S37 / Airlines and transportation
Early warning of airline disruption across an airport network
An airline operations team must prioritize flights for early disruption review. Test whether recent network conditions add useful warning of severe arrival delays or cancellations beyond schedule and seasonal patterns.
Planned topic
Proposed design & source
Temporal network features; calibrated classification; event severity
Define a fixed pre-departure prediction cutoff and create schedule, airport-congestion and lagged completed-flight features. Compare calibrated boosting with a simpler logistic model and a network-aware challenger. Use aircraft rotation features only where the assignment is knowable at the cutoff; exclude the target flight's realized delay causes.
Evaluation: Use rolling month/season holdouts and airport-transfer tests. Report rare-event precision-recall, calibration, warning lead time and severe disruptions captured at a fixed review capacity. Separate cancellation from conditional delay severity and audit publication/schedule revision timing.
Boundary: Retrospective BTS records may not reconstruct every live schedule or aircraft assignment. Do not present hindsight features as real-time knowledge or simulated schedule changes as proven delay reduction.
Airline On-Time Performance ↗
S38 / Airlines and transportation
Fleet positioning under uncertain urban trip demand
A mobility operator needs to place a limited fleet where upcoming trips are likely. Compare fixed, historically proportional and forecast-driven allocation under explicit travel and repositioning assumptions.
Planned topic
Proposed design & source
Spatiotemporal forecasting; min-cost flow; stochastic allocation
Choose one TLC vehicle category and consistent date range. Aggregate completed trips into zone-time demand and estimate travel-time distributions from eligible historical trips. Forecast next-period observed pickups using seasonal and spatially pooled models; feed scenarios into a min-cost-flow allocation model.
Evaluation: Hold out later weeks and demand-shift periods. Report zone-level scaled error, interval coverage and peak-zone performance. In a clearly specified simulator, compare unmet observed-request proxies, relocation distance and modeled cost across policies; vary travel-time and fleet-size assumptions.
Boundary: Completed trips omit unserved requests and empty-vehicle movements. Public monthly files also have publication lag. The project is a historical fleet-planning benchmark, not a validated live dispatch system.
NYC TLC Trip Records ↗
S39 / Airlines and transportation
Chicago transit planning through structural demand changes
A transit planning team needs a forecast that adapts when commuting patterns change. Test whether regime-aware models improve daily bus and rail boarding forecasts over a stable seasonal baseline.
Planned topic
Proposed design & source
State-space models; change detection; coherent aggregation
Use CTA's system-level daily bus, rail and total boarding series. Build calendar/day-type features and compare seasonal naive, dynamic regression and state-space models with change detection. Fit change points using training data only; evaluate known disruption periods as stress tests.
Evaluation: Use rolling 7- and 28-day forecast origins spanning stable and changing demand periods. Report scaled error, interval coverage and recovery after shifts. Compare expanding versus recent-window training and evaluate weekday/weekend performance separately.
Boundary: This specific dataset contains daily system totals, not station-hour demand or individual trips. Capacity scenarios cannot justify train-by-train staffing or prove service changes caused ridership growth without additional data.
CTA Daily Boarding Totals ↗
