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Lead Instructor(s)
Jul 29 - Aug 02, 2024
Registration Deadline
Live Virtual
Course Length
5 Days
Course Fee
3.0 CEUs
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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. 

Course Overview

Cities worldwide are undergoing radical changes in their transportation systems with the advent of advances in technology such as autonomous vehicles, electric vehicles, AI-enabled vehicles, vehicle-to-vehicle (V2V) communication, autopilot features and on-demand urban transportation services. Recent trends include the proliferation of on-demand and shared services and automation in public and private transportation systems. These trends have heightened interest in Intelligent Transportation Systems (ITS), Smart Mobility, and real-time network management as potential solutions to mitigate congestion issues and improve traffic network efficiency. ITS techniques traditionally include real-time traffic control measures and real-time traveler information and guidance systems whose purpose is to assist travelers in making travel decisions including departure time, mode, and route choice decisions. Transportation researchers have developed models and simulation tools for use in the planning, design, and operations of such systems. However, with the advent of new technologies and services, these techniques need to be modified and better leveraged to improve system performance.

This course presents theory of transportation modelling and simulation techniques, with a focus on Smart Mobility, AI and ML solutions and real-world applications. It provides an in-depth study of the most sophisticated traffic simulation models, demand modeling methods, and related discrete choice, machine learning analytical techniques. Some of the topics include: modeling and simulation approaches for future mobility; discrete choice models and their application to travel choices and driving behavior; predicting traffic congestion; traffic flow models and simulation methods (microscopic, mesoscopic, and macroscopic); automated and connective vehicles in mixed traffic; alternative dynamic traffic assignment methods; and calibration of large scale simulation systems. In addition, the course covers recent developments in modelling, simulation, operations of smart mobility services, and machine learning applications in transportation. The course also includes case studies to elucidate the concepts and showcase the potential applications.

This course draws heavily on the results of recent research and is sponsored by the ITS Lab of the Massachusetts Institute of Technology. It was previously titled "Modeling and Simulation of Transportation Networks."

Certificate of Completion from MIT Professional Education

Transportation Networks cert image
Learning Outcomes
  • Understand transportation network demand and supply models.
  • Distinguish among alternative approaches to dynamic traffic assignment and traffic simulation.
  • Assess the advantages and disadvantages of alternative network modeling and simulation methods.

Program Outline

Class runs 9:30 am - 5:00 pm every day.

Special events include a reception for course participants and faculty on Monday night and a dinner on Thursday evening. All evening activities are included in tuition.

The course consists of a series of lectures, including software demonstrations and case studies that develop the concepts and techniques.

The following lecture topics may be addressed as part of the course:

Traffic Performance

  • Modeling and Simulation Approaches for Future Mobility
  • Microscopic and Mesoscopic Traffic Simulation
  • Static and Dynamic Network Supply Models

Smart Mobility

  • Analyzing Smart Mobility
  • Automated and Connected Vehicles in Mixed Traffic
  • Bi-level Optimization Algorithms for Smart Mobility
  • Mobility of the Future Outlook

Demand and User Behavior

  • Overview of Discrete Choice Analysis
  • Machine Learning Concepts
  • Route and Time-of-Travel Choice

Traffic Assignment

  • Equilibrium and Day-to-Day Dynamics
  • DTA Algorithms and Applications
  • Pricing and Travel Time Reliability
  • Real-Time Systems

Public Transportation Models

  • Framework and Low Frequency Services
  • High Frequency Services

Freight Models

  • Freight Data and Economic Activity Models
  • Simulating Freight Flows and Logistics Choices

Real-Time Systems

  • Evaluation of Traffic Predictions

Calibration and Validation

  • Calibrating Simulation Systems
Who Should Attend

This program is intended for individuals interested in theory, research, and practice, including:

1. Professionals in the mobility industry: including car companies, all mobility business in the private sector, manufacturers, infrastructure, and Transportation Network Companies (TCNs) such as Uber, Lyft, and other car-rental and car-sharing companies

2. Individuals with experience in transportation consulting, planning, and related government agencies

3. PhD students in transportation systems, civil engineering, economics, planning, and/or urban mobility

Participants with backgrounds in diverse areas such as traffic engineering, systems engineering, transportation planning, operations management, operations research, and control systems are also welcome.

Computer requirements

This course will be taught on the Zoom platform.


“The MIT short course far exceeded the experience at other classes. The environment and instructor's skills were excellent.”
“In general, the quality level of the instructors made the course. To have five days of lectures from the academics who are advancing the state-of-the-art instead of talking about the state-of-the-practice was extremely beneficial.”
“Great overview with specificity of model design, confirmation, calibration, and application.”
“The instructors were experts in this field, and they obviously had practical experience, which enhanced the lectures, and made for interesting conversation.”
“Very high quality of lectures. Comprehensive overview of the state-of-the-art in transportation modeling and simulation with a good balance of theory and practical applications.”
Download the Course Brochure
Transportation Networks and Smart Mobility: Methods and Solutions - Brochure Image

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 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 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.


The type of content you will learn in this course, whether it's a foundational understanding of the subject, the hottest trends and developments in the field, or suggested practical applications for industry.

Fundamentals: Core concepts, understandings, and tools - 30%|Latest Developments: Recent advances and future trends - 50%|Industry Applications: Linking theory and real-world - 20%
Delivery Methods

How the course is taught, from traditional classroom lectures and riveting discussions to group projects to engaging and interactive simulations and exercises with your peers.

Lecture: Delivery of material in a lecture format - 100%

What level of expertise and familiarity the material in this course assumes you have. The greater the amount of introductory material taught in the course, the less you will need to be familiar with when you attend.

Introductory: Appropriate for a general audience - 25%|Specialized: Assumes experience in practice area or field - 50%|Advanced: In-depth explorations at the graduate level - 25%