Examining active travel behavior through explainable machine learning: Insights from Beijing, China

Abstract

Active travel, namely walking and cycling, is an eco-friendly and socially beneficial mode of sustainable transportation. However, existing research on active travel relies on limited survey data and generalized linear models. To fill the gap, our study integrates large-scale big trip data and data-driven machine learning to simultaneously predict active travel flow and probability. We employ SHapley Additive exPlanation to analyze the nonlinear effects of various characteristics (e.g., travel, socioeconomic, infrastructure, environment) on active travel. Gradient Boosting Decision Tree performs best for both prediction tasks. The overall importance of travel distance is over 50% to the model. Features like crow-fly distance, housing price, point-of-interest density, subway proximity, building area/road density, and urban greenery exhibit pronounced nonlinear effects. Local interpretability analysis reveals the determinants of specific trips, facilitating targeted optimization implications. Our study reveals the drivers and nonlinearities of active travel behavior and aids sustainable transportation planning.

Ganmin Yin

PhD Student
2020 - present

My research interests include Human Mobility, Transportation, Urban Data Mining, Social Sensing and GeoAI.

Zhou Huang

Associate professor

Associate professor of GIScience

Chen Fu

Grad Student
2021 - 2024

My research interests include spatial data analysis and machine learning.

Shuliang Ren

PhD Student
2022 - present

My research interests include Geographical Information Systems, GeoAI, Urban Science, and Urban Data Mining.

Yi Bao

Postdoctoral Researcher
2023 - present
PhD Student
2018 - 2023

My research interests include Geographical Information Systems, Remote Sensing, Urban Data Mining, Deep Learning