Chapter
Aug 30, 2023
Understanding Mobility Dynamics and Predicting Urban Traffic State via Improved Unsupervised Learning
Authors: Ruiyi Wang [email protected], Huachun Tan, Ph.D. [email protected], Fan Ding, Ph.D. [email protected], and Zoutao Wen [email protected]Author Affiliations
Publication: CICTP 2023
ABSTRACT
Traffic dynamic evolution concerning multiple coupling factors. The key to traffic forecasting is to deal with the multi-modal coupling spatiotemporal factors in the observation data, such as weather information (temperature, wind), different time scales (hours, days, weeks), and some uncontrollable random factors (traffic accidents, etc.). To this end, this paper proposes the semantic factorization-based traffic prediction generative adversarial network (SFTPGAN), which is an improved semantic factorization method based on unsupervised learning. It can automatically find meaningful semantic information in traffic dynamics evolution through its network structure and visualize the impact of each factor on the traffic dynamics evolution by changing the direction of each semantic individually. We evaluate the model on a large-scale GPS trajectory data set in the main urban area of Beijing and find it works well in searching semantic information.
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Published online: Aug 30, 2023
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ASCE Technical Topics:
- Climates
- Continuum mechanics
- Coupling
- Dynamics (solid mechanics)
- Engineering fundamentals
- Engineering mechanics
- Environmental engineering
- Forecasting
- Infrastructure
- Mathematics
- Measurement (by type)
- Meteorology
- Solid mechanics
- Statistics
- Structural dynamics
- Structural engineering
- Structural members
- Structural systems
- Temperature effects
- Temperature measurement
- Traffic accidents
- Traffic engineering
- Traffic management
- Transportation engineering
- Urban and regional development
- Urban areas
- Weather forecasting
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1Master’s Candidate, Dept. of Transportation, Southeast Univ., Nanjing, Jiangsu, China. Email: [email protected]
2Professor, Dept. of Transportation, Southeast Univ., Nanjing, Jiangsu, China. Email: [email protected]
3Assistant Professor, Dept. of Transportation, Southeast Univ., Nanjing, Jiangsu, China. Email: [email protected]
4Master’s Candidate, Dept. of Transportation, Southeast Univ., Nanjing, Jiangsu, China. Email: [email protected]
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Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.