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

Shichen Huang, Chunfu Shao, Juan Li, Xiong Yang, Xiaoyu Zhang, Jianpei Qian and Shengyou Wang
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Shichen Huang: School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
Chunfu Shao: School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
Juan Li: School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
Xiong Yang: School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
Xiaoyu Zhang: School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
Jianpei Qian: School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
Shengyou Wang: School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China

Sustainability, 2020, vol. 12, issue 22, 1-17

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.

Keywords: intelligent transportation system; urban road network; graph embedding; deep learning; feature extraction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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