Accurate traffic speed prediction holds immense significance in mitigating traffic congestion and enhancing traffic safety. However, traffic data exhibit distinct patterns across different cycles (such as weekdays, weekends, and holidays), making it challenging for traditional models to effectively capture this multiperiod heterogeneity in traffic data. Furthermore, most existing research on traffic speed prediction struggles to efficiently capture the spatiotemporal characteristics of dynamic traffic data simultaneously. To tackle these challenges, this paper first introduces spatiotemporal‐aware position encoding (STAPE) technology, which addresses the multiperiod heterogeneity in traffic data by integrating temporal cycle information with spatial position information. Second, a multilevel spatiotemporal feature extraction architecture is designed, leveraging graph convolutional network (GCN) to capture the topological structure and spatial features of the traffic road network. By applying gated recurrent unit (GRU) to capture the temporal dependencies of traffic data, and combining GCN and GRU in multiple stages, this architecture deeply explores the spatiotemporal features of traffic data. Additionally, this paper integrates a multihead attention mechanism, which, in conjunction with the parallelized attention channel adaptive mechanism and the multilevel spatiotemporal feature extraction architecture, enhances the model’s ability to adaptively model different spatiotemporal patterns dynamically, thereby efficiently capturing the dynamically changing spatiotemporal features. Extensive performance evaluation experiments conducted on the METR‐LA and PEMS‐BAY datasets demonstrate that the predictive performance of the proposed model surpasses that of nine other baseline methods.

The recently proposed learned bloom filter (LBF) opens a new perspective on how to reconstruct bloom filters with machine learning. However, the LBF has a massive time cost and does not apply to multidimensional spatial data. In this paper, we propose a prefix‐based and adaptive learned bloom filter (PA‐LBF) for spatial data, which efficiently supports the insertion and deletion. The proposed PA‐LBF is divided into three parts: (1) the prefix‐based classification. The Z‐order space‐filling curve is used to extract data, prefix it, and classify it. (2) The adaptive learning process. The multiple independent adaptive sub‐LBFs are designed to train the suffixes of data, combined with part 1, to reduce the false positive rate (FPR), query, and learning process time consumption. (3) The backup filter uses CBF. Two kinds of backup CBF are constructed to meet the situation of different insertion and deletion frequencies. Experimental results prove the validity of the theory and show that the PA‐LBF reduces the FPR by 84.87%, 79.53%, and 43.01% with the same memory usage compared with the LBF on three real‐world spatial datasets. Moreover, the time consumption of PA‐LBF can be reduced to 5× and 2.05× that of the LBF on the query and learning process, respectively.