/Computational_Transportation_Science

Computational Transportation Science (CTS) resources for everyone!

Primary LanguageJupyter Notebook

Computational Transportation Science

Operation, management, control, design, evaluation of passenger and freight transportation systems. Traffic theory, introductory road design, air transportation, rail transportation, and an introduction to both Autonomous Vehicles, Intelligent Transportation Systems (ITS) and modern concepts of Smart Mobility for futuristic Smart Cities. Extensive use of computational tools will be used to work on hands-on assignments to solve modern day transportation problems.

Prerequisites:

  • One of the following:

    • CEE 202 Engineering Uncertainty & Risk
    • CS 361 Probability & Statistics for Computer Science
    • ECE 313 Probability with Electrical Engineering Applications
    • IE 300 Analysis of Data
    • STAT 400 Statistics & Probability 1

Contributors:

  • Louis Sungwoo Cho (ITE@UIUC President)
  • Institute of Transportation Engineers UIUC Chapter (ITE@UIUC)

Teaching Guidelines:

Proposed Grading:

  • 5% Lecture Attendance
  • 5% Weekly Checkpoints
  • 20% Prairielearn Assignments
  • 20% Midterm Exams
  • 40% Mini-Projects (MP)
  • 10% Final Presentations
  • Extra Credits

Proposed Grading Scale:

Percentage Letter Grade
95 A+
90 A
85 A-
80 B+
75 B
70 B-
65 C+
60 C
55 C-
50 D+
45 D
40 D-
0 F ~1%

Don't worry everyone, I will make the grading as lenient as possible. If you got a flat 90.0% overall, then you will get a flat 90.0%. If you get a floating point number of 90.00125% or 90.6763% or 90.9998%, you will be rounded up to 91%.

IMPORTANT!

UIUC CEE 310 Transportation Engineering is run independently. I am NOT using their grading scale to compute what the proposed transportation engineering course's final grading will be like. If you are an undergraduate student at UIUC who needs course credit AND letter grade for CEE 310, please take their course. Official course description of UIUC CEE 310 can be found here: CEE 310

Lecture Attendance:

  • Please go to lecture to meet new friends and professors. You will have a much diverse understanding of how transportation engineering is applied into many different fields in our modern society. 3 of your lecture attendances will be dropped.

Weekly Checkpoints:

  • Every week, checkpoint problems on Prairielearn will be assigned to test whether if students have understood the topics learned in that particular week.

Prairielearn Assignments:

  • Each homework will have multiple attempts to demonstrate mastery in the material taught in that particular unit. This gets rid of the burden both TAs and CAs have on grading tons of written assignments which takes forever. The lowest homework assignment will be dropped.

Midterm Exams:

  • There will be two midterm exams. The first midterm exam will be on Traffic Theory and the second midterm will be on the remaining topics covered in the course. You are allowed to drop one midterm exam and the highest score will be counted only. The two midterm exams are NOT cumulative and students are allowed to bring an one page cheat sheet (EACH PAGE DOUBLE-SIDED) which can be used as a reference. There will be NO final exam and the second midterm will be scheduled before the finals period.

Mini-Projects (MP):

  • Groups of 4 or 5 students will work on data science and engineering problems to analyze various transportation data using Python and Excel. These are called Mini-Projects (A.K.A MP). Group activities will not only enhance students skills in data computation, but also effective communicative skills necessary to be successful in the modern 21st century transportation academia and industry. Each group activity will be given 2 weeks for completion.

Final Presentations:

  • Groups of 4 or 5 will present on a given topic for a unit in transportation they are interested covered in the course and record a video of their presentations. It can be a topic that they are interested in a group computational problem they have been solving or something else. The winning team of the best presentation will get an extra credit of 1% for each group member in the winning team! Everyone is required to participate in the mini-presentations.

Extra Credits:

  • Numerous extra credit opportunities at instructor's choice.

References: