# Efficient Computation of Trip-based Group Nearest Neighbor Queries (Full Version)

## TL;DR

This paper introduces the Trip-based Group Nearest Neighbor (T-GNN) query, a novel method to find optimal meetup points that minimize total detour distances for users' trips, with efficient pruning techniques for real-time computation.

## Contribution

The paper proposes a new T-GNN query type, along with pruning techniques and an efficient algorithm for real-time processing, addressing the challenge of trip-based meetup point selection.

## Key findings

- The proposed algorithm effectively reduces search space through pruning.
- The method achieves real-time performance in large-scale scenarios.
- Experimental results demonstrate significant efficiency improvements.

## Abstract

In recent years, organizing group meetups for entertainment or other necessities has gained significant importance, especially given the busy nature of daily schedules. People often combine multiple activities, such as dropping kids off at school, commuting to work, and grocery shopping, while seeking opportunities to meet others. To address this need, we propose a novel query type, the Trip-based Group Nearest Neighbor (T-GNN) query, which identifies the optimal meetup Point of Interest (POI) that aligns with users' existing trips. An individual trip consists of a sequence of locations, allowing users the flexibility to detour to the meetup POI at any location within the sequence, known as a detour location. Given a set of trips for the users, the query identifies the optimal meetup POI (e.g., restaurants or movie theaters) and detour locations from each user's trip that minimize the total trip overhead distance. The trip overhead distance refers to the additional distance a user must travel to visit the meetup POI before returning to the next location in their trip. The sum of these overhead distances for all users constitutes the total trip overhead distance. The computation time for processing T-GNN queries increases with the number of POIs. To address this, we introduce three techniques to prune the POIs that cannot contribute to the optimal solution, and thus refine the search space. We also develop an efficient approach for processing T-GNN queries in real-time. Extensive experiments validate the performance of the proposed algorithm.