As ridesourcing continues to grow in popularity, being able to accurately predict the demand for it is essential for effective land-use and transportation planning and policymaking. In this research project, we show that using random forest, a machine learning model that can automatically capture nonlinear relationships and interactive effections, can significantly improve predictive accuracy compared to the traditional statistical model.
Key words: random forest, machine learning, travel demand prediction
Yan, X., Liu, X., Zhao, X. (2020). Using machine learning for direct demand modeling of ridesourcing services in Chicago. [Download Preprint]. Journal of Transport Geography, 83, 102661.