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Making applications aware of the mobility experienced by the user can openthe door to a wide range of novel services in different use-cases, from smartparking to vehicular traffic monitoring. In the literature, there are manydifferent studies demonstrating the theoretical possibility of performingTransportation Mode Detection (TMD) by mining smart-phones embedded sensorsdata. However, very few of them provide details on the benchmarking process andon how to implement the detection process in practice. In this study, weprovide guidelines and fundamental results that can be useful for bothresearcher and practitioners aiming at implementing a working TMD system. Theseguidelines consist of three main contributions. First, we detail theconstruction of a training dataset, gathered by heterogeneous users andincluding five different transportation modes; the dataset is made available tothe research community as reference benchmark. Second, we provide an in-depthanalysis of the sensor-relevance for the case of Dual TDM, which is required bymost of mobility-aware applications. Third, we investigate the possibility toperform TMD of unknown users/instances not present in the training set and wecompare with state-of-the-art Android APIs for activity recognition.

Custom Dual Transportation Mode Detection by Smartphone Devices Exploiting Sensor Diversity