ECOD: Employee Carpooling Optimization Dataset

The Employee Carpooling Optimization Dataset (ECOD) contains two synthetic datasets designed for evaluating multi-objective optimization approaches in the context of employee carpooling. The datasets simulate realistic commuting scenarios by defining drivers, passengers, and their attributes, along with distance and travel time matrices.
Data were generated using PyMC for probabilistic modeling of user attributes and OSMnx for street network visualization and shortest path calculations. Each dataset includes:
- Driver data (ID, location, available seats, travel preferences, and route deviations)
- Passenger data (ID, pickup locations, and driver preferences)
- Distance and travel time matrices for drivers and passengers
These datasets are intended to facilitate research on carpooling optimization, enabling reproducibility and benchmarking against alternative solutions.