Programming for Transport Systems

Instructor information

Module leader: Prof. Natalia Isaenko

Course information

ECTS: 6 credits
Status: Elective
Semester: 1
Hours: 30/18 (lectures/exercises)

Wednesday 12.00 - 14.00

Friday 11.00 - 14.00

Meet room:

Google Classroom: zims5x5


The programming course for transportation engineering introduces students to fundamental programming concepts essential for analyzing transportation data and solving real-world problems. Beginning with basic algorithms and control flow operators, students progress to advanced topics such as data analysis, simulation methods, and optimization algorithms. Through hands-on exercises, students learn to manipulate transportation data, plot graphs, and apply scientific computing techniques using Python. This synthesis of programming skills with transportation engineering principles equips students with practical tools to address complex challenges in transportation engineering.

Syllabus outline

  • Part 1: Foundations of Python Programming
  • Introduction to the Course

    Algorithms and Control Flow Operators


    Data Structures in Python

    Basic Algorithms examples

    Monte Carlo Methods

    Simulating a queuing process

  • Part 2: Data Analysis with Python
  • Scientific Computing with NumPy

    Data Analysis with Pandas

    Gradient Descent Algorithm

  • Part 3: Advanced Algorithms and Data Analysis Techniques
  • Clustering Algorithms

    Simulated Annealing Algorithm

    Spatial Data Analysis

Essential reading list

  • Lecture notes provided by the instructor