Data-Driven Analysis of Cycling Behavior and Determinants of Bicycle Use Based on a Field Survey in Tehran, Iran

Promoting cycling is an essential component of sustainable urban transportation, particularly in developing countries where bicycle use remains limited. This study proposes a two-step machine-learning framework to identify the predictors associated with bicycle use and cycling popularity in Tehran, Iran. A questionnaire survey was conducted among 1027 respondents, of whom 971 valid responses were retained for analysis. In the first step, the entire sample, including cyclists and non-cyclists, was analyzed using Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN) models to predict bicycle use. In the second step, the analysis focused exclusively on existing cyclists. A Cycling Popularity Index (CPI) was developed and classified into three levels, and ten machine-learning algorithms were evaluated. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was incorporated into model development. The results showed that Random Forest achieved the highest predictive performance in the first step, with bicycle-sharing availability, access to private cars, traffic congestion, occupation, proximity to public transportation, and social norms identified as the strongest predictors of bicycle use. In the second step, KNN-SMOTE achieved the best overall predictive performance, while Permutation Feature Importance identified occupation, age, social norms, perceived cycling safety, and physical condition as the most influential predictors of cycling popularity. The proposed framework demonstrates the value of combining multiple machine-learning algorithms with class-balancing techniques for analyzing imbalanced transportation datasets and provides a transferable methodology for similar studies in developing cities.