# A Multiview Representation Learning Framework for Large-Scale Urban Road Networks
## 2 Related Works and Motivations  
### Related Works  
Most existing studies of road network representation can be divided into two categories. One category involves topological information encoding via classical general-network representation learning methods. The other category includes encoding various geospatial properties and transportation elements of road segments through various methods. Recently, with the rapid development of deep learning [13], neural network-based representation learning methods [6; 10; 11; 14] have achieved great success in natural language processing (NLP) [15], computer vision (CV) [16], etc. These works assumed that nearby nodes in a network tend to yield close representation vectors in latent space. Thus, the nodes are typically utilized to learn topological information of road networks. DeepWalk [14] first introduced the word2 vec [12] framework based on NLP to generate network representations by sampling nodes via a random walk procedure. To capture the diversity of connectivity patterns that are observed in networks, Node2 vec [6] follows the general framework of DeepWalk but embeds a parameterized random walk, which controls the node-sampling process based on the breadth-first search (BFS) and depth-first search (DFS) strategies. Considering the weakness of random walk-based methods for learning multiscale network information, LINE [10] samples nodes according to multiple connectivity considerations, rather than via the random walk method, and optimizes a carefully designed objective function to capture first- and second-order similarities. These topology-aware representation methods perform well in general networks, such as social or citation networks. However, due to the complex traffic dependency and traffic properties of urban road networks, these methods typically perform poorly in traffic downstream tasks (e.g., traffic flow prediction and travel time estimation) [2; 17]. As shown in Figure 2, the connections between each topologically adjacent road segment are homogeneous in terms of topology awareness, but due to the different driving rules under various road properties, these connections are likely heterogeneous in the real world. For instance, segments 1, 2, 3 and 4 are all one-way roads with only one allowable driving direction; thus, the connection 1-2  
To address this issue, researchers have focused on the geospatial properties and transportation elements of road segments, rather than on the topological structure. They have developed a series of methods [1; 5; 18; 19; 20]. [PERSON] et al. [1; 19] noted the shortcomings of topology-based methods in characterizing road property information and established the IRN2 vec and RN2 vec models. IRN2 vec learns representations using the geographic location, intersection tag, and N-way type [19], while RN2 vec learns representations using the shortest path [1]. [PERSON] et al. [18] focused on multilevel properties (i.e., functional zones, structural regions, and road segments) to develop a hierarchical road-network representation model (HRNR). The experiment indicated that these models significantly outperformed topology-based models. Although these works could preserve the inherent properties of road segments, they still ignored the heterogeneity of connections among road segments in latent space. As illustrated in Figure 2, the two road segments of 2 and 2 exhibit the same properties. However, the traffic volume in segment 2 is larger than that in segment 2 due to the school that is located next to segment 2. Therefore, more vehicles are transferred from other segments to segment 2 than to segment 2, which indicates the underlying heterogeneity of connections in latent space. In addition, the scale of most existing representation methods cannot be adapted to real-world urban road networks, which usually contain tens of millions of nodes [21].