Digital Twin Mobility Profiling: A Spatio-Temporal Graph Learning Approach
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866914668887408640 |
|---|---|
| author | Chen, Xin Hou, Mingliang Tang, Tao Kaur, Achhardeep Xia, Feng |
| author_facet | Chen, Xin Hou, Mingliang Tang, Tao Kaur, Achhardeep Xia, Feng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_03750 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Digital Twin Mobility Profiling: A Spatio-Temporal Graph Learning Approach Chen, Xin Hou, Mingliang Tang, Tao Kaur, Achhardeep Xia, Feng Machine Learning Artificial Intelligence Human-Computer Interaction 68T09, 68T30, 68U35 I.2.6; I.2.4; H.1.2 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. |
| title | Digital Twin Mobility Profiling: A Spatio-Temporal Graph Learning Approach |
| topic | Machine Learning Artificial Intelligence Human-Computer Interaction 68T09, 68T30, 68U35 I.2.6; I.2.4; H.1.2 |
| url | https://arxiv.org/abs/2402.03750 |