Work

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

Year: 2026

Type: article

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.

Source: Applied Geography

DOI: 10.1016/j.apgeog.2026.104052

Authors: Hao Huang, Jin Yeu Tsou, Hao Zheng, Rui Ma, Haochen Shi, Botao Zhang

Institutions: City University of Hong Kong, Zhengzhou University, Guangzhou University, Hong Kong Polytechnic University

Language: English

Topic: Recreation, Leisure, Wilderness Management

Subfield: Social Psychology

Field: Psychology

Domain: Social Sciences

SDG: Sustainable cities and communities

Open Access status: hybrid

Estimated APC paid: $3,220