, user-centric, and AI-driven transportation solutions, and apply advanced methods to assess their impact on policy, infrastructure, and urban planning
Program Outline
Class runs 9:30 am - 5:00 pm every day (ET)
Special events include a welcome solcial hour for course participants and faculty on Monday night.
Each day of the course is structured into 4 lectures covering: Demand, Supply, Interactions and Foresight, respectively, and one dynamic breakout session designed to apply and discuss concepts to real-life scenarios.
The week is organised into 5 daily topics:
Day 1 – Foundations
- Demand Models
- Transportation Network Supply Models
- Equilibrium and Day-to-day Dynamics
- AI in Transportation Systems for Decision Making
Day 2 – User Centric Mobility
- Route, Time-of-Travel and Other Relevant Choices
- On-Demand and Responsive Services
- On-Demand Systems Simulation
- Future Demand Management
Day 3 – Green Transition
- User Behavior and Electric Mobility
- Managing Electric Mobility
- Active and Micro mobility
- Alternative Energies and decarbonization scenarios for road transportation
Day 4 – Connected and Automated Mobility
- Demand and Preferences Shifts with Automated Mobility
- Future road systems and traffic theory: Connected and Automated Vehicles
- Simulating Future Automated Mobility
- Autonomy and its Implications for Society and Environment
Day 5 – Integrated Systems
- Big data for Transportation Demand
- Scenario Discovery
- Future Mobility and Land-use
- Transport Innovations: Evolutions or Revolutions - Lecture and Round Table
Links & Resources
News/Articles:
This program is designed for professionals from various sectors looking for a comprehensive understanding of today’s evolving transportation landscape. The program is particularly relevant for:
- Transportation engineers and urban planners working in government, industry, or academia looking to deepen their understanding of emerging transportation technologies and how to integrate them into urban and regional planning
- AI developers in the automotive and transportation services sectors interested in designing smarter, more responsive mobility solutions
- Researchers and data scientists seeking to gain practical experience with advanced transportation modeling, big data applications, and predictive modeling techniques
- Urban science and environmental analysts looking to understand how transportation technologies intersect with environmental and societal factors
- Transportation systems analysts seeking to use advanced modeling techniques to improve traffic flow, reduce congestion, and enhance urban mobility
Computer requirements
This course will be taught on the Zoom platform.
One full scholarship will be awarded to an outstanding doctoral student. 50% scholarships are available for junior faculty, postdocs, and doctoral students. To apply for the scholarship, before submitting your registration for this program please email a CV and a letter stating the relevance of the course to your research to transportationdoctoralscholarship@mit.edu. The deadline to apply for the scholarship is June 30. You should wait for the scholarship decision before submitting your registration.
Please contact Katie Rosa at transportationdoctoralscholarship@mit.edu with any questions.
Discounts for Faculty
In addition, a limited number of 50% scholarships are available for teaching faculty, rank of instructor or higher, at other educational institutions. Decisions are made on a rolling basis after submitting a course registration form and a Scholarship Request Form. Please note that these scholarships are only for tuition and do not cover travel, lodging, or other expenses associated with the course.
If you have any questions please contact the Short Programs office.