Holiday Aware, Data Driven Relief Train Planning

Problem Statement

Relief trains are currently planned with limited anticipation of demand peaks caused by holidays and special travel patterns. Planning is largely reactive and relies on historical assumptions, leading to late interventions, inefficient capacity allocation, and missed opportunities to prevent overcrowding.

Proposed Solution

A combined, data driven planning approach that integrates holiday calendars, demand patterns, and operational data with passenger flow insights derived from mobile device trajectory data. The solution enables early identification of peak demand situations and supports proactive, evidence based planning and timing of relief trains.

Required Expert Profiles

• Rail operations and capacity planning
• Demand forecasting and transport planning
• Data analytics using mobility data (e.g. passenger counts, mobile‑trajectory‑based passenger counts)
• Holiday, event, and seasonal demand modeling
• Optional: Data engineering and prototyping (e.g. Python, Jupyter, dashboarding tools)

Challenge Owners

David Chmelik, Steven Sernett, Bertram Wassermann (ÖBB)