# Ensemble Convolutional Neural Networks for Mode Inference in Smartphone Travel Survey

arXiv: https://arxiv.org/abs/1904.08933 · HTML: https://mlsanity.com/paper/1904.08933/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2019.

## Built on by (most cited first, 6 of 6)

- [Untargeted Poisoning Attack Detection in Federated Learning via Behavior Attestation](https://mlsanity.com/paper/2101.10904/) (2021)
- [Deep hybrid model with satellite imagery: how to combine demand modeling and computer vision for behavior analysis?](https://mlsanity.com/paper/2303.04204/) (2023)
- [Efficient Deep Learning Models for Privacy-preserving People Counting on Low-resolution Infrared Arrays](https://mlsanity.com/paper/2304.06059/) (2023)
- [eFedDNN: Ensemble based Federated Deep Neural Networks for Trajectory Mode Inference](https://mlsanity.com/paper/2205.05756/) (2022)
- [Multi-task Recurrent Neural Networks to Simultaneously Infer Mode and Purpose in GPS Trajectories](https://mlsanity.com/paper/2110.12113/) (2021)
- [Evaluating geospatial context information for travel mode detection](https://mlsanity.com/paper/2305.19428/) (2023)

## Abstract (arXiv)

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%.