Modeling hiking speed via interpretable machine learning: Uncovering nonlinear impacts of multidimensional environmental factors

Q: What does this research mean for the field?

Slope is the most influential environmental factor affecting hiking speed, while road type, altitude, and precipitable water exert significant nonlinear impacts on trail mobility. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

Hao Huang; Jin Yeu; id_orcid 0000-0002-7682-4327 TSOU; Hao Zheng; Rui Ma; Haochen Shi; Botao Zhang

doi:10.1016/j.apgeog.2026.104052

May 27, 2026Applied GeographyOpen Access

Modeling hiking speed via interpretable machine learning: Uncovering nonlinear impacts of multidimensional environmental factors

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Key Points

The research aims to model hiking speed based on various environmental factors, focusing on their nonlinear impacts.
Utilized LightGBM to predict segment speed using multi-source geodata.
Conducted SHAP analysis to assess interactions and thresholds among environmental variables.
Analyzed hiking trails in Hong Kong.
Slope is identified as the most influential factor affecting hiking speed.
Road type, altitude, and precipitable water significantly impact mobility in a nonlinear manner.
Insights from the findings can inform trail management and weather-responsive planning.

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Implication

Uncovers nonlinear impacts of environmental factors on hiking speed, suggesting improved trail management.

Abstract

• Slope is the most influential variable affecting hiking speed in Hong Kong trails. • LightGBM best predicts segment speed from multi-source geodata. • Road type, altitude, and precipitable water show significant nonlinear impacts on mobility. • SHAP analysis uncovers the threshold and interaction among environmental variables. • Findings provide actionable insights for trail management and weather-responsive planning.

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Cite This Study

Huang et al. (2026) studied this question.

synapsesocial.com/papers/6a1a7e6a0307b78509431138 https://doi.org/10.1016/j.apgeog.2026.104052

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Modeling hiking speed via interpretable machine learning: Uncovering nonlinear impacts of multidimensional environmental factors

Key Points

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Implication

Abstract

Discussion

Cite This Study

Modeling hiking speed via interpretable machine learning: Uncovering nonlinear impacts of multidimensional environmental factors

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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