An Attention-Driven Spatio-Temporal Deep Hybrid Neural Networks for Traffic Flow Prediction in Transportation Systems

In the context of rapidly growing city road networks, understanding complex traffic patterns and implementing effective safety monitoring through advanced Transportation Cyber-Physical Systems (T-CPS) has become increasingly challenging. This involves understanding spatial relationships and non-linear temporal associations. Accurately predicting traffic in such scenarios, particularly for long-term sequences, is challenging due to the complexity of the data. Traditional ways of predicting traffic flow use a single fixed graph structure based on location. This structure does not consider possible correlations and cannot fully capture long-term temporal relationships among traffic flow data, thereby limiting the…

• NN
  National Natural Science Foundation of China

  Awards: 62373255, 62173234
• NS
  Natural Science Foundation of Guangdong Province

  Award: 2024A1515011204