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

2025-08-29

AI Summary

This paper addresses the optimal meeting point-of-interest (POI) query problem under multi-user trajectory constraints, aiming to identify a meeting location that minimizes the total additional detour distance incurred by all users. To this end, we propose the Trajectory-based Group Nearest Neighbor (T-GNN) query model—the first of its kind—supporting dynamic detour-path selection and flexible trajectory matching. Methodologically, we design three POI pruning strategies and an efficient real-time query processing algorithm that substantially reduces the search space. Theoretical analysis and extensive experiments on large-scale POI datasets demonstrate the algorithm’s high efficiency and strong scalability: query response time improves by an order of magnitude over baseline approaches. Our solution is thus both theoretically sound and practically deployable for real-time multi-user rendezvous scenarios.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Combinatorial Optimization
• Data Mining & Knowledge Management: Intelligent Query Processing

Application Category

• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Social Networks and Social Media: Social mining and social search on the Web

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.

Problem

• Optimizing meetup locations for users with existing trips
• Minimizing total detour distance for group meetups
• Efficiently computing trip-based group nearest neighbor queries

Innovation

• Trip-based Group Nearest Neighbor query
• Pruning techniques for POI optimization
• Real-time processing algorithm development