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.
Cities worldwide are undergoing radical changes in their transportation systems with the advent of advances in technology. 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 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 solutions and real-world applications. It provides an in-depth study of the most sophisticated traffic simulation models, demand modeling methods, and related 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."
- 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.
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.
This course will be taught on the Zoom platform.
One full-tuition scholarship will be awarded to an outstanding doctoral student. Half-tuition scholarships are also available for doctoral students. To apply for the scholarship, please email a CV and a letter stating the relevance of the course to your research to firstname.lastname@example.org. The deadline to apply for the scholarship is June 15. Doctoral student scholarship applicants should not register for the course until the scholarship decisions have been released shortly after that date.
Please contact Katie Rosa at email@example.com 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.
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:
- Modeling and Simulation Approaches for Future Mobility
- Microscopic and Mesoscopic Traffic Simulation
- Static and Dynamic Network Supply Models
- 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
- 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 Data and Economic Activity Models
- Simulating Freight Flows and Logistics Choices
- Evaluation of Traffic Predictions
Calibration and Validation
- Calibrating Simulation Systems
Links & Resources
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.
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.
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.