Rules

You may submit 5 submissions every day and 50 in total in the development phase.

Accuracy

Submitted results are evaluated by weighted accuracy. As test trajectories are trimmed in its beginning and end with random time period (5 to 60 seconds), prediction becomes harder for longer trimming. Therefore, the period of the trajectory is the weight.

More specifically, w_i is weight of i-th trajectory, and pred_s_i and pred_g_i are predicted start and goal locIDs, and gt_s_i and gt_g_i are corresponding ground truth locIDs, of i-th trajectory.

• 1 - sum_i (w_i int((pred_s_i == gt_s_i) and (pred_g_i == gt_g_i)) ) / sum_i w_i

where int(true)=1 and int(false)=0. It is 0 if all predictions are wrong, while 1 if perfect.

Submission Format of the result

There are 234 test trajectory, and classification results should be a 234-line text file, and each line contains two digits (from 0 to 8) for each of start and goal locations of the test trajectory of the line.

Each line has two-digit prediction of the corresponding test trajectory; that is, line 0 is the prediction of start and goal of test trajectory 000.csv. Each of two predicted location IDs (start and goal), separated by a comma, is one digit corresponding to goal_info.csv. If the trajectory 000.csv is predicted as it starts from locID 5 and arrives to locID 3, then the line is "5, 3".

Here is an example:

5, 3
0, 2
1, 3
0, 1
2, 1
8, 0
7, 6

• The name of this file should be "y_submission.txt" in the zip file to be uploaded.
• You need to zip the file to submit. (Do not put it in a folder in the zip file, do not include any folders. The file must be in the top in the zip file. ) Therefore the uploaded filename maybe "y_submission.txt.zip", or any zip file of the following contents:
  • y_submission.txt
    • anyfilename.zip/
• On linux or mac, the command "zip y_submission.txt.zip y_submission.txt" is enough.

Prizes

Prize

The winner will recieve the following prizes;

• First place: 100USD (tentative)

Note that, to recieve the prizes, the winners must attend the workshop below.

Travel grant

In addition, the first winner will recieve

• Flight/surface ticket (+ hotel for a few days) to Kyoto, one of the most beautiful cities in Japan, for attending the award seremony and presenting the method in the poster session of the workshop;
  • Workshop: International Workshop on Behavior analysis and Recognition for knowledge Discovery (BiRD 2019) in conjunction with the IEEE International Conference on Pervasive Computing and Communications (PerCom 2019), March 11-15, 2019, Kyoto, Japan
  • Date: one day of 11-15 March 2019
  • Venue: Kyoto International Conference Center (ICC Kyoto), Kyoto, Japan
• Flight/surface ticket (+ hotel for a few days) to Kyoto, one of the most beautiful cities in Japan especially in autumn, for attending the award seremony and presenting the method in the poster session of our symposium.
• Date: 5-6 September 2018
• Venue: Hardy Hall, Kanbai-kan, Imadegawa Campus, Doshisha University, Kyoto, Japan
• website: Symposium on Systems Science of Bio-Navigation 2018
• find it on Google map

Note that

• In case of team submission, the travel grant goes to one person only who is a representative.
• The prize will go to the next winner if the first winner withdraws the attendance and the poster presentation for any reaasons (e.g., personal or VISA issues).
• We will provide an invitation letter and the winner have to obtain a VISA by himself/herself.

Terms and Conditions

This challenge is governed by the general ChaLearn contest rules.

Task

For each trajectory, predict start and goal location IDs from pre-defined locations

File format

train and test trajectories

A single CSV file (000.csv, 001.csv, ...) contains a trajectory, and each line represents the information of a location of a smartphone. In addition to longitude and latitude, some other information is provided;

• lat (float): latitude
• lon (float): longitude
• tripID (int): ID of this trajectory. Note that values of this field is the same in the same trajectory csv file.
• elapsedTime_sec (float): elapsed time (in second) from the beginning of this trajectory

The first line has the header (strings). Fields are separated by a single comma.

Here is an example:

lat,lon,tripID,elapsedTime_sec
35.157885,136.926141,1,0.0
35.15775299999999,136.92623033333334,1,8.082
35.15777023076923,136.92619484615386,1,10.118
35.15771669230769,136.92623269230768,1,12.017
35.15771669230769,136.92623269230768,1,14.019

• Different trajectories have different number of locations.
• Time intervals between successive two locations are about few seconds when the smartphone catches BLE beacon signals well, otherwise interval may vary up to tens of seconds.
• Trajectories in the training and test sets are in the same format.
• Ground truth information for training trajectories are given in a separate file.

Note that first and last few seconds (uniform in 5 to 60 seconds) of test trajectories are omitted (but train trajectories are not omitted). The task is hence to infer the future and past of test trajectories and predict the goal and start.

start and goal locations: goal_info.csv

A single txt file of locations of the pre-defined goals.

• LocID (int): goal ID digit (from 0 to 8) to be predicted for test trajectory files.
• lat (float): latitude
• lon (float): longitude

The first line has the header (strings). Fields are separated by a single comma.

trajectoy info: train_trip_info.csv and test_trip_info.csv

Each txt file describes trips (trajectories).

• tripID (int): ID of a trajectory.
• duration (float): duration (in minute) of this trajectory.
• startLocID (int): goal ID digit (from 0 to 8) from which this trajectory starts.
• endLocID (int): goal ID digit (from 0 to 8) to which this trajectory reaches. This filed is removed from test_trip_info.csv.
• age (int): age of the user of the trajectory. There are missing values.
• gender (char): male (m), female (f). There are missing values.

The first line has the header (strings). Fields are separated by a single comma.

Here is an example of train_trip_info.csv:

tripID,duration,startLocID,destLocID,age,gender
1,7.696116666666667,3,4,26,m
5,13.35055,5,1,22,m
10,2.3670333333333335,4,6,19,f
11,3.53645,2,0,16,f
13,3.07525,4,6,21,m
15,4.80535,5,1,26,m

Note that test_trip_info.csv has only three fields: tripID, age, and gender. Here is an example:

tripID,age,gender
2,50,f
3,20,m
6,7,m
9,23,m