# A Deep Multi-Task Learning Model for OD Traffic Flow Prediction Between Highway Stations
## 2 Related Studies
Therefore, the focus of subsequent research is gradually shifting towards machine learning and neural network algorithms. [PERSON] et al. [20]. used traditional BP neural networks to predict future OD matrices in 2004, pioneering the use of deep learning to study OD matrices in China. [PERSON] et al. [21] used an improved fully connected neural network to input past OD matrix sequences and output predicted traffic flow values for the highway network during a certain period in the future. The evolution of big data necessitates the holistic utilization of existing transportation big data for modeling and problem-solving, with the aim of enhancing the precision of prediction outcomes. Recently, the shortcomings of traditional research methodologies, namely their slow computational speed and inadequate depth of information mining, which fail to meet the real-time and accuracy demands of prediction, have led to an increasing reliance on deep learning-based methods. These methods are now predominantly used to address prediction challenges stemming from OD. Among the most noteworthy are recurrent neural networks (RNNs), convolutional neural networks (CNNs), and attention mechanisms. Considering the distinct benefits of RNNs and CNNs in managing sequential and spatial relationships, prior research has frequently combined these methods within a unified model to address the OD matrix prediction problem. [PERSON] et al. [22] developed a hybrid model, known as SRCN, that integrates deep convolutional networks with long short-term memory networks (LSTM) to forecast a range of traffic data, including OD matrices. This process involves transforming the OD matrix, traffic flow, average speed, and other sequences within the transportation network into a series of static images. The model then employs convolutional networks to capture spatial dependencies, while utilizing LSTM to learn temporal dependencies. The learning results from these two processes are subsequently fused together for accurate prediction. [PERSON] et al. [23] proposed a deep learning model known as Multi Scale ConvLSTM, designed to learn spatial correlations across multiple regional scales. This model incorporates two-dimensional convolution kernels of varying sizes within the convolution operation component of the ConvLong Short Term Memory Network (ConvLSTM). This feature enables the extraction of spatial correlations among nodes in diverse regions. Furthermore, the model leverages the LSTM component to unearth temporal correlation data, ultimately yielding short-term prediction values of the OD matrix. [PERSON] [24] proposed a scenario based spatiotemporal network (CSTN), which consists of three modules. Firstly, CNN is used to extract local spatial features of the starting and ending points, and then input them into ConvLSTM for demand time evolution analysis. Finally, in order to capture the correlation between distant regions, GCC module is used to extract global correlation features. In order to delve into the intricate temporal and spatial correlations within data, several studies have incorporated attention mechanisms. These are designed to compute the correlation weights between any node and any time period on a comprehensive global scale. [PERSON] et al. [25] proposed an enhanced attention mechanism, building upon the traditional LSTM framework, to assess the influence of various dates and time periods on the forecasted target time period. They quantified this influence as attention weights, which were subsequently used to weight the OD matrix prediction values. In the study of graph neural networks, the adjacency relationships between nodes in the graph are represented by edges, which facilitates the transfer of feature information between nodes. Due to the high degree of fit between graph structures and transportation networks, graph neural networks are increasingly considered the primary choice for OD traffic prediction. Edges between nodes can be delineated based on diverse spatial relationships, including distance, traffic flow, and road connectivity.