Examining active travel behavior through explainable machine learning: Insights from Beijing, China
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, the authors' study integrates large-scale big trip data and data-driven machine learning to simultaneously predict active travel flow and probability. The authors 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. The authors' study reveals the drivers and nonlinearities of active travel behavior and aids sustainable transportation planning.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/13619209
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Supplemental Notes:
- © 2023 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
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Authors:
- Yin, Ganmin
- Huang, Zhou
- 0000-0002-1255-1913
- Fu, Chen
- Ren, Shuliang
- Bao, Yi
- Ma, Xiaolei
- Publication Date: 2024-2
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: 104038
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Serial:
- Transportation Research Part D: Transport and Environment
- Volume: 127
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 1361-9209
- Serial URL: http://www.sciencedirect.com/science/journal/13619209
Subject/Index Terms
- TRT Terms: Decision trees; Machine learning; Nonmotorized transportation; Sustainable transportation; Travel behavior
- Geographic Terms: Beijing (China)
- Subject Areas: Pedestrians and Bicyclists; Planning and Forecasting; Safety and Human Factors;
Filing Info
- Accession Number: 01904847
- Record Type: Publication
- Files: TRIS
- Created Date: Jan 18 2024 11:37AM