Ridesharing services have been becoming a prominent solution to reduce road traffic congestion and environmental pollution in urban areas. Existing ridesharing services fall apart in ensuring the social comfort of the riders. We formulate a Community aware Ridesharing Group Set (CaRGS) query that satisfies the spatial and social constraints of the riders and finds a set of ridesharing groups with the maximum number of served riders. The CaRGS query utilizes user social data in community levels to ensure user privacy. We show that the problem of finding CaRGS query answer is NP-Hard and propose two heuristic approaches: a hierarchical approach and an iterative approach to evaluate CaRGS queries. We evaluate the effectiveness, efficiency, and accuracy of our solution through extensive experiments using real datasets and present a comparative analysis among the proposed algorithms.

拼车服务已成为减少城市地区道路交通拥堵和环境污染的重要解决方案。现有的乘车共享服务无法确保乘客的社交舒适度。我们制定了一个社区感知乘车共享组集（CaRGS）查询，该查询满足乘客的空间和社会约束，并找到一组具有最大服务乘客数量的乘车共享组。CaRGS 查询利用社区级别的用户社交数据来确保用户隐私。我们表明寻找 CaRGS 查询答案的问题是 NP-Hard 的，并提出了两种启发式方法：分层方法和迭代方法来评估 CaRGS 查询。我们通过使用真实数据集的大量实验来评估我们解决方案的有效性、效率和准确性，并对所提出的算法进行比较分析。