Large-scale Ridesharing DARP Instances Based on Real Travel Demand
This dataset presents a set of large-scale 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 based on real 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. The instances and results of two solution methods, the Insertion Heuristic and the optimal Vehicle-group Assignment method, can be found in the dataset. 📄 Paper: arXiv:2305.18859 📁 Data: DOI:10.5281/zenodo.7986103 👩💻 Code: https://github.com/aicenter/Ridesharing_DARP_instances The dataset was presented at the IEEE International Conference on Intelligent Transportation Systems (ITSC 2023) in Bilbao, Bizkaia, Spain, 24-28 September 2023 (Session CON03)
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See the GitHub repository readme.