Qube Research & Technologies (QRT) was founded on a strong culture of collaboration. With 13 offices worldwide (and growing!), teams frequently travel across continents to meet, connect and work together. This global setup offers rich opportunities for teamwork, and to learn from a diverse set of peers - but also introduces challenges around fairness and sustainability.

The Challenge:#

Colleagues from several offices need to meet in person, but where should the meeting be held? Hosting the meeting in one location involves trade-offs between:

  • Carbon emissions - Inter-office travel produces CO,, especially from long-haul flights.
  • Fairness - Some offices are much further away than others. We want to avoid any one region always taking the longest trips.

Your task is to design a tool or algorithm that provides insights into the best host location (not necessarily at one of our offices) - balancing environmental impact with fairness in travel time.

We’re excited to partner with OAG - a global leader in travel data - who will be providing the travel datasets for this challenge. They are providing flight schedules and CO, emissions data for those flights. You’re also free to augment with open-source data (e.g. pricing, train times, geography) to enrich your analysis.

Input File Structure#

At the end of the Hackathon we will test your solution against a range of unseen scenarios formatted in the following way:

{
	"attendees": {
		"Mumbai": 2,
		"Shanghai": 3,
		"Hong Kong": 1,
		"Singapore": 2,
		"Sydney": 2
	},
	"availability_window": {
		"start": "2025-12-10T09:00:00Z",
		"end": "2025-12-15T17:00:00Z"
	},
	"event_duration": {
		"days": 0,
		"hours": 4
	}
}
  • Attendees - describes how many colleagues will be attending the meeting from each office
  • Availability Window - the range of dates that the meeting can occur between
  • Event Duration - how long the meeting itself needs to be

Your tool can (and ideally should) have interactive controls for playing around with these parameters, but must include a mechanism for ingesting JSON data to test its abilities during the judging phase. We will provide a smaller number of sample scenarios to test your code against whilst developing it.

Outputs#

We want teams to present tools that, given scenarios such as the above, can produce:

  • A chosen host location and supporting metrics.
  • A visualisation showing an intuitive, clear depiction of each office’s travel plans (such as a maps or flow diagrams based on the actual transport options that are available).
  • A short rationale explaining how your method balances the trade-offs.
  • Bonus points for a comparison of candidate cities (e.g. total CO,, median travel time, availability of transport options, etc).
{
	"event_location": "New York",
	"event_dates": {
		"start": "2025-12-10T09:30:00Z",
		"end": "2025-12-11T13:20:00Z"
	},
	"event_span": {
		"start": "2025-12-09T17:30:00Z",
		"end": "2025-12-11T22:27:00Z"
	},
	"total_co2": 125,
	"average_travel_hours": 10.9,
	"median_travel_hours": 5.7,
	"max_travel_hours": 26.3,
	"min_travel_hours": 0.5,
	"attendee_travel_hours": {
		"Mumbai": 20.5,
		"Shanghai": 4.6,
		"Hong Kong": 13.7,
		"Singapore": 2.1,
		"Sydney": 23.9
	},
}
  • Event Location - Location that the meeting will occur in
  • Event Dates - Dates that all attendees are in the chosen location between
  • Event Span - Dates between which the first attendee arrives and the final attendee leaves (the closer to the start and end times the better)
  • Event CO2 - Total CO2 emitted for all attendees travel
  • Attendees Travel Hours - Breakdown of how many hours an attendee from a given office will spend travelling (both ways)

Judging Criteria#

  • Correctness - produces a reasonable output given some input parameters
  • Insight - provides a clear overview of the different trade-offs
  • Creativity - great visualisation or unique fairness metrics
  • Polish - clean, presentation-ready outputs and code

Inspiration#

  • Experiment with different weightings between emissions and fairness.
  • Calculate cost-benefit calculations for proposed locations based on attendee travel costs.
  • Incorporate alternative transport modes (trains, short-haul vs long-haul).
  • Factor in last-mile transit from the airport to the office (our office locations can be found at our website )
  • Create an interactive dashboard or map for users to explore scenarios.
  • Investigate how hotel/flight costs could change the optimal meeting location.

How To Access The Data#

Sign up to our AWS platform using the instructions here .

All data for this challenge is available in the qrt-durhack2025-challenge-data AWS S3 Bucket.

You can view what is in this bucket with the following command:

aws s3 ls qrt-durhack2025-challenge-data --summarize --recursive

Or copy it’s contents to your linux box (recommended for faster access):

cd /shared/
sudo mkdir challenge_data
sudo aws s3 cp s3://qrt-durhack2025-challenge-data /shared/challenge_data --recursive

Only do this once per team as it puts the data in a shared location all users can access - your linux box only has enough space for 1 copy of the data!

To get the workshop python script up and running on the server, run the following commands:

uv pip install polars
source .venv/bin/activate
aws s3 cp s3://qrt-durhack2025-challenge-data/workshop.py ./
python ./workshop.py

Beyond that, it’s entirely up to you…

Collaborate, optimise, and visualise - and help QRT to meet in the middle!

Need Help?#

Speak to Marshall Bradley or Orry Gooberman from QRT. We’ll be walking around the venue, or you can find us on Discord.