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We develop ensemble Convolutional Neural Networks (CNNs) to classify thetransportation mode of trip data collected as part of a large-scale smartphonetravel survey in Montreal, Canada. Our proposed ensemble library is composed ofa series of CNN models with different hyper-parameter values and CNNarchitectures. 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 ameta-learner. The ensemble method with random forest as meta-learner shows anaccuracy of 91.8% which surpasses the other three ensemble combination methods,as well as other comparable models reported in the literature. The "majorityvoting" and "optimal weights" combination methods result in prediction accuracyrates around 89%, while "average voting" is able to achieve an accuracy of only85%.

Ensemble Convolutional Neural Networks for Mode Inference in Smartphone Travel Survey