## 1 Introduction  
Cars have become the preferred means of transportation for more and more people due to the rapid development of urbanization and improvement of people's living standards. The huge number of cars has become very challenging in terms of the efficient operation of urban road traffic networks and causes traffic congestion. Road traffic congestion in many cities around the world is very serious, especially in metropolitan cities [1]. There have been a lot of research on the prediction of urban road traffic congestion and traffic management [2; 3; 4; 5]. Understanding the congestion patterns of an entire road network rather than a single road or several roads in an area is important. Prediction of traffic congestion helps people choose better routes and helps traffic administration departments manage and operate road networks more effectively and more efficiently [6; 7]. With the development and improvement of intelligent transportation systems, traffic data has become more and more accessible due to factors such as the deployment of road sensors and probes, free availability of online map services, and widespread popularization of GPS services and equipment such as smart phones. Similar to other industries, the field of transportation has entered the era of big data. One of the challenges proposed by the large amount of traffic-related data is how to represent large-scale traffic data and apply various models to utilize these data. In recent years, researchers have utilized various types of urban traffic flow data as inputs to different kinds of models for the prediction of traffic flow variables such as speed, volume, density and demand [8]. Two methods dominate the research of traffic prediction: parametric and nonparametric methods [5; 9]. Parametric methods require a model's structure and parameters to be determined in advance according to theoretical or physical assumptions [9]. Auto regressive integrated moving average (ARIMA) is a typical parametric method. This method builds a model based on historical time series data to predict future values. ARIMA and other ARIMA-based models, such as seasonal ARIMA (SARIMA) models [10; 11; 12], KARIMA models [13], ARIMAX models [14], and CTM-SARIMA models [15], have been used to predict traffic flow variables such as congestion and volumes [13; 16]. Compared with parametric methods, nonparametric methods are flexible because their structure and parameters are not fixed. The support vector machine (SVM) approach is based on statistical learning theory and is very popular when making prediction because it can transform low-dimensional nonlinear data to high-dimensional space through a kernel function [17]. SVMs together with their variants, such as SVR, seasonal SVM, PSO-SVM, and online-SVM, have been utilized for traffic flow prediction [18; 19; 20; 21]. Another typical nonparametric method is neural networks. Neural networks are widely used in almost all fields including traffic flow prediction. Neural networks can model complex nonlinear relationships and have an excellent performance in processing multidimensional data [22]. In recent years, deep neural networks have been involved in traffic flow prediction, such as deep belief networks (DBN) [23] and deep neural networks [24]. Although these neural network-based methods are suitable for small traffic networks or networks with a small number of roads, they cannot take advantage of spatial correlations among different roads and temporal dependencies of traffic flow variables. On the other hand, for neural network models, especially deep neural network models, oversized input data make models use too many computing resources, such as GPU memory, and also cause overfitting [25]. Additionally, such input data increase the training and prediction time of models, which restricts the application of neural network models. In order to alleviate such problems, there are methods in the existing literature which are used to represent traffic flow data about road networks.