Abstract:
Modern traffic management relies on crowd flow prediction (CFP) for many intelligent transportation services tasks. Most methods only forecast metro station entrance and exit flows, which is not enough for traffic management.
Managers desperately want crowd flow distribution (CFD) forecasts to improve transport services. Thus, we proposed three spatiotemporal models to solve the network-wide CFD prediction problem using the online latent space (OLS) strategy to improve transportation services.
Our models account for trending patterns, climate influences, and station similarities to accurately predict CFD and entrance/exit flows. Our online systems train with CFD snapshots.
The previous trend and transition patterns can be used to predict metro station latent attribute evolutions. All empirical results show that the three developed models outperform all state-of-the-art approaches on three large-scale real-world datasets.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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