1.041/1.200/IDS.075/IDS.675/11.544 Spring 24      

Transportation: Foundations and Methods


Undergrad/Grad (Spring)

Prereq: 1.010 and (1.00 or 1.000)

Units: 3-1-8

Lectures: WF 2:30 - 4:00 pm (1-135)


M 2-3 PM (1-150)

Course description

Covers core analytical and numerical methods for modeling, planning, operations, and control of transportation systems. Traffic flow theory, vehicle dynamics and behavior, numerical integration and simulation, graphical analysis. Properties of delays, queueing theory. Resource allocation, optimization models, linear and integer programming. Autonomy in transport, Markov Decision Processes, reinforcement learning, deep learning. Applications drawn broadly from land, air, and sea transport; private and public sector; transport of passengers and goods; futuristic, modern, and historical. Hands-on computational labs. Linear algebra background is encouraged but not required. Students taking graduate version complete additional assignments.


At a glance

Expectations and prerequisites

This class is suitable for students who are interested in a technical introduction to transportation systems. In terms of prerequisites, students should be comfortable with the basics of probability and statistics (1.010 or equivalent) and programming (Python). For those joining with experience with different programming languages (e.g. MATLAB), we will offer some additional guidance and resources to get started with Python. Please note that the course features a large hands-on computational lab component, and the computational labs will require proficiency in Python. The course will introduce foundational knowledge and technical tools. The course also serves as preparation for more advanced coursework and will prepare students for conducting technical research in transportation fields.

Textbooks and references

  1. Unit 1: Daganzo, Carlos. Fundamentals of transportation and traffic operations. Emerald Group Publishing (2008). Available online: http://ndl.ethernet.edu.et/bitstream/123456789/75532/1/66.pdf.
  2. Unit 2: Larson, Richard C. and Amedeo R. Odoni. Urban Operations Research. Prentice-Hall (1981). Available online: https://web.mit.edu/urban_or_book/www/book/.
  3. Unit 3: Morales, Miguel. Grokking deep reinforcement learning. Manning Publications (2020). Available online: https://www.manning.com/books/grokking-deep-reinforcement-learning
  4. Unit 4: Bradley, Stephen P., Arnoldo C. Hax, and Thomas L. Magnanti. Applied mathematical programming. Addison-Wesley (1977). Available online:
    https://web.mit.edu/15.053/www/AppliedMathematicalProgramming.pdf .
  5. Additional handouts will be distributed as needed.



Assignments will be released ~2 weeks ahead of the due date. Submissions are through Gradescope and due dates will be provided. Please register for the course on Gradescope with your MIT email. Each student will be permitted up to 3 late days. After that, late homework will be penalized 10% every 24 hours. The solutions for homework will be released shortly after the deadline (those submitting late must abide by honor code).

If you are interested in finding pset partners, check out https://psetpartners.mit.edu. Sign up early; matching will be done at the end of the first week of classes.

Class project

Graduate students only: Research project, which seeks to establish new knowledge in transportation research fields.
  • (Optional) Students may opt to do a video presentation
  • Groups of 1-2 are permitted.
  • Class participation

    Class participation includes:
    1. Live participation during lectures.
    2. Answering questions for fellow students on Piazza.
    3. Attending office hours and recitation.

    Course pointers

    1. Website: for class materials & info
    2. Piazza: For class announcements, assessments, solutions
      • The Piazza is also a resource for you to collaborate with one another.
      • For obvious reasons, don't post answers in Piazza.
      • We (the staff) can’t answer each question on Piazza, so do come to office hours.
    3. Gradescope: For HW/quiz submissions
    4. Canvas: Code/project submissions, Zoom (in case of going remote)
    5. Email: You can reach the staff generally via office hours or via email. When emailing, include "[1.041]" or/and "[1.200]" at the start of the subject line.

    On collaboration and academic honesty

    The bottom line is:
    1. Use whatever sources you need to support your learning.
    2. Cite your sources.
    3. No copying.You must write up your own solutions.
    4. Don’t allow others to copy your work.
    In more words:

    If you do collaborate on homework, you must cite, in your written solution, your collaborators. If you use sources beyond the course materials in one of your solutions, e.g., a “friendly expert,” another text, website, or a ”bible”, be sure to cite the source. There is no penalty for such collaboration or use of other sources, as long as it is disclosed.

    This also applies for use of Generative AI (GenAI) tools, optionally including the prompt(s) because the instructors are also curious. (Exception: Use of these tools for help with writing is fine, encouraged, and does not need to be cited.)

    We encourage you to collaborate on homework. Study groups can be an excellent means to master course material. However, you must write up solutions on your own, neither copying solutions nor providing solutions to be copied. Duplicating a solution that someone else has written (verbatim or edited), or providing solutions for a fellow-student to copy, is not acceptable.

    In general, we expect students to adhere to basic, common sense concepts of academic honesty. Presenting somebody else’s work as if it were your own, or cheating in exams, is of course unacceptable.

    On student support

    Undergraduate students
    If you are dealing with a personal or medical issue that is impacting your ability to attend class, complete work, or take an exam, you should contact a dean in Student Support Services (S3). S3 is here to help you. The deans will verify your situation, provide you with support, and help you work with your professor or instructor to determine next steps. In most circumstances, you will not be excused from coursework without verification from a dean. Please visit the S3 website for contact information and more ways that they can provide support.

    Website: https://studentlife.mit.edu/s3

    Graduate students As a graduate student, a variety of issues may impact your academic career including faculty/student relationships, funding, and interpersonal concerns. In the Office of Graduate Education (OGE), GradSupport provides consultation, coaching, and advocacy to graduate students on matters related to academic and life challenges. If you are dealing with an issue that is impacting your ability to attend class, complete work, or take an exam, you may contact GradSupport by email at gradsupport@mit.edu or via phone at (617) 253-4860.

    Website: https://oge.mit.edu/development/gradsupport/

    Disability and Access Services MIT is committed to the principle of equal access. Students who need disability accommodations are encouraged to speak with Disability and Access Services (DAS), prior to or early in the semester so that accommodation requests can be evaluated and addressed in a timely fashion. If you have a disability and are not planning to use accommodations, it is still recommended that you meet with DAS staff to familiarize yourself with their services and resources. Please visit the DAS website for contact information.

    If you have already been approved for accommodations, course staff are ready to assist with implementation. Please inform Professor Wu AND TAs who will oversee accommodation implementation for this course.

    Website: https://studentlife.mit.edu/das