## 3 Problem Statement and Input Data Representation Methods  
### Traffic Data and Their Unique Characteristics  
The first step in ensuring accurate traffic prediction is acquiring high-quality data primarily from various sources. These sources mainly include fixed sensors (such as  
Figure 2: Timeline of deep learning-based prediction algorithms. traffic cameras, induction loops, and radar sensors), mobile sensors (such as GPS devices in vehicles and smartphone apps), public transit systems, traffic management centers, social media and online platforms, satellites, aerial imagery, and third-party data service providers. Fixed sensors collect information about vehicle flow, speed, and road occupancy. Mobile sensors provide data on vehicle location and moving speeds. Public transit system data include the positions of buses and subways, departure intervals, and passenger counts. Traffic management centers integrate real-time and historical traffic data. Social media and online platforms offer updates on traffic incidents and road conditions. Satellites and aerial imagery monitor large-scale traffic flow and vehicle density. Third-party data service providers provide comprehensive traffic data that has been preprocessed and analyzed. After obtaining these data, preprocessing steps such as data cleaning, handling outliers, and data integration are typically performed to improve data quality and relevance, providing reliable inputs for traffic prediction models. These high-quality data are the foundation for achieving accurate traffic predictions. To summarize, the unique characteristics of traffic data are as follows:  
1. **Strong periodicity**. Traffic data exhibit significant daily and weekly cycles. Typical patterns include morning and evening rush hours during weekdays and different traffic patterns on weekends. Seasonal variations, such as holiday traffic spikes, also exhibit periodicity. 2. **Spatial dependencies**. While many time series involve only time, traffic data are inherently spatiotemporal. Due to the interconnected nature of road networks, the traffic conditions at one location can highly depend on the traffic conditions at nearby or distant locations. 3. **Non-stationarity**. Traffic patterns change over time and are influenced by factors like urban development, changes in traffic regulations, or the introduction of new infrastructure. This non-stationarity means that the statistical properties of the traffic data (such as mean and variance) can vary, making modeling more challenging. 4. **Volatility**. Traffic data can be highly volatile due to unexpected events such as accidents, roadwork, or weather conditions. These events can cause sudden spikes or drops in traffic flow that are not easily predictable with standard models. 5. **Heteroscedasticity**. Traffic volume variability is not constant over time. It can vary significantly across different times of the day or days of the week, particularly increasing during rush hours and decreasing at night. 6. **Multivariate influences**. Traffic conditions are influenced by a wide range of factors beyond just the number of vehicles on the road. Weather conditions, special events, economic conditions, and social media trends can affect traffic flow and congestion levels. These unique traffic data characteristics make it challenging to model traffic data prediction. The general prediction model cannot be directly applied to the task of traffic data prediction. On the other hand, traffic data encompass both spatial and temporal dimensions. These datasets capture traffic events' geographical locations (spatial) and corresponding timeframes (temporal). Integrating spatial coordinates and time stamps enables the analysis of traffic patterns, flow dynamics, and congestion over time and across different locations. This dual-dimensional nature renders traffic data inherently spatiotemporal. Thus, effective traffic data prediction must incorporate spatial and temporal dependencies within the dataset.