# A Deep Multi-Task Learning Model for OD Traffic Flow Prediction Between Highway Stations
## 1 Introduction  
In recent years, there has been a significant expansion and enhancement of the highway network. Highways, serving as the primary backbone of this network, play a crucial role in effectively interlinking various transportation modes. The rapid development of highways has greatly improved the transportation efficiency of the transportation system and the travel efficiency of residents, promoting economic connections and resource flow between regions. Refinement and intelligent management and control of highways, encompassing congestion management, accident response, safety rescue, and toll optimization, have emerged as paramount and pressing concerns for the advancement of highways in the contemporary era. Predicting highway flow stands as a foundational step in addressing these challenges, especially forecasting the OD flow of travel has emerged as a critical task [1]. The evolution of information technology and the proliferation of intelligent transportation systems have enabled dynamic data acquisition for highways. Consequently, there is an imperative need to address the theoretical task of formulating predictive models based on such dynamic data. As of now, the installation rate of ETC for automobiles in variousprovinces (regions, cities) has reached over 80%, and the utilization rate of ETC for vehicles passing through highways has reached over 90%. The electronic toll collection system utilizes vehicle automatic recognition technology to complete wireless data communication between vehicles and toll stations, enabling automatic vehicle recognition and exchange of relevant toll data. It can also record sea volume transaction data, including entrance and exit ramps, time, vehicle type, driving direction, license plate number, etc. How to effectively and quickly utilize this information, analyze historical data for data statistics and internal connection mining, apply it to predict traffic flow, optimize road network design, allocate resources reasonably, reduce congestion, deeply mine the traffic characteristics it presents, and effectively apply them to the operation analysis of highways has become the most concerned issue for operators and researchers. The OD matrix sequence under investigation is a three-dimensional tensor, encapsulating intricate temporal and spatial correlation data. This presents substantial challenges for associated research. Particularly, when examining OD matrix true value sequences across various historical periods, researchers must discern the spatial correlations among traffic nodes and the temporal correlations among sequence position vectors. Subsequently, these findings are amalgamated to predict OD matrix values for one or more forthcoming periods, serving as the target output. The current study seeks to understand the mutual information between graph nodes by embedding each traffic node and its associated feature vector into the graph structure. This process heavily relies on the establishment of the graph structure itself. However, topological graph structures are often built upon pre-established prior information such as inter-node distance [2], connectivity [3], and adjacency [4]. Consequently, this hinders the graph learning process in capturing the inherent dependency relationships between nodes that lack edge connections. Nevertheless, certain studies depend on attention learning or convolutional networks for correlation learning among nodes across the entire domain. However, this approach overlooks the intrinsic spatial positional relationships present in traffic data. Current research methodologies are unable to equitably manage the intrinsic and extrinsic relationships among transportation nodes. In this paper, we propose a multi-task prediction model for OD traffic flow between highway stations considering the influence of population factors.