The Challenge: Multi-City Prediction

The challenge takes place in 4 metropolitan areas (cities A, B, C, D), somewhere in Japan. Each area is divided into 500 meters x 500 meters cells, which span a 200 x 200 grid. The human mobility datasets contain the movement of individuals across a 75-day period, discretized into 30-minute intervals and 500-meter grid cells (see Figure below).

The task is to predict the movement of a subset of individuals in cities B, C, and D, during days 61 to 75 (orange colored parts), using movement data of individuals in city A (from day 1 to 75) and cities B, C, D (from day 1 to 60) (blue colored parts), as shown in the following Figure. 

Not all cities’ data are required to be used for prediction. For instance, to predict city B’s movement from days 61 to 75, one can just use the movement patterns in city B between 1 to 60 (bold arrow). Using data from other cities (e.g., city A) may or may not improve the prediction accuracy!  

While the name or location of the city is not disclosed, the participants are provided with points-of-interest (POIs; e.g., restaurants, parks) data for each grid cell (85-dimensional vector) as supplementary information (which is optional for use in the challenge). –> added here: https://zenodo.org/records/13237029

Evaluation Metrics

The predicted human movement trajectories will be evaluated against the actual trajectories and the accuracy using the GEO-BLEU metric [2] as well as Dynamic Time Warping (DTW) [3]. Python implementations of the evaluation metrics will be provided on Yahoo Japan’s GitHub page (https://github.com/yahoojapan/geobleu).

Submissions will be ranked for each metric, and the top 10 teams will be decided based on the two rankings. We recommend the teams try to optimize for both metrics.