Exploring the use of Machine-learning and Non-Radial DEA for Transit efficiency score prediction

A data-driven bus efficiency prediction model is a useful tool for transport planners to optimize the current and plan efficient new routes. This study proposes a novel approach for predicting efficiency scores by leveraging non radial DEA model and machine learning (ML) techniques. A labeled dataset is developed using a non-radial DEA method that considers interrelationships between operational and service efficiency and the selected features. Two machine learning models, Linear Regression (LR) and Support Vector Regression (SVR) are trained on the labeled dataset. The trained model can be used to predict the efficiency scores of a new bus routes based on decision-makers’ preferences on input parameters and without requiring a full DEA analysis. The methodology is experimented on CyRide, a real-world dataset provided by the Ames transit Agency. The effectiveness of both models in predicting efficiency is also evaluated using $R^{2}$, MSE, and residual plots with detailed discussion on exploration analysis of selected features and overall efficiency score. The proposed methodology can be generalized on any bus route dataset and used by transportation authorities for improved decision-making.