Transportation Systems, M.S.
Technological advances in sensing, mobile communication, computation, imaging, artificial intelligence and many other fields have ushered in a new era for urban mobility. Transportation systems are becoming connected, automated, and electrified; on-demand mobility and delivery services are now ubiquitous in our cities; abundant, real-time traffic data makes possible adaptive congestion management strategies; combined with the near universality of smartphone ownership, these data make multi-modal transit systems possible. However, along with their promises for a better world, these systems pose a number of technological, operational, economic, and social challenges. The gap between technological advancement and its integration to our transportation system infrastructure is still large; concerns about privacy and data ownership abound; the safety of self-driving vehicles is still in question; the decarbonization of transportation systems is lagging behind; and the economic and social impacts of these technologies are yet to be fully understood.
The M.S. in Transportation Systems at NYU aims to equip students with the necessary knowledge to tackle the challenges inherent to this new era of urban mobility. The program, shaped by immersion in one of the largest metropolitan cities in the world, will provide students with a truly multidisciplinary education. They will gain solid technical foundations as transportation engineers, but also engage with other fields such as data science, operations research, economics, and public policy, to solve the pressing urban mobility challenges of the 21st century. Thus, our graduates will be able to bring their talents to engineering and technology companies; public agencies; and academia.
Transportation at NYU
Select Program Alumni:
- Chetan Sharma (Eisenhower Fellow), 2023; Transportation Engineer at VHB
- Zhuo Hao (Allen) Zhang, 2022; Engineering Associate, Burns Engineering
- Patrick Scalise, 2021, MS Thesis: “Paratransit operations design with disease contact exposure”; Transportation Planner, MTA
- Sofia Duran, 2021; Engineer III, Toole Design Group
- William Wong, 2021; Community Planner (Travel Forecast), Federal Transit Administration
- Ziyi Ma (Eisenhower Fellow), 2020; Research Engineer II, Blue Halo
- Srushti Rath, MS 2020, PhD 2022, MS Thesis: "Air taxi skyport location problem for airport access“; Research Scientist, Amazon
- Nick Caros, 2019, MS Thesis: “Dynamic operations of a mobility service with en-route transfers”; PhD student, MIT
- Heba Omholt (Eisenhower Fellow), 2018; Market Insights Analyst, Lyft
Eligibility
Applicants to the M.S. in Transportation Systems are expected to have an undergraduate degree in an engineering or related discipline, with a basic level of knowledge of probability and statistics. This background includes one year of college-level calculus, one year of college-level physical science, one semester engineering-level probability and statistics and one semester computer programming.
Find out more about Admission Requirements.
Curriculum
Goals and Objectives
The primary goal of the MS in Transportation Systems is to educate transportation professionals to plan, functionally design, control and operate facilities, systems and services that satisfy the demand for passenger and freight transportation. Students are expected to gain the knowledge and skills to become transportation systems engineers, mobility data scientists, transportation modelers, or traffic engineers. More specifically, students will be able to:
- Fundamentally understand the nature of supply and demand in transportation systems and their interactions;
- Design and analyze transportation systems and facilities (e.g.: transit systems, on-demand mobility services, freight and delivery services, etc.);
- Leverage a variety of tools and data sources to evaluate the performance of transportation systems and implement demand and supply management strategies;
- Understand the political and economic forces that shape and interact with these transportation systems.
Overview
A student must complete 30 credits, as outlined below.
A student must have a 3.0 GPA or better averaged across all graduate courses and in all guided studies (readings, projects, theses). Averages are separately computed for courses and guided studies. Transfer credits from other institutions are omitted from this average.
In addition, students are required to have an overall 3.0 GPA in all courses required for this degree. A student may not repeat a course that counts toward the program more than once.
In total there are 5 required courses (15 – 18 credits). All students have the same two required courses. A student chooses one Concentration to follow, which determines the remaining three required courses they will need to take.
Concentration A: Mobility Systems Engineering
focus on courses in traffic and transit operations, control, and mobility systems design. Students are expected to gain the knowledge and skills to become transportation systems engineers, mobility data scientists, transportation modelers, or traffic engineers.
