# A Multiview Representation Learning Framework for Large-Scale Urban Road Networks
## 1 Introduction  
As the most basic computing unit of traffic analysis in intelligent transportation systems (ITSs) [1], urban road networks comprising various road segments play a vital role in multiple analysis tasks, such as traffic flow prediction [2] and travel time estimation [3]. In industry, a high-quality road network is also indispensable for location-based service (LBS) provision. For example, digital maps (e.g., Google Maps and Baidu Maps) utilize road networks to support navigation or recommendation [4]. However, it is a challenging issue to embed a road network, which is represented as a complex graph structure, into common data-driven methods, such as support vector machines and artificial neural networks [5]. Hence, learning low-dimensional vector representations of road segments is critical for data-driven traffic analysis.  
An urban road network is a complex traffic system that reflects the connections among various regions from different perspectives, including topological structures and human mobility patterns. Compared to general graph structure data, the road network scale is significantly larger. For example, the number of road segments reaches tens of millions in some large cities. The above properties suggest that informative representations of road networks should be both topology and mobility aware. In addition, the representation method should be adapted to the calculation of large-scale data.  
Topology awareness indicates that the latent space of representation should preserve the topological neighborhood of road segments [6]. Specifically, if two road segments aretopologically close, then their representation vectors are also close in latent space. This property is a special case of [PERSON]'s first law of geography [7] in topological space. In the real world, if two road segments are topologically adjacent, their geographic and physical configurations (e.g., road class, speed limit, and land use type) should be similar. Hence, in the latent space of topology-aware representations, most road segments are usually connected, but those that are topologically adjacent are more notably connected than those that are further away in the topological structure of the road network. As illustrated in Figure 1a, road segments 2, 3, 4 and 5 are the topological neighborhoods of road segment 1, so their connections are stronger in latent space (Figure 1b).  
Mobility awareness indicates that the latent space of representations should preserve the human mobility pattern within the urban road network [8]. This perspective is motivated by the way a person sequentially visits locations through several road segments with distinctive purposes, and we analyzed the underlying connections within its context, defined as the set of road segments along a trajectory [9]. As shown in Figure 1a, if most vehicles travel from road segments 1 to 0 via path (1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1  
The contributions of this research are as follows:  
* We propose a new random walk method to capture the structure function of the road network from a topology-aware graph and the vehicle transfer pattern from a mobility-aware graph.
* We design a new road network data-organization algorithm, including optimal segmentation, subgraph repair, and walk index calculation to optimize our representation framework for lossless random walks in large-scale road networks.
* We conduct extensive experiments involving two real-world urban road networks. The experimental results of the estimated time of arrival (ETA) task verify the effectiveness and efficiency of the proposed framework.  
The remainder of this paper is organized as follows: Section 2 summarizes related works. Section 3 introduces the details of our proposed multiview representation learning framework. Section 4 presents the experimental results for two real-world urban road networks. Section 5 provides an attribution analysis of the experimental results. Finally, we conclude our work in Section 6.