Large-scale Ridesharing DARP Instances Based on Real Travel Demand

This repository 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. The dataset and methodology used to create it are described in the paper Large-scale Ridesharing DARP Instances Based on Real Travel Demand.

本仓库提供了一系列大规模接送问题（Dial-a-Ride Problem, DARP）实例。这些实例被构建为一套标准化的共乘接送问题数据集，旨在用于基准测试与不同求解方法的性能对比。本数据集基于美国芝加哥、纽约市、华盛顿哥伦比亚特区三座城市的真实出行需求与真实出行时间数据构建，包含选定时段内的真实出行请求、带载客容量限制的车辆位置信息，以及各城市内任意地点间的真实最短出行时长。本数据集同时收录了两类求解方法的求解结果与对应实例：插入启发式算法（Insertion Heuristic）以及最优车辆分组分配算法（optimal Vehicle-group Assignment method）。该数据集的构建细节与所用方法已在论文《基于真实出行需求的大规模共乘接送问题数据集》（Large-scale Ridesharing DARP Instances Based on Real Travel Demand）中详细阐述。