# k-Nearest Neighbors on Road Networks: A Journey in Experimentation and In-Memory Implementation

arXiv: https://arxiv.org/abs/1601.01549 · HTML: https://mlsanity.com/paper/1601.01549/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jan 2016.

## Built on by (most cited first, 4 of 4)

- [EMDS-6: Environmental Microorganism Image Dataset Sixth Version for Image Denoising, Segmentation, Feature Extraction, Classification and Detection Methods Evaluation](https://mlsanity.com/paper/2112.07111/) (2021)
- [No-Reference Point Cloud Quality Assessment via Weighted Patch Quality Prediction](https://mlsanity.com/paper/2305.07829/) (2023)
- [End-to-end Hyperspectral Image Change Detection Network Based on Band Selection](https://mlsanity.com/paper/2307.12327/) (2023)
- [COL-Trees: Efficient Hierarchical Object Search in Road Networks](https://mlsanity.com/paper/2601.22183/) (2026)

## Abstract (arXiv)

A k nearest neighbor (kNN) query on road networks retrieves the k closest points of interest (POIs) by their network distances from a given location. Today, in the era of ubiquitous mobile computing, this is a highly pertinent query. While Euclidean distance has been used as a heuristic to search for the closest POIs by their road network distance, its efficacy has not been thoroughly investigated. The most recent methods have shown significant improvement in query performance. Earlier studies, which proposed disk-based indexes, were compared to the current state-of-the-art in main memory. However, recent studies have shown that main memory comparisons can be challenging and require careful adaptation. This paper presents an extensive experimental investigation in main memory to settle these and several other issues. We use efficient and fair memory-resident implementations of each method to reproduce past experiments and conduct additional comparisons for several overlooked evaluations. Notably we revisit a previously discarded technique (IER) showing that, through a simple improvement, it is often the best performing technique.