Smart Mobility (SM)

The UN predicts that 60% of the world’s population will live in cities by 2030. Updating urban transportation systems to accommodate this population growth is a massive undertaking, especially when many city planners are also concerned with increasing efficiency and reducing environmental impact. Civil engineers play essential roles in developing and executing strategies in AI, big data, and green transportation, and demand for civil engineers with training in these fields is expected to increase in the coming years.

The Smart Mobility (SM) track gives students hands-on experience studying emerging technologies in transportation engineering and related fields. Students will explore techniques for assessing the safety, efficiency, sustainability and societal impacts of smart mobility systems.

Research has shown that engineers with interdisciplinary educational backgrounds perform well in teams and enjoy a broad range of job opportunities. Purdue University – ranked #2 in online civil engineering by U.S. News and World Report 2024 – offers a Master's in Civil Engineering with an SM track, combining top-tier education with interdisciplinary training. Learn more about this track’s unique curriculum below:

Curriculum

Learners are required to take 15 core Civil Engineering course credits.  You may pick any from this group:

Course Title Credits
CE 50801: Geographic Information Systems 3
CE 53600: Non-destructive Testing and Sensing for Civil Infrastructures 3
CE 56401: Data Science for Smart Cities 3
CE 56601: Network Models for Connected and Autonomous Vehicles 3
CE 56901: Smart Logistics 3
CE 59700: UAS-Based LiDAR Mapping 1
CE 59700: UAS-Based Photogrammetric Mapping 1
CE 59700: UAS-Based Mapping: Basic Principles 1

Technical Electives can be chosen from the CE courses not taken from the above group, or the list below.  Nine credits are required:

Course Title Credits
ME 53900: Introduction to Scientific Machine Learning 3
ME 55400: Intellectual Property for Engineers  1
ECE 57000: Artificial Intelligence 3
ECE 58000: Optimization Methods for Systems Control 3
ECE 69500: Communication for Engineering Leaders 1
ECE 59500: Introduction to Deep Learning 3

Six credits of Applied Math and/or Statistics courses can be chosen from this list:

Course Title Credits
MA 51100: Linear Algebra with Applications 3
MA 52700: Adv Math for Engineers and Physicists I 3
MA 52800: Adv Math for Engineers and Physicists II 3
STAT 51200: Applied Regression Analysis 3
STAT 51400: Design of Experiments 3
STAT 51700: Statistical Inference 3

Sample Plan of Study - Smart Mobility

Course Title Credits Category
CE 50801: Geographic Information Systems 3 CE Core
CE 53600: Non-destructive Testing and Sensing for Civil Infrastructures 3 CE Core
CE 56401: Data Science for Smart Cities 3 CE Core
CE 56601: Network Models for Connected and Autonomous Vehicles 3 CE Core
CE 56901: Smart Logistics 3 CE Core
ECE 57000: Artificial Intelligence 3 Tech Elective
ECE 58000: Optimization Methods for Systems Control 3 Tech Elective
CE 59700: UAS-Based LiDAR Mapping 1 Tech Elective
CE 59700: UAS-Based Photogrammetric Mapping 1 Tech Elective
CE 59700: UAS-Based Mapping: Basic Principles 1 Tech Elective
STAT 51400: Design of Experiments 3 MATH/STAT
STAT 51700: Statistical Inference 3 MATH/STAT