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Understanding the Interaction of Spatial and Temporal Variables on Cycling Behavior in London Through Machine Learning and Explainable AI
Arthurius, William (2025) Understanding the Interaction of Spatial and Temporal Variables on Cycling Behavior in London Through Machine Learning and Explainable AI.
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| Abstract: | Understanding the factors influencing cycling behavior is essential for proposing data-driven transport policies and infrastructure planning. This study explores how spatial and temporal variables affect cycling activity across Greater London using an interpretable machine learning method. The gradient boosting model (XGBoost) was developed to predict bike count based on temporal conditions (time of day and weather), cycling infrastructure (proximity to facilities and road/lane type), and built environment (distance to city center, density, and elevation). Shapley Additive Explanations (SHAP) were applied for both global and local interpretation of model predictions. Results show that infrastructure variables, such as road type, cycle lane design, and proximity to bike share stations, have the strongest predictive power, followed by built environment variables. Temporal variables, especially peak hours, also contribute to the model's performance. The weather had the least effect due to limited variation in the dataset. SHAP enabled interpretation by ranking variable importance, revealing spatial variations, identifying non-linear effects, and detecting threshold values. These insights informed data-driven planning recommendations at the global, region, and borough levels. This study demonstrates the potential combining machine learning with explainable AI to generate actionable insights. The proposed method offers a transferable framework for understanding cycling behavior and supporting data-driven planning in other cities. |
| Item Type: | Essay (Master) |
| Faculty: | ITC: Faculty of Geo-information Science and Earth Observation |
| Subject: | 55 traffic technology, transport technology, 74 (human) geography, cartography, town and country planning, demography |
| Programme: | Geoinformation Science and Earth Observation MSc (75014) |
| Link to this item: | https://purl.utwente.nl/essays/107905 |
| Export this item as: | BibTeX EndNote HTML Citation Reference Manager |
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