Traffic State Lane-Level Estimation Based on Transformer and Vehicle Trajectory Data.

Investigating the fundamental link between microscopic vehicular motion parameters and macroscopic traffic flow states is pivotal for advancing refined traffic state estimation research and propelling the progression of Intelligent Transportation Systems. In this paper, a basic Transformer model has been optimized and extended by incorporating embedding and pooling layers, and the model's hyperparameters have been finely tuned through random search cross-validation. The creation of the Generalized Optimized Transformer (GOT) model ensued, where the multi-head attention mechanism adeptly encapsulates all spatiotemporal dynamics inherent in traffic data. Various benchmark models such as LSTM, RNN, and Transformer were put to test, each demonstrating unique performances in managing different traffic flow states. Among them, the GOT model exhibited superior performance, adeptly handling lane-level traffic state estimation tasks derived from microscopic vehicle trajectory data. In conclusion, this research elucidates the intricate and mutable mapping relationship between microscopic vehicular motion parameters and traffic flow states, proficiently executing lane-level traffic state estimation grounded on microscopic trajectory data. This paper is anticipated to provide fresh insights into the understanding of the complex relationship between microscopic vehicular motion parameters and traffic flow states.