Ridesharing has been becoming increasingly popular in urban areas worldwide for its low cost and environmental friendliness. Much research attention has been drawn to the optimization of travel costs in shared rides. However, other important factors in ridesharing, such as the social comfort and trust issues, have not been fully considered in the existing works. In this paper, we formulate a new problem, named Assignment of Requests to Offers (ARO), that aims to maximize the number of served riders while satisfying the social comfort constraints as well as spatial-temporal constraints. We prove that the ARO problem is NP-hard. We then propose an exact algorithm for a simplified ARO problem. We further propose three pruning strategies to efficiently narrow down the searching space and speed up the assignment processing. Based on these pruning strategies, we develop two novel heuristic algorithms, the request-oriented approach and offer-oriented approach, to tackle the ARO problem. We also study the dynamic ARO problem and present a novel algorithm to tackle this problem. Through extensive experiments, we demonstrate the efficiency and effectiveness of our proposed approaches on real-world datasets.

中文翻译：

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社交共享乘车中的要约和请求的有效匹配

乘骑共享（Ridesharing）以其低成本和对环境友好而在全球城市地区变得越来越流行。对于共享乘车中旅行成本的优化已经引起了很多研究关注。但是，在现有的作品中，尚未充分考虑过其他重要因素，例如社会舒适度和信任问题。在本文中，我们提出了一个新问题，即“要约分配”（ARO），目的是在满足社会舒适性约束和时空约束的同时，最大程度地提高服务骑手的人数。我们证明了ARO问题是NP难题。然后，我们为简化的ARO问题提出了一种精确的算法。我们进一步提出了三种修剪策略，以有效地缩小搜索空间并加快分配过程。基于这些修剪策略，我们开发了两种新颖的启发式算法，即面向请求的方法和面向提供的方法，以解决ARO问题。我们还研究了动态ARO问题，并提出了解决该问题的新颖算法。通过广泛的实验，我们证明了我们提出的方法在现实数据集上的效率和有效性。