Ensemble Convolutional Neural Networks for Mode Inference in Smartphone Travel Survey
This work addresses mode inference for urban planning and transportation research, but it is incremental as it applies existing ensemble techniques to a specific dataset.
The paper tackled transportation mode classification from smartphone travel survey data by developing ensemble Convolutional Neural Networks, achieving an accuracy of 91.8% with a Random Forest meta-learner, surpassing other methods and literature benchmarks.
We develop ensemble Convolutional Neural Networks (CNNs) to classify the transportation mode of trip data collected as part of a large-scale smartphone travel survey in Montreal, Canada. Our proposed ensemble library is composed of a series of CNN models with different hyper-parameter values and CNN architectures. In our final model, we combine the output of CNN models using "average voting", "majority voting" and "optimal weights" methods. Furthermore, we exploit the ensemble library by deploying a Random Forest model as a meta-learner. The ensemble method with random forest as meta-learner shows an accuracy of 91.8% which surpasses the other three ensemble combination methods, as well as other comparable models reported in the literature. The "majority voting" and "optimal weights" combination methods result in prediction accuracy rates around 89%, while "average voting" is able to achieve an accuracy of only 85%.