# A Multiview Representation Learning Framework for Large-Scale Urban Road Networks
## 6 Conclusions  
This paper presents a novel multiview representation learning framework for large-scale urban road networks that simultaneously preserves topological and human mobility information. Specifically, a road network organization method is proposed in this paper to effectively optimize the calculation efficiency and memory usage of the random walk algorithm for large-scale urban road networks. Based on this concept, we develop a multiview random walk method to capture the structure function of the road network from a topology-aware graph and vehicle transfer pattern from a mobility-aware graph. Finally, we utilize word2 vec to generate representations of the road network.  
In the experiments, two real-world datasets and the ETA determination task are utilized to evaluate the performance of our framework. The experimental results indicate the superiority of the proposed framework over baseline methods in three ways. 1 The large-scale road network organization in our framework can effectively reduce the memory usage of the representation learning process and can improve the computational efficiency in real-world large-scale road network learning. 2 The multiview random walk method in our framework can preserve both topological and human mobility information, but the baseline methods can only preserve one of them. 3 In the downstream traffic task, the representations that are generated based on our framework achieve the highest accuracy, compared to the baseline methods, which shows that our framework has great advantages in terms of accuracy and efficiency in practical application and deployment.  
However, there are also some limitations in our framework: 1 As an important part of a complex urban system, the road traffic network is inherently affected by the surrounding geographical environment, and our framework cannot model such geographical scene characteristics. 2 The relationship between road segments is also dynamic and non-stationarity in the time dimension, which our method cannot handle. Based on this, in the future, we will continue to enhance the road network representation framework in the following ways: first, unstructured knowledge in the human perception of road networks (e.g., this road is the main road connecting two functional areas) should be considered to enrich the multiview context. Furthermore, road network representations should be dynamic over time to identify the different characteristics of different road segments at various times (e.g., morning and evening peaks).  
Conceptualization, K.C., Y.S. and M.D.; methodology, K.C. and G.C.; validation, G.C. and K.L.; formal analysis, K.C.; resources, Y.S. and M.D.; data curation, K.C., G.C. and K.L.; writing--original draft preparation, K.C.; writing--review and editing, K.C.; visualization, G.C. and K.L.; supervision, M.D. and Y.S.; project administration, M.D., K.C. and Y.S.; funding acquisition, M.D. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Natural Science Foundation of China (NFSC), Project No. 42171459 and 41901319; the Key Program of the National Natural Science Foundation of China, Project No. 41730105.  
Not applicable.  
This work was conducted in part using computing resources at the High-Performance Computing Platform of the Central South University.  
The authors declare no conflict of interest.