In recent times, Group Trip Planning Query (henceforth referred to as GTP Query) is one of the well\mbox{-}studied problems in Spatial Databases. The inputs to the problem are a road network where the vertices represent the Point-of-Interests (mentioned as POIs henceforth) and they are grouped into different categories, edges represent the road segments, and edge weight represents the distance and a group of users along with their source and destination location. This problem asks to return one POI from every category such that the aggregated distance traveled by the group is minimized. As the objective is to minimize the aggregated distance, the existing solution methodologies do not consider the individual distances traveled by the group members. To address this issue, we introduce and study the \textsc{Envy Free Group Trip Planning Query} Problem. Along with the inputs of the GTP Query Problem, in this variant, we also have a threshold distance $D$ such that aggregated distance traveled by the group is minimized and for any member pairs the difference between their individual distance traveled is less than equal to $D$. However, it may so happen that a given $D$ value no such set POIs are found. To tackle this issue, we introduce the surrogate problem \textsc{Envy Free Group Trip Planning Query with Minimum Additional Distance} Problem which asks what is the minimum distance to be added with $D$ to obtain at least one solution. For these problems, we design efficient solution approaches and experiment with real-world datasets. From the experiments, we observe that the proposed solution approaches lead to less aggregated distance compared to baseline methods with reasonable computational overhead.
翻译:近些年来, Group Trip Plan Query (以下称为 GTP Query ) 是空间数据库中研究过的问题之一。 问题的投入是一个道路网络, 上面的顶点代表着利益点( 以后称为 POIs ), 它们被分为不同的类别, 边缘代表着路段, 边重量代表着距离, 用户群及其来源和目的地位置。 这个问题要求从每类返回一个 POI, 以便尽可能减少集团所走的总距离。 由于目标是尽量减少总距离, 现有的解决方案并不考虑集团成员所走的距离。 为了解决这个问题, 我们引入并研究\ textc { Envy Free Group Trip planslation Query} 问题。 除了GTP Query 问题的投入外, 我们还有一个门槛值的门槛距离( $) $, 并且每个成员所选择的距离之间的距离是最小的。 为了最小的距离( 美元) 相对于实际设计距离, 我们所找到的底点, 方向可能降低。 然而, 我们所找到的路径到额外的轨道, 我们所找到的路径可能要降低。