Context
Technology has many effects on the transportation industry.
Content
We have provided an accurate dataset describing a complete year (from 01/07/2013 to 30/06/2014) of the trajectories for all the 442 taxis running in the city of Porto, in Portugal (i.e. one CSV file named “train.csv”). These taxis operate through a taxi dispatch central, using mobile data terminals installed in the vehicles. We categorize each ride into three categories: A) taxi central based, B) stand-based or C) non-taxi central based. For the first, we provide an anonymized id, when such information is available from the telephone call. The last two categories refer to services that were demanded directly to the taxi drivers on a B) taxi stand or on a C) random street.
Use case
RL-driven fleet re-positioning for a ride-hailing operator
| Example observations | current grid cell (lat, lon snapped to 300 m hex) speed & heading (derived from last 2 GPS points) •occupied flag (binary) clock time (minute-of-week 0-10079) neighbouring grid demand in past 30 min (# pick-ups) remaining fuel range estimate (proxy: time since refuel event) |
| Example actions | 0 = stay 1–6 = move to one of the 6 neighbouring hexes (agent chooses every Δt = 5 min when the cab is empty) (~9 000 hexes → 7 actions/cab each step after masking non-reachable cells) |
| Example reward | + fare_amount earned in next 30 min − 0.2 € × dead-heading_km − 0.05 € × idle_min |