## 1 Introduction  
Traffic prediction plays a crucial role in intelligent urban management and planning research on traffic prediction, encompassing various aspects such as traffic flow on road networks, passenger flow on public transit networks, Origin-Destination (OD) demand prediction, traffic speed prediction, travel time prediction, traffic congestion prediction, etc., holds significant importance for both users and managers of transportation systems. Accurate traffic prediction enables users to plan their routes more effectively, whether driving on roads or using public transit. By avoiding congested areas and selecting the most efficient paths, users can reduce travel time and improve their overall commuting experience. Additionally, predicting traffic conditions allows users to anticipate potential hazards, accidents, or delays along their route, enabling them to make safer decisions while navigating roads or utilizing public transportation services. Moreover, real-time access to traffic predictions empowers users to make informed decisions about when to travel, which routes to take, or whether to switch to alternative modes of transportation. This leads to increased convenience and flexibility in managing their daily commutes. For urban planners and traffic managers, traffic prediction research assists transportation managers in optimizing the performance of road and transit networks by identifying congestion-prone areas and implementing appropriate traffic management strategies. This includes adjusting traffic signal timings, optimizing public transit schedules, and coordinating infrastructure improvements to enhance network efficiency. Additionally, accurate traffic predictions enable managers to allocate resources more effectively, such as deploying additional transit services, adjusting route schedules, or prioritizing maintenance activities. This ensures that resources are utilized efficiently to meet the demands of travelers and minimize disruptions. Moreover, insights derived from traffic prediction research inform strategic decision-making processes related to urban planning, infrastructure development, and transportation policy formulation. By understanding future traffic patterns and demands, decision makers can make informed choices to support sustainable and resilient transportation systems. In summary, research on traffic prediction plays a critical role in optimizing travel efficiency, safety, and convenience for users while assisting transportation managers in effectively managing and improving the performance of transportation networks. Traditional traffic prediction methods, such as those based on Historical Averages (HAs), time series analysis, and autoregressive models, while providing initial traffic prediction to some extent, often fail to fully capture traffic data's complexity and dynamic changes. These methods typically assume that traffic variations are linear or follow simple patterns, making it challenging to handle non-linear relationships, the impact of sudden events, and seasonal or periodic changes in traffic data. Additionally, they exhibit low efficiency when dealing with large-scale, high-dimensional data, making it challenging to respond to real-time traffic changes. In recent years, the rapid development of deep learning models has dramatically promoted research and applications in traffic prediction. Deep learning models such as Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), Graph Neural Networks (GNNs), and transformers have provided new solutions for traffic prediction with their outstanding data processing capabilities and ability to model complex temporal and spatial relationships. These models can automatically learn traffic flow patterns, passenger flow, speed, and congestion changes from massive historical traffic data, achieving high-accuracy predictions of future traffic conditions.