Abstract
Data on transport usage is important in a wide range of areas. These data are often obtained manually through costly and inaccurate interviews. In the last decade, several researchers explored the use of smartphone sensors for the automatic detection of transport modes. However, such works have focused on developing centralized machine learning mechanisms. This centralized approach requires user data to be transferred to a central server and, therefore, does not satisfy a transport mode detection mechanism’s practical response time and privacy needs. This research presents the Federated System for Transport Mode Detection (FedTM). The main contribution of FedTM is exploring Federated Learning on transport mode detection using smartphone sensors. In FedTM, both the training and inference process is moved to the client side (smartphones), reducing response time and increasing privacy. The FedTM was designed using a Neural Network for the classification task and obtained an average accuracy of 80.6% in three transport classes (cars, buses and motorcycles). Other contributions of this work are: (i) The use of data collected only on the curves of the route. Such reduction in data collection is important, given that the system is decentralized and the training and inference phases take place on smartphones with less computational capacity. (ii) FedTM and centralized classifiers are compared with regard to execution time and detection performance. Such a comparison is important for measuring the pros and cons of using Federated Learning in the transport mode detection task.