Computation for Transportation Engineering

Module code: EEP55C23 · Semester: Semester 2

Resources

• Module descriptor (TCD)

Questions by Year

0 of 3 done · tick papers and add notes as you go

• 2024/25
• 2023/24
• 2022/23

Papers open on tcd.ie (sign in to TCD first).

Topics

• Computational mobility models and digital twins for maintenance LO1
• Time-series analysis and anomaly detection LO2–3
• Imaging: traffic sign recognition, passenger flow, multi-task learning LO4–5, LO7
• Machine learning, neural networks, quantum ML overview LO4–5
• Traffic management, simulation, scheduling and optimisation LO9–10
• Railway systems: rolling stock, signalling and operation LO6, LO8

Learning outcomes

1. Build digital twin models for transport maintenance
2. Assess anomaly detection methods for transport systems
3. Describe time-series models for transport operation and maintenance
4. Apply imaging to traffic sign recognition and passenger flow
5. Assess imaging techniques for multi-task learning
6. Describe industry digital tools for rolling stock
7. Assess imaging techniques for multi-task learning (applications)
8. Apply railway signalling principles
9. Build simulation tools for traffic scheduling and optimisation
10. Solve traffic management and control problems

Summarised from the module descriptor, checked November 2026.