## 1 Introduction
A deep learning framework with a dynamic spatial-temporal graph convolutional network has been proposed to capture the dynamic spatial and temporal dependencies simultaneously, and an attention mechanism is incorporated to dynamically learn weights between traffic nodes based on graph features together with dilated causal convolution to capture the long-term tendencies of traffic data [25]. However, the impact of exceptional events, i.e., traffic accidents or congestion, on traffic flow prediction has seldom been considered, while the dissipation of spatiotemporal correlation features within the depth of the network are also not taken into account in their modeling framework. The occurrence of abnormal events on a road could lead to significant deviations in traffic patterns as the traffic conditions during such periods are more dependent on instantaneous variations. In such scenarios, they can be classified into two categories: (1) long-term and planned events, such as weekday rush hours and holidays; (2) short-term unexpected events, including traffic accidents, adverse weather conditions, temporary traffic controls, etc. Hence, real-world traffic data can be used, while similarity features among regions are used as the spatial information [26] and contextual data locations, weather conditions, and traffic accidents are useful for the traffic flow prediction with improved prediction performance [27]. Shallow machine learning methods (in contrast to deep learning) can be adopted for traffic prediction under accidental conditions. For instance, an online regression support vector machine model (RSVR) has been proposed with higher performance with respect to traffic flow prediction in holiday and traffic accident scenarios [28]. [PERSON] et al. [29] have developed a traffic predictor based on KNN (k-nearest neighbors) combined with singular spectrum analysis technology to predict short-term traffic flow via an integrated gradient lifting regression tree. Further, [PERSON] et al. [30] have conducted traffic flow prediction in the short term via integrated gradient lifting regression trees. However, these methods only directly use traffic data under abnormal conditions or varied conditions without explicitly modeling the spatiotemporal impact of the local abnormal events on traffic flow. [PERSON] et al. [31] extracted the temporal correlation of multi-scale traffic flow by stacking LSTM networks and used the stacked autoencoders to extract the potential feature representations under peak periods and traffic accidents. [PERSON] et al. [32] designed an encoder and decoder architecture via ConvLSTM to predict citywide crowd dynamics during major events (e.g., earthquakes, national holidays). [PERSON] et al. [33] designed a spatiotemporal convolutional sequence learning architecture based on accident encoding, which can effectively capture the short-term spatiotemporal correlations via unidirectional convolution, and it employs a self-attention mechanism to obtain long-term spatiotemporal periodic correlations. To extract the complicated spatiotemporal correlations efficiently, [PERSON] et al. [34] have proposed an encoder-decoder model for multi-step output prediction based on the bidirectional attention mechanism and multi-head spatial attention mechanism. The model uses bidirectional attention flows to investigate the intricate spatiotemporal correlations between the prediction data and historical data, predicting real-time ride-hailing demand during the COVID-19 pandemic. [PERSON] et al. [35] have developed a Multi-Task Multi-Range Multi-Subgraph Attention Network (M3 AN) which can be adaptable for both normal and abnormal traffic situations. Different subgraphs are used to model node attributes while attention mechanisms are employed to measure the dynamic spatial correlations. However, most current condition-aware traffic-prediction methods fall short of meeting expectations. It is noted that various abnormal events (i.e., type, duration, and intensity) also impact traffic conditions across areas.