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首页> 外文期刊>Geoinformatica: An international journal of advances of computer science for geographic >Efficient matching of offers and requests in social-aware ridesharing
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Efficient matching of offers and requests in social-aware ridesharing

机译：在具有社交意识的拼车服务中高效匹配报价和请求

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摘要

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.
机译：拼车因其低成本和环保性而在全球城市地区越来越受欢迎。许多研究都关注共享乘车的旅行成本的优化。然而，在现有工程中，共享拼车的其他重要因素，例如社会舒适度和信任问题，并未得到充分考虑。在本文中，我们提出了一个新问题，称为“请求到报价的分配”（ARO），旨在最大限度地提高服务乘客的数量，同时满足社会舒适度约束和时空约束。我们证明了 ARO 问题是 NP 困难的。然后，我们提出了一个简化的ARO问题的精确算法。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处理速度。基于这些剪枝策略，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产品的方法，以解决ARO问题。我们还研究了动态ARO问题，并提出了一种新的算法来解决这个问题。通过广泛的实验，我们证明了我们提出的方法在真实世界数据集上的效率和有效性。

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