Traffic Accident Prediction via Patch-Aware and Basis Representation in Time Series Modeling

Traffic accident prediction is of great importance for intelligent transportation systems and public safety management. Unlike conventional traffic flow forecasting tasks, accident data are characterized by low occurrence frequency and highly imbalanced distributions, with near-zero values during most time periods and occasional concentrated bursts. Accident occurrences are also strongly influenced by daily and weekly periodic patterns, resulting in mixed characteristics of low baseline levels, abrupt peaks, and long-term trends. These properties make traditional time series forecasting methods based on stationarity assumptions or single-period modeling less effective. To address this issue, this study proposes a time series forecasting framework that integrates patch-aware local perception with global basis representation. Specifically, this study aims to improve traffic accident time-series forecasting accuracy under sparse and bursty conditions by integrating patch-aware local perception with global basis representation. The patch-level structure captures fine-grained fluctuations in accident sequences by modeling short-term local variations, while basis decomposition provides robust modeling of overall trends through a set of global latent components, leading to complementary effects at both local and global levels. Experimental results on the I-405 highway accident dataset demonstrate that the proposed model significantly outperforms baseline methods, reducing mean squared error (MSE) and mean absolute error (MAE) by approximately 9.7% and 12.6% compared with PatchTST, and by 22.3% and 28.2% compared with Basisformer. Furthermore, experiments on public benchmark datasets ETTh1 and Electricity show that the proposed method achieves comparable or superior performance to mainstream models, indicating its effectiveness and generalization ability across different types of time series scenarios.