FIFA World Cup 2026 Public Transport Demand Prediction Data

Data supporting a hierarchical framework for predicting acute, intra-day public transport demand surges during mega-events. The dataset was created by Juan Angel Lucio Rojas and relates to FIFA World Cup 2026 host cities Mexico City and the New York/New Jersey metropolitan area. It was last updated on May 27, 2026.

Use Cases

• Training machine learning models to predict station-level ridership profiles based on event descriptions.
• Benchmarking graph neural network performance against empirical demand estimation methods.
• Analyzing the impact of past soccer events on public transit demand in major metropolitan areas.
• Forecasting demand shocks for future mega-events in host cities.

Strengths

• The description includes a specific performance metric: a Sentence-Encoder-augmented GNN reduced mean absolute error by 67.5% over a baseline GNN in cross-validation.
• The dataset is associated with a published research framework and methodology.
• The data is licensed under CC-BY-4.0, allowing for open use and modification.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

Juan Angel Lucio Rojas via figshare

Collection Method

Likely combines empirical demand estimation from past events with machine learning model outputs.

Freshness

Last updated 2026-05-27 20:06:23; freshness should be verified.

Geography

Mexico City (CDMX) and the New York/New Jersey metropolitan area