  Making applications aware of the mobility experienced by the user can open
the door to a wide range of novel services in different use-cases, from smart
parking to vehicular traffic monitoring. In the literature, there are many
different studies demonstrating the theoretical possibility of performing
Transportation Mode Detection (TMD) by mining smart-phones embedded sensors
data. However, very few of them provide details on the benchmarking process and
on how to implement the detection process in practice. In this study, we
provide guidelines and fundamental results that can be useful for both
researcher and practitioners aiming at implementing a working TMD system. These
guidelines consist of three main contributions. First, we detail the
construction of a training dataset, gathered by heterogeneous users and
including five different transportation modes; the dataset is made available to
the research community as reference benchmark. Second, we provide an in-depth
analysis of the sensor-relevance for the case of Dual TDM, which is required by
most of mobility-aware applications. Third, we investigate the possibility to
perform TMD of unknown users/instances not present in the training set and we
compare with state-of-the-art Android APIs for activity recognition.
