This course is offered in a hybrid format, with in-person and live online cohorts attending simultaneously. When registering, select the appropriate registration button below.

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Lead Instructor(s)
Participating Instructor(s)
Date(s)
Jul 27 - 31, 2026
Registration Deadline
Location
On Campus
Course Length
5 Days
Course Fee
$3,750
CEUs
3.0 CEUs
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Ready to face the revolutions in transportation systems and discover how disruptive innovations are reshaping the mobility sector? In this immersive five-day course, you will learn to model, analyze and optimize transportation systems using the latest research from MIT and beyond, delving into demand and network modelling, artificial intelligence, simulation, optimization and control. Innovative methods are explored for emerging systems that are still in an early stage of development and deployment, such as user-centric new smart mobility services, automated and AI-driven vehicles and alternative energy vectors for decarbonizing transportation. Through real-world case studies, transportation researchers and professionals from urban and mobility organizations, the automotive industry, logistics companies, and other transportation sectors can gain actionable insights to address current and future transportation challenges.

Registered students will have the opportunity to propose relevant specific projects, research topics, or problems that they would like to be included in the course activities.

Course Overview

This course offers a comprehensive exploration of emerging transportation modeling and simulation techniques, with an emphasis on Smart Mobility, AI, and machine learning applications. Participants will delve into the latest advancements in traffic simulation models (microscopic, mesoscopic, and macroscopic), discrete choice modeling for travel behavior, and machine learning techniques for transport applications. 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, and urban air mobility. 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 in this early stage of their deployment. Participants will gain insights into the societal and environmental implications of emerging technologies including equity and resiliency while exploring their transformative potential for transportation systems.

Learning Outcomes
  • Understand how to apply fundamental methods for modeling and analyzing transportation systems including new technologies and services
  • Analyze individual mobility-related choices
  • Design and evaluate the operations of transportation networks with new technologies 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 social 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 organized into 5 daily topics including case studies:

Day 1

  • Demand Models
  • Transportation Network Models
  • Equilibrium and Day-to-day Dynamics
  • AI in Transportation Systems 

Day 2

  • Mode , Route, Time-of-Travel and Other Relevant Choices
  • Supply Simulation
  • On-Demand and User-centric Mobility
  • Future Demand Management Policies

Day 3 

  • User Behavior and Electric Mobility
  • Managing Electric Mobility
  • Systems with Green and Active Mobility
  • Alternative Energies and Decarbonization Scenarios

Day 4

  • Demand and Preferences Shifts with Automated Mobility
  • Traffic Theory with Connected and Automated Vehicles
  • Simulating Automated Mobility Systems
  • Autonomy and its Implications for Society and Environment

Day 5

  • Big data for Transportation Demand
  • Resiliency of Transportation Systems
  • Future Mobility and Urban Spaces
  • Transport Innovations: Evolutions or Revolutions - Lecture and Round Table

Links & Resources

News/Articles:

Who Should Attend

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
  • Professionals 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
  • Public and private sector project managers and consultants that seek to deepen their understanding of modeling and AI methods for transportation
     

Computer requirements

This course will be taught on the Zoom platform.

Brochure
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Scholarships

One full scholarship will be awarded to an outstanding doctoral student. A limited number of 75% scholarships are available for junior faculty, postdocs, and doctoral students. To apply for a 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 75% 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.