自転車ルートを個別最適化するマップ技術を開発(BikeButler map creates personalized routes for riders based on preferences)

2026-04-28 ワシントン大学((UW)

University of Washingtonの研究チームは、サイクリスト向けに最適な走行ルートを提案する地図ツール「Bike Butler」を開発した。坂道の傾斜、交通量、道路の安全性、景観など複数の要素を統合し、利用者の好みに応じたルートを提示できるのが特徴である。従来の最短距離重視のナビとは異なり、安全性や快適性を重視した経路選択が可能となる。シアトルでの実証では、利用者の満足度向上や自転車利用促進への効果が示された。都市の持続可能な交通手段の普及や健康増進にも寄与することが期待される。

自転車ルートを個別最適化するマップ技術を開発(BikeButler map creates personalized routes for riders based on preferences)
BikeButler is a demo web app that lets users find personalized bike routes in Seattle. Cyclists plug in their destination and origin — just like in other mapping apps — and can then toggle sliders for eight attributes to create personalized route options. Above is the interface. The images on the right show different segments of the route.

<関連情報>

BikeButler:オープンデータとVLMベースのストリートビュー画像分析を用いた、パーソナライズされた状況に応じた自転車ルート案内ツール BikeButler: A Personalized, Context-sensitive Bike Routing Tool using Open Data and VLM-based Analyses of Street View Imagery

Jared Hwang, John S. O’Meara, Zeyu Wang, Jasmine Zhang, Jon E. Froehlich
CHI ’26: Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems  Published: 13 April 2026
DOI:https://doi.org/10.1145/3772318.3791292

Abstract

Urban cycling benefits personal wellbeing, public health, and global sustainability. While current tools such as Google and Apple Maps provide bike route recommendations, they do not account for a person’s dynamic context (e.g., commuting, recreation). We introduce BikeButler, a personalized, context-sensitive bicycle route generation tool that enables users to generate, compare, virtually preview, and iteratively customize bike routes via custom profiles that encode seven bikeability features, including bike lane existence, slope, vegetation, and surface quality—fusing data from OpenStreetMap, open government data, and a custom VLM-based analysis of Street View images. To design BikeButler, we employed a human-centered, iterative approach starting with formative interviews and culminating in a user study (N=16). Our findings demonstrate that bike routing preferences change as a function of context, that BikeButler enables users to quickly create and iterate context-sensitive routes, and that generated routes differ significantly from Google Maps bike routing, reinforcing the importance of personalization.

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