  Transport mode detection is a classification problem aiming to design an
algorithm that can infer the transport mode of a user given multimodal signals
(GPS and/or inertial sensors). It has many applications, such as carbon
footprint tracking, mobility behaviour analysis, or real-time door-to-door
smart planning. Most current approaches rely on a classification step using
Machine Learning techniques, and, like in many other classification problems,
deep learning approaches usually achieve better results than traditional
machine learning ones using handcrafted features. Deep models, however, have a
notable downside: they are usually heavy, both in terms of memory space and
processing cost. We show that a small, optimized model can perform as well as a
current deep model. During our experiments on the GeoLife and SHL 2018
datasets, we obtain models with tens of thousands of parameters, that is, 10 to
1,000 times less parameters and operations than networks from the state of the
art, which still reach a comparable performance. We also show, using the
aforementioned datasets, that the current preprocessing used to deal with
signals of different lengths is suboptimal, and we provide better replacements.
Finally, we introduce a way to use signals with different lengths with the
lighter Convolutional neural networks, without using the heavier Recurrent
Neural Networks.
