Learning to Generate Maps from Trajectories

Accurate and updated road network data is vital in many urban applications, such as car-sharing, and logistics. The traditional approach to identifying the road network, i. e. , field survey, requires a significant amount of time and effort. With the wide usage of GPS embedded devices, a huge amount of trajectory data has been generated by different types of mobile objects, which provides a new opportunity to extract the underlying road network. However, the existing trajectory-based map recovery approaches require many empirical parameters and do not utilize the prior knowledge in existing maps, which over-simplifies or overcomplicates the reconstructed road network. To this end, we propose a deep learning-based map generation framework, i. e. , DeepMG, which learns the structure of the existing road network to overcome the noisy GPS positions. More specifically, DeepMG extracts features from trajectories in both spatial view and transition view and uses a convolutional deep neural network T2RNet to infer road centerlines. After that, a trajectory-based post-processing algorithm is proposed to re- fine the topological connectivity of the recovered map. Extensive experiments on two real-world trajectory datasets con- firm that DeepMG significantly outperforms the state-of-theart methods.

Authors

• Sijie Ruan Beijing Institute of Technology
• Cheng Long Nanyang Technological University
• Jie Bao
• Chunyang Li
• Zisheng Yu
• