EAI Endorsed Transactions on Ambient Systems 14(4): e7

Research Article

Review of Transportation Mode Detection techniques

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  • @ARTICLE{10.4108/amsys.1.4.e7,
        author={Jacopo Biancat and Chiara Brighenti and  Attilio Brighenti},
        title={Review of Transportation Mode Detection techniques},
        journal={EAI Endorsed Transactions on Ambient Systems},
        keywords={Urban mobility, Sustainable commuting, Persuasive sustainability},
  • Jacopo Biancat
    Chiara Brighenti
    Attilio Brighenti
    Year: 2014
    Review of Transportation Mode Detection techniques
    DOI: 10.4108/amsys.1.4.e7
Jacopo Biancat1,*, Chiara Brighenti1, Attilio Brighenti1
  • 1: Attain IT S.r.l., Venezia, Italy
*Contact email: jacopo.biancat@attainit.eu


This paper reviews the works found in the literature in the field of Transportation Mode Detection (TMD) which is a subfield of Activity Recognition aiming at indentifying (i.e. classifying) the mean of transportation a person is using. The solutions found in literature have different characteristics according to the device for which the solution was tailored (smartphones or other systems such as, e.g., GPS loggers) and to the algorithm used for the classification task. This may vary a lot according to the number and type of input used (e.g. accelerations, GPS, maps information or GIS - Geographical Information System information) and to the identified classes of transportation mode. These two aspects are the most relevant to consider when evaluating and comparing the accuracies claimed by each work. A comparison of the works is proposed taking into account the characteristics discussed above. In general the accelerometer is the most widely used sensor for TMD applications, as it limits battery consumption and captures relevant features for detecting motion. Indeed a key challenge in TMD is to detect different motorized classes such as bus, car, train and metro because they share common characteristics (such as e.g. the average speed and accelerations) which make hard identifying suitable features for the classification algorithm. Identifying the “walk” and “stationary” transportation modes is a simpler task because they are characterized by distinct features.