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 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
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.