STAE-BiSSSM: A Traffic Flow Forecasting Model with High Parameter Effectiveness

Traffic flow forecasting plays a significant role in intelligent transportation systems (ITSs) and is instructive for traffic planning, management and control. Increasingly complex traffic conditions pose further challenges to the traffic flow forecasting. While improving the accuracy of model forecasting, the parameter effectiveness of the model is also an issue that cannot be ignored. In addition, existing traffic prediction models have failed to organically integrate data with well-designed model architectures. Therefore, to address the above two issues, we propose the STAE-BiSSSM model as a solution. STAE-BiSSSM consists of Spatio-Temporal Adaptive Embedding (STAE) and Bidirectional Selective State Space Model (BiSSSM), where STAE aims to process features to obtain richer spatio-temporal feature representations. BiSSSM is a novel structural design serving as an alternative to Transformer, capable of extracting patterns of traffic flow changes from both the forward and backward directions of time series with much fewer parameters. Comparative tests between baseline models and STAE-BiSSSM on five real-world datasets illustrates the advance performance of STAE-BiSSSM. This is especially so on METRLA and PeMSBAY datasets, compared with the SOTA model STAEformer. In the short-term forecasting task (horizon: 15 min), MAE, RMSE and MAPE of STAE-BiSSSM decrease by 1.89%/13.74%, 3.72%/16.19% and 1.46%/17.39%, respectively. In the long-term forecasting task (horizon: 60 min), MAE, RMSE and MAPE of STAE-BiSSSM decrease by 3.59%/13.83%, 7.26%/16.36% and 2.16%/15.65%, respectively.