- Title
- Digital twin mobility profiling : a spatio-temporal graph learning approach
- Creator
- Chen, Xin; Hou, Mingliang; Tang, Tao; Kaur, Achhardeep; Xia, Feng
- Date
- 2022
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/189043
- Identifier
- vital:17365
- Identifier
-
https://doi.org/10.1109/HPCC-DSS-SmartCity-DependSys53884.2021.00182
- Identifier
- ISBN:9781665494571 (ISBN)
- Abstract
- With the arrival of the big data era, mobility profiling has become a viable method of utilizing enormous amounts of mobility data to create an intelligent transportation system. Mobility profiling can extract potential patterns in urban traffic from mobility data and is critical for a variety of traffic-related applications. However, due to the high level of complexity and the huge amount of data, mobility profiling faces huge challenges. Digital Twin (DT) technology paves the way for cost-effective and performance-optimised management by digitally creating a virtual representation of the network to simulate its behaviour. In order to capture the complex spatio-temporal features in traffic scenario, we construct alignment diagrams to assist in completing the spatio-temporal correlation representation and design dilated alignment convolution network (DACN) to learn the fine-grained correlations, i.e., spatio-temporal interactions. We propose a digital twin mobility profiling (DTMP) framework to learn node profiles on a mobility network DT model. Extensive experiments have been conducted upon three real-world datasets. Experimental results demonstrate the effectiveness of DTMP. © 2021 IEEE.
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- 23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data Science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021, Hainan, China, 20-22 December 2021, Proceedings 2021 IEEE 23rd International Conference on High Performance Computing & Communications, 7th International Conference on Data Science & Systems 19th International Conference on Smart City 7th International Conference on Dependability in Sensor, Cloud & Big Data Systems & Applications p. 1178-1187
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2021 IEEE
- Rights
- Open Access
- Subject
- Cyber-physical system; Digital twin; Graph convolution network; Mobility profiling; Spatio-temporal graph learning; Transportation network
- Full Text
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| Thumbnail | File | Description | Size | Format | |||
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