## 2 Literature Review
### Traffic Speed Prediction  
Accurate traffic speed prediction is an important component in ITS, as it offers useful information to reduce traffic congestion by providing route guidance to travelers [23]. Extensive research has been conducted on using available datasets to predict traffic evolution. The difficulty of predicting traffic speed in urban road networks lies in (i) accurately extracting temporal and spatial features of traffic networks, and (ii) adequately considering the impact of external factors on traffic flow from multiple sources of data, such as weather, social events, accidents, etc. Traditionally, researchers use mathematical statistics to analyze and predict traffic states, such as the ARIMA model [24; 25], the Kalman filter algorithm [26], the hidden Markov model [27], the Bayesian network [28], etc. These statistical techniques could model the traffic conditions using relatively small-scale datasets but have limited ability to capture the nonlinear characteristics of traffic data. With the development of data collection and computing power, most recent works have focused on data-driven models, particularly from traditional machine learning models [29; 30] to deep learning models [31; 32] that could perform the prediction task well based on historical traffic databases. The advantages of machine learning models are the ability to handle multidimensional data, implementation flexibility, generality, and strong predictive capabilities [33]. However, they cannot capture the spatial correlations of road networks well. In addition, compared with deep learning methods, the prediction accuracy of machine learning is relatively lower due to the shallow structure. Recently, more advanced and powerful deep learning models have been applied to traffic prediction. Ref. [34] represented large-scale traffic networks as images, and adopted the deep learning architecture of convolutional neural network (CNN) to extract the spatio-temporal traffic features contained in the images. The traditional CNN can use a fixed-size learnable convolution kernel, which effectively describes the spatial characteristics of Euclidean data, such as text, sound, and images, and extracts useful information fromthem [35]. However, real road networks are difficult to meet the approximate grid shape in space, and a large amount of traffic data is complex non-Euclidean data [36]. In order to incorporate the topology of road networks and exploit graph structure information, the graph convolutional neural network (GCN) is extended and applied to traffic prediction [37; 38]. The principles of GCN are to regard the transportation network as a graph and recognize the connectivity of roads by an adjacency matrix, then extend the convolution operation on the graph structure to aggregate the information of each node, which is proved to be more effective than a grid-type convolution on capturing topology features of transportation networks and forecasting traffic speed [39]. In order to take into account the temporal characteristics of traffic data simultaneously, the gated recurrent unit (GRU) is also widely used in traffic speed prediction combined with GCN [31]. GRU is an improved version of the recurrent neural network (RNN), which could process sequence data and can reduce the vanishing gradient problem, while preserving long-term sequence information [40]. The efficiency of GCN and GRU as traffic prediction models has widely been proved using real traffic data [39; 41]. In addition, traffic forecasting is more challenging than other spatiotemporal forecasting problems because it involves many external factors, which affect traffic states. [PERSON]. [42] constructed the traffic speed prediction model considering the day of the week and POI. [PERSON]. [43] proposed a model with bidirectional long short-term memory (LSTM) and a complex attention mechanism to predict the urban traffic volume, combined with weather conditions and event information as external features to further improve the prediction precision.