Design and Optimization of Transport Networks (DOTNET)

Course Overview

This graduate-level course teaches multimodal transport network modeling, equilibrium analysis, and network design optimization for strategic planning. Students learn to evaluate transport infrastructure changes using computational models and real-world case studies implemented in Python.

Instructor: Bahman Madadi
Guest Lecturer: Angelo Furno (HDR)
Format: 10 sessions including lectures, hands-on practice, and project presentations

Learning Objectives

Upon completion, students will be able to:

  • Identify characteristics and requirements of different travel modes in multimodal systems
  • Describe and assess different approaches for modeling multimodal mobility systems
  • Perform accessibility analysis and evaluate transport network resilience
  • Apply network equilibrium models for traffic assignment and mode choice
  • Design and optimize multimodal transport networks for strategic planning
  • Implement complete transport network analysis workflows using Python

Topics Covered

  • Multimodal Transportation: Mode characteristics, integration, and mobility hubs
  • Network Equilibrium Analysis: Traffic assignment for cars, bikes, and public transit
  • Mode Choice Modeling: Disaggregate and aggregate choice models
  • Transport Network Resilience: Vulnerability assessment and robustness analysis
  • Accessibility Analysis: Spatial and temporal accessibility metrics
  • Network Design: Infrastructure evaluation and optimization methods
  • Python Implementation: Complete workflow from data to results using computational models

Course Structure

The course combines:

  • Theory Sessions: Fundamental concepts and methodologies
  • Hands-on Practice: Weekly Python exercises (TP) with real-world datasets
  • Guest Lectures: Expert insights on network resilience
  • Quizzes: Individual assessments (50% of grade)
  • Course Project: Group project on transport network design and evaluation (50% of grade)

Tools & Technologies

  • Programming: Python with focus on transport modeling libraries
  • Software: Network analysis and optimization packages

All course materials, including lecture slides, Python notebooks, and datasets, are provided via Moodle.