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.
