# A Deep Multi-Task Learning Model for OD Traffic Flow Prediction Between Highway Stations
## 2 Related Studies  
Generally, OD traffic flow prediction aims to predict the traffic flow at each starting and ending point. The research on OD matrix prediction can be divided into three stages: the first stage is mainly based on statistical learning methods to model the temporal trend of OD matrix. The second stage of research is mainly based on the algorithms of recurrent neural networks, convolutional neural networks, and attention mechanisms, and has made breakthrough progress in mining temporal and spatial mutual information. After 2017, graph neural networks, especially graph convolutional networks, have become the mainstream of spatiotemporal relationship learning research and have been widely used in traffic related problems, bringing OD matrix prediction problems into a new stage of graph learning. This section will introduce the current research status of OD matrix prediction problem at home and abroad from three parts: traditional research methods, research based on deep learning algorithms, and research based on graph neural networks. Traditional prediction methods can be roughly divided into two categories: growth coefficient method and comprehensive method [6]. The growth coefficient method assumes that the future OD distribution will be the same as the existing OD distribution, and based on this assumption, predicts the future OD, mainly including the constant growth coefficient method, the average growth coefficient method, the Dett method, and the Follett method [7]. [PERSON] et al. cleverly adopted the gravity model method, utilizing more easily obtainable population Using GDP and other data resources to infer the OD traffic matrix, and then predict the passenger flow between stations [8]. [PERSON] et al. treated OD matrix prediction as a non-negative matrix factorization problem and used autoregressive models to process the decomposition results to obtain the prediction target [9]. This method does not involve changes in traffic impedance, and once there are significant changes in land use and economic structure, this method will not be applicable [10]. The comprehensive method starts from the actual OD traffic volume, assumes human travel behavior based on the distribution pattern of actual OD, analyzes the distribution pattern of OD matrix, constructs a model, calibrates model parameters, and predicts future OD [11]. [PERSON] et al. [12] proposed using Bayesian estimation to predict OD matrices. [PERSON] et al. [13] applied the generalized least squares method to the inverse derivation of OD matrices. [PERSON] et al. [14] proposed using a maximum likelihood estimation model to estimate the OD matrix. [PERSON] et al. [15] proposed combining the maximum entropy method with the user optimal allocation model to estimate the OD matrix. [PERSON] et al. [16] proposed an estimation method for OD matrix using linear approximation of allocation matrix, where the allocation ratio is linearly correlated with travel demand. [PERSON] et al. [17] proposed an integrated heterogeneous sensor deployment model that does not require prior OD information and path selection probability. The results show that only possible and reasonable ODs can be extracted, and the solution is not unique. [PERSON] et al. [18] proposed a widely used iterative proportional fitting (IPF) method, which can solve problems without updating prior information on a large amount of on/off vehicle data. Furthermore, regardless of the given initialization matrix, it can converge to the same estimated value in the shortest computation time. [PERSON] et al. [19] proposed a genetic algorithm for solving the optimization model of OD matrix extraction based on the least squares model, and verified it with practical data in Nanjing, China. The results showed that the proposed method can effectively derive accurate dynamic OD matrix. Moreover, predicting OD matrices based on mathematical statistical methods has drawbacks such as high computational complexity and difficulty in deeply exploring the internal relationships of data.