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: https://meet.google.com/qfp-ducy-bhr

Google Classroom: flurico

Objectives

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

Introduction to the Course

Algorithms and Control Flow Operators

Functions

Data Structures in Python

Basic Algorithms examples

Monte Carlo Methods

Simulating a queuing process

Scientific Computing with NumPy

Data Analysis with Pandas

Gradient Descent Algorithm

Clustering Algorithms

Simulated Annealing Algorithm

Spatial Data Analysis

  • Part 1: Foundations of Python Programming
  • Part 2: Data Analysis with Python
  • Part 3: Advanced Algorithms and Data Analysis Techniques

Essential reading list

  • Lecture notes provided by the instructor