## 1 Introduction  
Urban rail transit (termed as URT) plays an important role in solving the problems of traffic congestion and environmental pollution in medium and large cities. Short-term origin-destination (termed as OD) prediction is crucial to improve the operation of URT. Short-term OD prediction in URT can alleviate problems such as congestion or an incident that may occur in the URT network, so as to improve the operation of URT and provide a better travel experience for passengers. At present, there are three main challenges for short-term OD prediction in URT: (1) data sparsity--in an OD matrix, many OD pairs are either small or zero; (2) complex spatio-temporal correlations--specifically, the variation of passenger flow among adjacent OD matrices in the time dimension usually follows the same distribution. Moreover, for each OD matrix, the change of one element may cause the change of other elements. Thus, there are complex spatio-temporal correlations among adjacent OD matrices; (3) low data availability from the same day--specifically, in-and-out card swiping information for each passenger is required to obtain a complete OD matrix. However, there are different time delays for different passengers between entering the station and leaving the station; therefore, it is difficult to obtain the historical OD matrices of the same day, and only the historical inflow series of the same day can be obtained. The above challenges make it difficult to further improve the short-term OD prediction performance. The existing OD prediction methods in traffic scenarios can be mainly divided into three categories, which are the conventional methods [1; 2], the machine learning methods [3; 4], and the recently emerging deep learning methods [5; 6; 7; 8]. There are three main problems with the conventional methods and machine learning methods as follows: (1) the computational complexity is high for large-scale URT network, which cannot meet the need of real-time prediction; (2) it is difficult to capture complex spatio-temporal correlations; (3) the prediction accuracy is low. Compared with the conventional methods and machine learning methods, the deep learning methods can not only meet the need of real-time prediction, but also further improve the prediction accuracy. In recent years, deep learning has developed rapidly and achieved excellent performance in various fields [9; 10; 11; 12; 13; 14; 15; 16]. However, many advanced neural network design ideas have not been fully applied in the field of short-term OD prediction in URT. For example, channel-spatial attention and feature interaction are rarely applied in the short-term OD prediction in URT. The introduction of these advanced neural network designs into the field of short-term OD prediction in URT can further improve the short-term OD prediction accuracy by overcoming the above challenges. Based on the above analysis, in this paper, a novel parallel interactive attention network (termed as PIANet) for short-term origin-destination prediction in URT is proposed to further improve the short-term OD prediction accuracy. The novelty of the proposed PIANet mainly lies in the architectural design, in which two innovative components including the omnidirectional attention module (termed as OAM) and the feature interaction are proposed. Therefore, our main contributions are three-fold:  
* A novel omnidirectional attention module (termed as OAM) is proposed to improve the representational power of the network by calculating the feature weights in the channel-spatial dimension, which can overcome the difficulty of extracting effective feature information caused by data sparsity and complex spatio-temporal correlations. * A simple yet effective feature interaction is proposed to improve the feature utilization between two network branches.