STA-former: encoding traffic flows with spatio-temporal associations in transformer networks for prediction
H Zeng, X Duan, X Huang, Q Cui - Cluster Computing, 2024 - Springer
Modern transportation networks exhibit intricate dynamic characteristics and diverse
correlations, making the construction of an efficient traffic prediction system quite …
correlations, making the construction of an efficient traffic prediction system quite …
Spatio-temporal graph-TCN neural network for traffic flow prediction
H Ren, J Kang, K Zhang - 2022 19th International Computer …, 2022 - ieeexplore.ieee.org
Building smart cities in the new era depend heavily on traffic flow analysis, forecast, and
management. How to integrate time series and spatial data is a crucial difficulty for …
management. How to integrate time series and spatial data is a crucial difficulty for …
A geomagnetic sensor dataset for traffic flow prediction
Traffic state prediction is essential in Intelligent Transportation Systems for surveillance,
management, and daily commuting. For developing high-accuracy prediction models, real …
management, and daily commuting. For developing high-accuracy prediction models, real …
Enhancing Spatio-temporal Quantile Forecasting with Curriculum Learning: Lessons Learned
Training models on spatio-temporal (ST) data poses an open problem due to the
complicated and diverse nature of the data itself, and it is challenging to ensure the model's …
complicated and diverse nature of the data itself, and it is challenging to ensure the model's …
Forecasting Lifespan of Crowded Events with Acoustic Synthesis-Inspired Segmental Long Short-Term Memory
Forecasting crowd congestion is crucial for ensuring comfortable mobility and public safety.
Existing methods forecast crowding by capturing the increase in planned visits, which …
Existing methods forecast crowding by capturing the increase in planned visits, which …
A local global attention based spatiotemporal network for traffic flow forecasting
Y Lan, J Ling, X Huang, J Wang, Z Hu, L Xiong - Cluster Computing, 2024 - Springer
Accurate traffic forecasting is critical to improving the safety, stability, and efficiency of
intelligent transportation systems. Although many spatiotemporal analysis methods have …
intelligent transportation systems. Although many spatiotemporal analysis methods have …
[HTML][HTML] Foresight plus: serverless spatio-temporal traffic forecasting
J Oakley, C Conlan, GV Demirci, A Sfyridis… - GeoInformatica, 2024 - Springer
Building a real-time spatio-temporal forecasting system is a challenging problem with many
practical applications such as traffic and road network management. Most forecasting …
practical applications such as traffic and road network management. Most forecasting …
Forecasting Citywide Crowd Transition Process via Convolutional Recurrent Neural Networks
Perceiving and modeling urban crowd movements are of great importance to smart city-
related fields. Governments and public service operators can benefit from such efforts as …
related fields. Governments and public service operators can benefit from such efforts as …
[HTML][HTML] A Multi-Scale Residual Graph Convolution Network with hierarchical attention for predicting traffic flow in urban mobility
J Ling, Y Lan, X Huang, X Yang - Complex & Intelligent Systems, 2024 - Springer
Accurate prediction of traffic flow is essential for optimizing transportation resource allocation
and enhancing urban mobility efficiency. However, traffic data generated daily are vast and …
and enhancing urban mobility efficiency. However, traffic data generated daily are vast and …
Make Graph Neural Networks Great Again: A Generic Integration Paradigm of Topology-Free Patterns for Traffic Speed Prediction
Urban traffic speed prediction aims to estimate the future traffic speed for improving urban
transportation services. Enormous efforts have been made to exploit Graph Neural Networks …
transportation services. Enormous efforts have been made to exploit Graph Neural Networks …