Inferring Transportation Mode and Human Activity from Mobile Sensing in Daily Life

Publication Year: 2018 Publication Type : ConferenceProceeding


In this paper, we focus on simultaneous inference of transportation modes and human activities in daily life via modelling and inference from multivariate time series data, which are streamed from off-theshelf mobile sensors (e.g. embedded in smartphones) in real-world dynamic environments. The transportation mode will be inferred from the structured hierarchical contexts associated with human activities. Through our mobile context recognition system, an accurate and robust solution can be obtained to infer transportation mode, human activity and their associated contexts (e.g. whether the user is in moving or stationary environment) simultaneously. There are many challenges in analysing and modelling human mobility patterns within urban areas due to the ever-changing environments of the mobile users. For instance, a user could stay at a particular location and then travel to various destinations depending on the tasks they carry within a day. Consequently, there is a need to reduce the reliance on location-based sensors (e.g. GPS), since they consume a significant amount of energy on smart devices, for the purpose of intelligent mobile sensing (i.e. automatic inference of transportation mode, human activity and associated contexts). Nevertheless, our system is capable of outperforming the simplistic approach that only considers independent classifications of multiple context label sets on data streamed from low energy sensors.


    author = {Jonathan Liono and Zahraa S. Abdallah and A. Kai Qin and Flora D. Salim},
    bibsource = {dblp computer science bibliography,},
    biburl = {},
    booktitle = {Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, MobiQuitous 2018, 5-7 November 2018, New York City, NY, USA},
    doi = {10.1145/3286978.3287006},
    editor = {Henning Schulzrinne and Pan Li},
    pages = {342--351},
    publisher = {ACM},
    timestamp = {Wed, 25 Sep 2019 01:00:00 +0200},
    title = {Inferring Transportation Mode and Human Activity from Mobile Sensing in Daily Life},
    url = {},
    year = {2018}


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