Concentration B: Transportation Systems Management
focus on managerial analytics and decision support for policymaking involving transportation systems. Students are expected to gain the knowledge and skills to become transportation or mobility systems managers, transportation policy analysts, or project managers for transportation infrastructure operations and design.
Up to 6 credits may be taken for an MS thesis (TR-GY 997X) with a thesis supervisor. A student choosing this alternative may opt out of Transportation Capstone, TR-GY 6403.
Up to 9 credits may be taken from outside the civil engineering department, with the approval of the program director. Students typically take outside courses from CUSP, Wagner, Stern, the Department of Technology Management and Innovation at Tandon, or other areas that supplement the program along the student’s interests.
MS students who wish to continue onto a Ph.D. in Transportation Systems at NYU and have been accepted into the program by the time of the qualifying exam at the end of the Spring semester are eligible to take it.
Required Courses
All students:
- Urban Transportation & Logistics Systems TR-GY 7013 (3 credits)
- Transportation Capstone TR-GY 6403 (3 credits), OR MS Thesis TR-GY 997X (6 credits)
Mobility Systems Engineering Concentration A:
- Forecasting Urban Travel Demand TR-GY 6113 (3 credits)
- Choose TWO from:
- Traffic Operations and Control TR-GY 6343 (3 credits)
- Travel Behavioral Informatics TR-GY 7073 (3 credits)
- Analytics and Learning Methods for Smart Cities TR-GY 7083 (3 credits)
- Data Driven Mobility Modeling and Simulation TR-GY 7353 (3 credits)
Transportation Systems Management Concentration B:
- Transportation Economics TR-GY 6053 (3 credits
- Choose TWO from:
- Project Management for Construction CE-GY 8253 (3 credits)
- Forecasting Urban Travel Demand TR-GY 6113 (3 credits)
- Travel Behavioral Informatics TR-GY 7073 (3 credits)
- Public Transport TR-GY 7133 (3 credits)
Elective Courses
For any courses not listed below, check with the program advisor for approval. Any course listed from the required courses above is pre-approved as an elective, plus:
● TR-GY 7223 Transit Maintenance and Operations
● CE-GY 8263 Construction Cost Estimating
● CE-GY 8273 Contracts and Specifications
● CE-GY 8283 Risk Analysis
● CE-GY 8293 Construction Operations Analysis
● CE-GY 8303 Information Systems in Project Management
● CE-GY 8333 Marketing for Construction Management and Engineering Services
● CE-GY 8353 Construction Scheduling
● CE-GY 8373 Construction Accounting and Finance
Up to 9 credits can be taken from external departments including CUSP, Wagner, and Management of Technology. Preapproved courses include:
CUSP
● CUSP-GX 5002 Principles of Urban Informatics
● CUSP-GX 5003 Machine Learning for Cities
● CUSP-GX 5009 Innovative City Governance
● CUSP-GX 6001 Applied Data Science
● CUSP-GX 6002 Big Data Management & Analysis
● CUSP-GX 6006 Data Visualization
● CUSP-GX 7002 Urban Spatial Analytics
● CUSP-GX 7003 Civic Analytics and Urban Intelligence
● CUSP-GX 7004 Urban Decision Models
● CUSP-GX 8005 Data Driven Methods for Policy Evaluation
● CUSP-GX 8006 Disaster Risk Analysis and Urban Systems Resilience
Wagner
● URPL-GP 2614 Intelligent Cities: Technology, Policy and Planning
● URPL-GP 2631 Transportation, Land Use, and Urban Form
● URPL-GP 2645 Planning for Emergencies and Disasters
● PADM-GP 2106 Community Organizing
● PADM-GP 2145 Design Thinking
Management of Technology
● MG-GY 6013 Organizational Behavior
● MG-GY 6193 Statistics for Data Analysis
● MG-GY 6303 Operations Management
● MG-GY 6463 Supply Chain Management
● MG-GY 8423 Machine Learning for Business
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