Feature Extraction and Representation of Urban Road Networks Based on Travel Routes

Abstract

Extraction of traffic features constitutes a key research direction in traffic safety planning. In previous traffic tasks, road network features are extracted manually. In contrast, Network Representation Learning aims to automatically learn low-dimensional node representations. Enlightened by feature learning in Natural Language Processing, representation learning of urban nodes is studied as a supervised task in this paper. Following this line of thinking, a deep learning framework, called StreetNode2VEC, is proposed for learning feature representations for nodes in the road network based on travel routes, and then model parameter calibration is performed. We explain the effectiveness of features from visualization, similarity analysis, and link prediction. In visualization, the features of nodes naturally present a clustered pattern, and different clusters correspond to different regions in the road network. Meanwhile, the features of nodes still retain their spatial information in similarity analysis. The proposed method StreetNode2VEC obtains a AUC score of 0.813 in link prediction, which is greater than that obtained from Graph Convolutional Network (GCN) and Node2vec. This suggests that the features of nodes can be used to effectively and credibly predict whether a link should be established between two nodes. Overall, our work provides a new way of representing road nodes in the road network, which have potential in the traffic safety planning field.

Prediction of the Potential Trade Relationship of Lithium-Ion Battery’s Main Element Raw Material Minerals Combined with the Local Characteristics of the Trade Network

“…Initially, the research on link prediction mainly focused on improving the accuracy of the algorithm and the innovation of new algorithms [26,[31][32][33][34][35]. Gradually, link prediction is widely used in the exploration of citation networks [36], cooperation networks [37], traffic networks [38], and social networks [39]. In recent years, Guan et al [40], Feng et al [41], Liu and Dong [42,43], Zhang et al [44], and Yang et al [45] applied link prediction algorithms to explore the rules for the formation of trade relations in international mineral trade networks.…”

Prediction of the Potential Trade Relationship of a Lithium-Ion Battery's Main Element Raw Material Minerals: Combined with Local Characteristics of the Trade Network

“…Initially, the research on link prediction mainly focused on improving the accuracy of the algorithm and the innovation of new algorithms [26,[31][32][33][34][35]. Gradually, link prediction is widely used in the exploration of citation networks [36], cooperation networks [37], traffic networks [38], and social networks [39]. In recent years, Guan et al [40], Feng et al [41], Liu and Dong [42,43], Zhang et al [44], and Yang et al [45] applied link prediction algorithms to explore the rules for the formation of trade relations in international mineral trade networks.…”