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Data Fusion for MaaS: Opportunities and Challenges

Conference Paper


Abstract


  • Computer Supported Cooperative Work (CSCW) in design is an essential facilitator for the development and implementation of smart cities, where modern cooperative transportation and integrated mobility are highly demanded. Owing to greater availability of different data sources, data fusion problem in intelligent transportation systems (ITS) has been very challenging, where machine learning modelling and approaches are promising to offer an important yet comprehensive solution. In this paper, we provide an overview of the recent advances in data fusion for Mobility as a Service (MaaS), including the basics of data fusion theory and the related machine learning methods. We also highlight the opportunities and challenges on MaaS, and discuss potential future directions of research on the integrated mobility modelling.

UOW Authors


Publication Date


  • 2018

Citation


  • Wu, J., Zhou, L., Cai, C., Shen, J., Lau, S. K., & Yong, J. (2018). Data Fusion for MaaS: Opportunities and Challenges. In Proceedings of the 2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design, CSCWD 2018 (pp. 184-189). doi:10.1109/CSCWD.2018.8465224

Scopus Eid


  • 2-s2.0-85054373684

Start Page


  • 184

End Page


  • 189

Abstract


  • Computer Supported Cooperative Work (CSCW) in design is an essential facilitator for the development and implementation of smart cities, where modern cooperative transportation and integrated mobility are highly demanded. Owing to greater availability of different data sources, data fusion problem in intelligent transportation systems (ITS) has been very challenging, where machine learning modelling and approaches are promising to offer an important yet comprehensive solution. In this paper, we provide an overview of the recent advances in data fusion for Mobility as a Service (MaaS), including the basics of data fusion theory and the related machine learning methods. We also highlight the opportunities and challenges on MaaS, and discuss potential future directions of research on the integrated mobility modelling.

UOW Authors


Publication Date


  • 2018

Citation


  • Wu, J., Zhou, L., Cai, C., Shen, J., Lau, S. K., & Yong, J. (2018). Data Fusion for MaaS: Opportunities and Challenges. In Proceedings of the 2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design, CSCWD 2018 (pp. 184-189). doi:10.1109/CSCWD.2018.8465224

Scopus Eid


  • 2-s2.0-85054373684

Start Page


  • 184

End Page


  • 189