Wu, Jianqing and Zhou, Luping and Cai, Chen and Shen, Jun and Lau, Sim Kim and Yong, Jianming (2018) Data Fusion for MaaS: Opportunities and Challenges. In: 2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design, May 9 2018, Nanjing, China.
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.
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| Item Type: | Conference or Workshop Item (Commonwealth Reporting Category E) (Paper) |
|---|---|
| Refereed: | Yes |
| Item Status: | Live Archive |
| Additional Information: | Files associated with this item cannot be displayed due to copyright restrictions. |
| Faculty/School / Institute/Centre: | Historic - Faculty of Business, Education, Law and Arts - School of Management and Enterprise (1 Jul 2013 - 17 Jan 2021) |
| Faculty/School / Institute/Centre: | Historic - Faculty of Business, Education, Law and Arts - School of Management and Enterprise (1 Jul 2013 - 17 Jan 2021) |
| Date Deposited: | 07 Feb 2020 06:20 |
| Last Modified: | 11 Feb 2020 23:34 |
| Uncontrolled Keywords: | data fusion, machine learning, mobility as a service |
| Fields of Research (2008): | 08 Information and Computing Sciences > 0806 Information Systems > 080699 Information Systems not elsewhere classified |
| Identification Number or DOI: | https://doi.org/10.1109/CSCWD.2018.8465224 |
| URI: | http://eprints.usq.edu.au/id/eprint/35584 |
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