Accurately predicting the real-life performance of algorithms solving the Dial-a-Ride Problem (DARP) in the context of Mobility on Demand (MoD) systems with ridesharing requires evaluating them on representative instances. However, the benchmarking of state-of-the-art DARP solution methods has been limited to small, artificial instances or outdated non-public instances, hindering direct comparisons. With the rise of large MoD systems and the availability of open travel demand datasets for many US cities, there is now an opportunity to evaluate these algorithms on standardized, realistic, and representative instances. Despite the significant challenges involved in processing obfuscated and diverse datasets, we have developed a methodology using which we have created a comprehensive set of large-scale demand instances based on real-world data. These instances cover diverse use cases, one of which is demonstrated in an evaluation of two established DARP methods: the insertion heuristic and optimal vehicle-group assignment method. We publish the full results of both methods in a standardized format. The results show significant differences between areas in all measured quantities, emphasizing the importance of evaluating methods across different cities.

翻译：准确预测算法在具有拼车功能的按需出行（MoD）系统中解决拨号乘车问题（DARP）的实际性能，需要在代表性实例上进行评估。然而，现有最先进的DARP求解方法的基准测试仅限于小型人工实例或过时的非公开实例，阻碍了直接比较。随着大型MoD系统的兴起以及美国许多城市开放出行需求数据集，现在有机会在标准化、真实且具有代表性的实例上评估这些算法。尽管处理混淆且多样化的数据集存在重大挑战，我们开发了一种方法论，利用该方法创建了一套基于真实世界数据的全面大规模需求实例。这些实例涵盖多种使用场景，其中一种场景通过对两种已建立的DARP方法（插入式启发式算法和最优车辆组分配方法）的评估进行了展示。我们以标准化格式发布了两种方法的完整结果。结果显示了所有测量量在不同区域间的显著差异，强调了在不同城市间评估方法的重要性。