Large-scale Ridesharing DARP Instances Based on Real Travel Demand

This dataset presents a set of large-scale ridesharing Dial-a-Ride Problem (DARP) instances. The instances were created as a standardized set of ridesharing DARP problems for the purpose of benchmarking and comparing different solution methods.

The instances are using actual past demand and realistic travel time data from 3 different US cities, Chicago, New York City, and Washington, DC. The instances consist of real travel requests from the selected period, positions of vehicles with their capacities, and realistic shortest travel times between all pairs of locations in each city.

Unlike the instances commonly used in the ridesharing DARP research, the presented instances use the latest demand data from different cities.

The dataset also contains the results of two baseline solution methods, the Insertion Heuristic, and the optimal Vehicle-group Assignment method.