## 1 Introduction
[8] proposed a bidirectional long short-term memory (Bi-LSTM) model using the Modified Firefly Optimization Algorithm (MFOA) called MFOA-Bi-LSTM. Short-term traffic flow prediction is crucial for intelligent transportation systems and congestion relief. Therefore, the accurate prediction of real-time traffic conditions becomes more important. Currently, the existing prediction models lack the ability to effectively extract spatiotemporal features and fail to fully consider the influence of nonlinear noise. To address these issues, research [9] proposed an integrated model for short-term traffic flow forecasting utilizing spatiotemporal characteristics. Currently, examining the spatiotemporal attributes of traffic flow has emerged as a focal point in research. [PERSON] et al. [10] proposed a spatiotemporal multifactor fusion graph neural network (STMFGNN), which aims to more comprehensively characterize spatiotemporal features. Precision in traffic flow forecasting is crucial for enhancing traffic management, alleviating traffic jams, and boosting travel productivity. [PERSON] et al. [11] proposed the MFSTBiSGAT model. The MFSTBiSGAT model utilizes graph attention networks to extract dynamic spatial features from complex road networks and utilizes bidirectional long short-term memory networks to capture temporal correlations from past and future temporal perspectives. Due to the influence of passenger travel mode and the difficulty of data collection, some traffic flow datasets not only have uneven time distribution but also lack data scale, resulting in inaccurate prediction results. [PERSON] et al. [12] proposed a method based on transfer learning to predict non-stationary traffic flow with insufficient data in public transportation systems. Traffic conditions in the real world, especially during rush hour, exhibit complex spatiotemporal dynamics and intricate nonlinear relationships. Existing studies tend to ignore traffic flow changes in different periods, locations, and scenarios, resulting in a lack of robustness and accuracy of prediction models across different environments. [PERSON] et al. [13] introduce a novel approach to address these challenges, namely, a multi-feature spatiotemporal coupled traffic flow prediction model (PE-MFSTC) based on peak-hour embedding. Aiming at the shortcomings of artificial rabbit optimization (ARO) algorithms with low accuracy and slow convergence speed, [PERSON] et al. [14] incorporated the idea of sine and cosine algorithm (SCA) into the rabbit optimization algorithm to further enhance the precision of short-term traffic volume forecasting. Combined with the SCA, a new position updating method based on nonlinear sine learning factor, sine cosine ARO (SARO), is proposed. Multi-step highway traffic flow prediction is essential for intelligent transportation systems, and the existing work has made significant progress in this field. However, physical structure, including paths, distances, and node degrees, is critical information in transportation networks and is often overlooked when encoding spatiotemporal dependencies. To overcome the above problems, [PERSON] et al. [15] proposed a new highway traffic flow prediction multi-task space-time network (MT-STNet), which is composed of an encoder-decoder structure, generative inference system, and multi-task learning. Based on the summary of the domestic and international research on short-time traffic flow prediction from the current research, the research on short-time traffic flow prediction mainly focuses on highways and trunk routes, while there is very little research on non-trunk roads and feeder routes, which have less traffic flow. [PERSON] et al. [16] proposed that, in order to facilitate travel of residents and reduce air pollution, it is important to keep the main roads of the city passing vehicles smoothly and quickly, discussing time series prediction models for short-term traffic flow prediction methods. [PERSON] et al.