Hack-A-Fleet v2.0

This is the repository containing Python tools and datasets for Hack-A-Fleet v2.0.

TLDR;

The file ferry_trips_data.csv contains data of multiple trips made by 5 ferries owned by Färjerederiet. The file utils.py contains functions that you can use to fetch and work with data from PONTOS-HUB; the file examples.ipynb shows how.

The challenge

The challenge is to develop new tools or create new information/knowledge/insights that can help Färjerederiet to achieve its Vision 45, becoming climate neutral by 2045 at the latest, using a real-world dataset from a subset of Färjerederiets fleet.

The real-world dataset is ferry_trips_data.csv. Use this data as a start point, but feel free to complement and/or expand it by using historical and real-time data in PONTOS-HUB or from any other sources.

File descriptions

• ferries.json: A JSON file containing information of the ferries owned by Färjerederiet that share their data to PONTOS-HUB. The pontos_vessel_id key-value pair can be use for querying the REST-API of PONTOS-HUB.
• ferry_trips_data.csv: A CSV file containing records of trips made by 5 ferries owned by Färjerederiet. See the section "Ferry trips" below for more information about the contents.
• ferry_route_descriptions.md: Descriptions of the ferry routes in the file ferry_trips_data.csv.
• schedures/*.pdf: Schedules for the ferry routes.
• utils.py: A Python module containing utility functions to fetch vessel data from PONTOS-HUB, manipulate it, and visualize it.
• examples.ipynb: A Jupyter notebook containing examples of the useage of the functions in utils.py.
• excel/ferry_trips.xlsx: An Excel file containing records of trips made by 5 ferries owned by Färjerederiet. Used as an input for the script exel/extend_ferrytrips.py.
• excel/extend_ferry_trips.py: A Python script that extends the trip information of excel/ferry_trips.xlsx using the data in PONTOS-HUB (e.g. distance and fuel consumption). Generates the file ferry_trips_data.csv. Uses data averaged within a 5 seconds time bucket.

Get Started with the Python tools

1. Clone the repository or download all the files (see Releases) to your computer.

2. If you do not have a TOKEN to access the data in PONTOS-HUB, get one here.

3. Create a file called .env and save it the root directory (i.e. the same directory that contains utils.py). The content of the file must be:

PONTOS_TOKEN=<Your PONTOS access token>

This file is read by utils.py when loaded so that the requests to PONTOS-HUB are authorized.

5. Create a virtual environment (e.g. using venvor conda).

6. Install all the dependencies pip install -r requirements.txt.

7. Take a look at examples.ipynb.

Ferry trips

The file ferry_trips_data.csv contains data from 5 ferries owned and operated by Färjerederiet:

• Fragancia
• Jupiter
• Merkurius
• Nina
• Yxlan

The data corresponds to the time period between 2023-03-01 and 2024-02-29. PONTOS-HUB launched on 2023-04-30, so the data contains datapoints corresponding to a period for which PONTOS-HUB has no data.

Each row in the ferry_trips_data.csv files contain fields describing 2 trips between two terminals:

- An `outbound` trip from the departure terminal to the arrival terminal.
- An `inbound` trip from the arrival terminal to the departure terminal.

The suffixes outbound and inbound in the field names indicate to which trip does the field corresponds to.

Here follows a description of some of the fields as most of them are self-explanatory:

Original fields:

• time_departure: Time of departure for the outbound trip as recoreded by Färjerederiet. Given in Central European Time (CET).
• vehicles_left_at_terminal_outbound/inbound: Number of vehicles left at the terminal on departure for the outbound/inbound trip. Estimated by the crew.
• trip_type: One of the following types of trip:
  • ordinary- Ordinary trip.
  • doubtful – An "ordinary" trip that does not match the timetable.
  • extra – An extra trip by an additional ferry that does not follow the timetable.
  • proactive – A trip made before the ordinary trip to stay ahead, comparable to an extra trip.
  • doubling – An extra trip between two regular trips to take car of vehicles left behind in the termial.
• tailored_trip: A special trip for vehicles with dangerous cargo. (1: True, 0: False).
• passenger_car_equivalent_outbound/inbound: The total number of vehicles in a outbound/inbound trip as Passenger Car Equivalent (PCE) according to the following conversion rules:
  • Length 0-6 meters (e.g. car): 1 PCE
  • Length 6-12 meters (e.g. lorry or car with trailer): 2.5 PCE
  • Length 15-24 meters (e.g. lorry with trailer): 4.5 PCE
  • Bus: 9 PCE
  • Other large vehicles (e.g. cranes, haversters): 9 PCE

Additional fields calculated with data from PONTOS-HUB (might not always contain values depending on data availability):

• distance_outbound/inbound_nm: Approximate distance travelled in the outbound/inbound trip. Calculated from PONTOS-HUB data and given in nautical miles.
• fuelcons_outbound/inbound_l: Approximate fuel consumption in the outbound/inbound trip. Calculated from PONTOS-HUB data and given in liters.
• start_time_outbound/inbound: Approximate start time of the outbound/inbound trip. Calculated from PONTOS-HUB data and given in CET.
• end_time_outbound/inbound: Approximate end time of the outbound/inbound trip. Calculated from PONTOS-HUB data and given in CET.