Identification of driving behavior in continuous diverging sections of expressway system interchange based on CNN-BiLSTM

The driving environment in continuous diverging sections of expressway system interchanges is highly complex, posing significant driving risks. To investigate driving behavior and its transition patterns in these areas, a simulated driving experiment was conducted to collect driving behavior parameters and construct a driving behavior spectrum (DBS) for continuous diverging sections. A driving behavior spectrum unit decomposition model, leveraging convolutional neural networks (CNN) and bidirectional long short-term memory networks (BiLSTM), was developed to identify specific driving behaviors. Additionally, a Hidden Markov Model (HMM) was employed to quantify transitions between various driving behavior states. The findings demonstrate that the DBS effectively captures and systematically records temporal changes in driving behavior. The CNN-BiLSTM model accurately identified four typical driving behaviors-straight driving, lane changing, deceleration, and turning-with an impressive average accuracy of 98%. Analysis revealed that the first lane change typically occurs approximately 121 m before the first diverging point, while the second occurs around 78 m before the second diverging point. Furthermore, the HMM model successfully elucidated the transition patterns between different driving states. These results provide valuable insights for identifying hazardous zones and optimizing facility design in expressway system interchanges.