# A Deep Multi-Task Learning Model for OD Traffic Flow Prediction Between Highway Stations
## 3 Preliminaries and Problem Definitions
### Impact of Spatial and Temporal Factors on Traffic Data  
Highway traffic flow is characterized by intricate spatiotemporal correlations, reflecting diverse and dynamic patterns that evolve in both time and space. These patterns are not only shaped by temporal and spatial dimensions but also by external environmental factors such as holidays, weather conditions, and population dynamics. In terms of temporal characteristics of traffic data: Traffic flow data from highways demonstrate non-linear correlations in their temporal characteristics. These include adjacent time features, trend time features, and cycle time features. In short, the periodicity of time, day, and week. From Figure 1, it can be seen that the daily flow trend of the same station shows obvious morning and evening peak characteristics, and the traffic flow sequence between days shows certain periodicity and similarity, with different weekdays and weekends. In terms of spatial characteristics of traffic data: The spatial correlation of highway traffic flow data can be bifurcated into static and dynamic spatial features. Static spatial features are characterized by geographic interdependence and pattern similarity, while dynamic spatial features are influenced by the trends in traffic flow at key original stations and the trends at key destination stations. From Figure 2, the traffic flow of node \(v_{3}\) is not only affected by the traffic flow of neighboring nodes \(v_{1},v_{2},v_{4},\ldots,v_{n}\) at the current time \(t\), and the influence coefficient can be represented by the covariance matrix, but also by the flow of traffic from node \(v_{i}(i\
eq 2)\) to node \(v_{2}\) at time \(t-1\). The degree of influence is represented by a probability matrix. In terms of external characteristics of traffic data: Firstly, the multi-source fusion processing of weather data and traffic flow at a toll station on the highway revealed that weather can affect people's travel choices, and the changes in traffic flow vary depending on different weather types, show in Figure 3.  
Figure 1: The “time”, “day” and “week” periodicity of traffic flow. Secondly, the traffic flow on highways is not only affected by weather, but also has a certain correlation with holidays, as shown in Figure 4. Whether it is a holiday has a significant impact on the changes in traffic flow. Finally, highways have strong externalities, and the distribution of traffic objectively reflects the laws of human movement. Exploring the correlation between the traffic flow of highways and their radiation population. Specifically, it examines the Origin-Destination (OD) traffic flow and radiation population data from Chengdu city in 2018. These figures are subsequently analyzed for correlation and normalized, as presented in Table 1. Pearson correlation coefficient is used for correlation measurement. It was found that the destination of highways is significantly correlated with the radiation population, as shown in Figure 5.  
Figure 3: Changes in the correlation between traffic flow and weather. Figure 2: Spatial correlation analysis of traffic flow of multi-nodes. Figure 4: Trend of traffic flow changes on weekdays and holidays. Figure 5: Matrix related to origin, destination, and population.