Identifying and Evaluating Cyclist Points of Interest Using Ride Log Data and User Centered Evaluations

Abstract

In cycle tourism, where cyclists can freely experience regional attractions, there exist Cyclist Points of Interest (CPoI) that cyclists prefer. However, these CPoIs are known only among cyclists and are not widely shared as general tourist information. This study proposes a semi-automated method for identifying and evaluating CPoIs using cyclist ride log data. The proposed method consists of three stages: automatic extraction using stop position estimation from GPS ride logs and Density-Based Spatial Clustering of Applications with Noise, facility identification using crowdsourcing, and evaluation using crowdsourcing. To evaluate the effectiveness of the proposed method, we collected ride data from 286 cyclists in Maebashi City, Gunma Prefecture, from April to December 2023, and successfully identified 48 CPoIs and assigned evaluation values to each CPoI. Expert validation of the identified CPoIs revealed high agreement rates: 89.6% for selection validity and 95.8% for the credibility of evaluation results. The detected CPoIs included many facilities often overlooked in conventional tourist destination selection, such as convenience stores, roadside stations, and cyclist-specialized cafes, demonstrating the practical utility of the proposed method.

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code:references.bib

@article{bib:nagayama_cyclistPoI_IEEEAccess,

author={Nagayama, Kazuki and Matsuda, Yuki and Suwa, Hirohiko and Yasumoto, Keiichi},

title={{Identifying and Evaluating Cyclist Points of Interest Using Ride Log Data and User Centered Evaluations}},

journal={IEEE Access},

volume={14},

year={2025},

pages={70060--70072},

doi={10.1109/ACCESS.2026.3686943}

}

https://scrapbox.io/files/6a0536ad5c3c134ed436171a.png

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Keywords

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