## 1 Introduction
These methods aim to reduce the sizes of traffic flow data and lower the demand of computation resources while at the same time preserving the spatial structures of traffic networks as much as possible. At present, in the related literature regarding the prediction of traffic flow variables of urban road traffic networks, one commonly used method to represent traffic flow data for an urban road traffic network is to first segment that network into consecutive equally sized grids, and then apply a certain pooling operation, such as addition or arithmetic mean, to values of traffic flow variables in each grid. For example, [PERSON] et al. segmented a road network of Beijing into \(256\times 256\) grids when predicting traffic speed using floating car data from taxis, and then they calculated the average speed for these grids [26]. When predicting the traffic flow of Beijing and New York City, [PERSON] et al. divided each city into grids in longitude and latitude directions, and counted the number of vehicles entering and leaving each grid within a certain time interval [27]. [PERSON] et al. segmented a local road network in Beijing when predicting speed in that area, and used the average speed of all road segments covered by each grid as that grid's speed [5]. [PERSON] et al. divided the urban area of Beijing into grids when predicting speed on roads in that area,and calculated each grid's average speed in the same way as [PERSON] et al. for every 30 min [28]. [PERSON] et al. segmented an urban area of Xi'an into 16 \(\times\) 16 grids and summed the number of trips from a certain origin to a certain destination when predicting the number of such trips [29]. [PERSON] et al. divided a metropolitan freeway transportation network in Seattle into grids and calculated the average congestion level for each grid when predicting traffic congestion in that area [30]. However, these operations are often selected without further consideration, and specifically, there has rarely been an evaluation of their impacts regarding the prediction of traffic flow variables. Although this existing scheme has facilitated the prediction of traffic flow variables such as traffic volume, speed, congestion, and demand, until recently there has been little in-depth research on how to properly represent traffic congestion data for an urban road network in order to retain its spatial structure as much as possible and to predict traffic congestion on its road segments. Compared with the prediction of other traffic flow variables, prediction of traffic congestion in an urban traffic network is much more intuitive for and practically significant to both travelers and traffic management departments. For travelers, traffic congestion prediction helps one to choose better travel routes and reduce pollution associated with emissions from vehicles. For traffic management, it can improve operational efficiency by controlling and coordinating urban road traffic networks. Therefore, we propose a representation framework for traffic congestion data in urban road networks. This framework aims to reduce the size of large-scale traffic congestion data in order to lower the requirements of computing resources for deep learning models. Moreover, it does not damage the performance of models used to predict traffic congestion. The contributions of the paper can be summarized as follows:  
* We develop an effective and cost-efficient representation framework for traffic congestion data of urban road networks.