Efficient kNN Search in Public Transportation Networks

Public transportation plays a vital role in mitigating traffic congestion and reducing carbon emissions. The Top-k Nearest Neighbor (kNN) search in public transportation networks is a fundamental problem in location-based services, which aims to find k nearest objects from a given query point. The traditional method, Dijkstra's algorithm has been employed to tackle the kNN problem, however, it is notably inefficient in processing queries. While other works precompute an index to speed up query processing. However, they are still slow in processing queries. Furthermore, they cannot scale to large graphs due to their reliance on resource-intensive path indexes. To address these limitations, we introduce a novel index-based approach that utilizes a simple yet effective index structure to handle kNN queries with a near-optimal time complexity. The index does not rely on a path index, making it efficient to construct and scalable to large graphs. Extensive experiments are conducted on real-world datasets to demonstrate the efficiency and scalability of our approach. The results show that our approach outperforms existing solutions by up to four orders of magnitude in query processing and two orders of magnitude in index construction.