FYI, here are some questions from last year’s HuMob Challenge

• Q. The datasets are not complete (i.e., location observations are not provided for all time steps for all individuals), is this an error?
  • A. No, mobile phone location data is not complete — that makes human mobility prediction difficult! Please try to predict the locations of the individuals labeled ‘999’ in each dataset.
• Q. The GEOBLEU code is computationally slow. How can we speed it up?
  • A. Note that you need to compute the GEOBLEU scores for each day per user, not for the entire 15-day trajectory for each individual. If the computation is slow even in this case, please try multi-thread processing. We will post the example code on the GitHub page soon. Please watch out for updates at (https://github.com/yahoojapan/geobleu)
• Q. Is the DTW score normalized by taking the average score for all steps for each user, and then averaged across all users?
  • A. DTW in the repo is not using normalization. This time we decided to calculate it in a more straightforward way. Essentially, we are treating each prediction step (rather than each user) equally in this competition.
• Q. What is the typical range of values of GEOBLEU and DTW metrics? I just want to know whether the score is in the same ballpark.
  • A. The results obtained by a simple baseline method was around 0.04 for GEOBLEU and around 60 for DTW, tested by the organizers. Details of the simple baseline method and results are outlined on the github readme “Baseline method and results” section, here: https://github.com/yahoojapan/geobleu