Doctor of Philosophy in Transportation
Graduate Programs in Transportation
Program Requirements
MIT graduate-level subjects taken to fulfill the requirements of the Master of Science in Transportation degree may be counted toward the doctoral program requirements.
| Core Subjects | ||
| 1.200[J] | Transportation: Foundations and Methods | 12 |
| 11.251 | Frontier of Transportation Research 1 | 6 |
| Select one of the following: | 12 | |
| Applied Category Theory for Engineering Design | ||
| Demand Modeling | ||
| Resilient Networks | ||
| Logistics Systems | ||
| Behavioral Science, AI, and Urban Mobility | ||
| Computation/Analytics | 12 | |
| Select one of the following: | ||
| Machine Learning for Sustainable Systems and Modeling with Machine Learning: from Algorithms to Applications | ||
| Statistics, Computation and Applications | ||
| Machine Learning | ||
| Statistical Learning Theory and Applications | ||
| The Analytics Edge | ||
| Advanced Analytics Edge | ||
| Core Research Area | 24 | |
| Select two subjects from one of the five approved areas: | ||
| Performance and Optimization | ||
| Resilient Networks | ||
| Nonlinear Optimization | ||
| Network Science and Models | ||
| Fundamentals of Probability | ||
| Discrete Probability and Stochastic Processes | ||
| Introduction to Mathematical Programming | ||
| Introduction to Mathematical Programming | ||
| Robust Modeling, Optimization, and Computation | ||
| Machine Learning and Statistics | ||
| Reinforcement Learning: Foundations and Methods | ||
| Machine Learning for Sustainable Systems and Modeling with Machine Learning: from Algorithms to Applications | ||
| Optimization for Machine Learning | ||
| Modern Mathematical Statistics | ||
| Inference and Information | ||
| Algorithms for Inference | ||
| Machine Learning | ||
| Statistical Learning Theory and Applications | ||
| Deep Learning | ||
| Advances in Computer Vision | ||
| Mathematical Statistics: a Non-Asymptotic Approach | ||
| Statistical Reinforcement Learning | ||
| Machine Learning Under a Modern Optimization Lens | ||
| Planning and Policy 2 | ||
| Behavioral Science, AI, and Urban Mobility | ||
| Comparative Land Use and Transportation Planning | ||
| Urban Transportation Planning and Policy | ||
| Logistics 3 | ||
| Logistics Systems | ||
| The Theory of Operations Management | ||
| Supply Chain Analytics and Supply Chain: Capacity Analytics | ||
| Demand 4 | ||
| Demand Modeling | ||
| Advanced Demand Modeling | ||
| Statistical Method in Economics and Estimation and Inference for Linear Causal and Structural Models 5 | ||
| Econometrics 5 | ||
| Unrestricted Electives 6 | 54 | |
| Thesis | ||
| 1.THG | Graduate Thesis 7 | 540 |
| Total Units | 660 | |
Note: Students in this program can choose to receive the Doctor of Philosophy or the Doctor of Science in Transportation. Students receiving veterans benefits must select the degree they wish to receive prior to program certification with the Veterans Administration.
| 1 | Taken twice for a total of 6 units. |
| 2 | The Policy and Planning area also requires a written exam. |
| 3 | Students who DO NOT take 1.260 to fulfill the Core requirement, must take either 15.764, or 15.762 and 15.763. Students who DO take 1.260 to fulfill the Core requirement must |
| 4 | Students who do NOT take 1.202 to fulfill the Core requirement must take 1.202, and either 1.205 or 14.382. Students who DO take 1.202 to fulfill the Core requirement must take either 1.205 and 14.382, or 1.205, 14.380, and 14.381. |
| 5 | For either 14.380 and 14.381, or for 14.382, students can instead choose one of the following, more advanced subjects: 14.384 Time Series Analysis, 14.385 Nonlinear Econometric Analysis, or 14.386 New Econometric Methods. |
| 6 | Students choose subjects in consultation with their advisor. |
| 7 | Thesis units are based on the average duration of the doctoral program (five years) but will vary based on how long the student remains in the program. |

