Paper Info
Title
Data Fusion For Maas: Opportunities And Challenges
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.
Year
Venue
Keywords
2018
PROCEEDINGS OF THE 2018 IEEE 22ND INTERNATIONAL CONFERENCE ON COMPUTER SUPPORTED COOPERATIVE WORK IN DESIGN ((CSCWD))
data fusion, machine learning, mobility as a service
Field
DocType
Citations 
Data integration,Kernel (linear algebra),Data science,Computer-supported cooperative work,Computer science,Matrix decomposition,Sensor fusion,Intelligent transportation system,Mobility modelling,Facilitator,Distributed computing
Conference
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Jun Shen123440.40
Luping Zhou249843.89
Chen Cai3124.36
Jun Shen4208.82
Sim Kim Lau55010.24
Jianming Yong670480.64