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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Shichen Huang & Chunfu Shao & Juan Li & Xiong Yang & Xiaoyu Zhang & Jianpei Qian & Shengyou Wang, 2020. "Feature Extraction and Representation of Urban Road Networks Based on Travel Routes," Sustainability, MDPI, vol. 12(22), pages 1-17, November.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:22:p:9621-:d:447144
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    References listed on IDEAS

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    1. Tanja Congiu & Giovanni Sotgiu & Paolo Castiglia & Antonio Azara & Andrea Piana & Laura Saderi & Marco Dettori, 2019. "Built Environment Features and Pedestrian Accidents: An Italian Retrospective Study," Sustainability, MDPI, vol. 11(4), pages 1-14, February.
    2. Lee, Jaeyoung & Abdel-Aty, Mohamed & Jiang, Ximiao, 2014. "Development of zone system for macro-level traffic safety analysis," Journal of Transport Geography, Elsevier, vol. 38(C), pages 13-21.
    3. Abdel-Aty, Mohamed & Lee, Jaeyoung & Siddiqui, Chowdhury & Choi, Keechoo, 2013. "Geographical unit based analysis in the context of transportation safety planning," Transportation Research Part A: Policy and Practice, Elsevier, vol. 49(C), pages 62-75.
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    1. Gozuacik, Necip & Sakar, C. Okan & Ozcan, Sercan, 2023. "Technological forecasting based on estimation of word embedding matrix using LSTM networks," Technological Forecasting and Social Change, Elsevier, vol. 191(C).

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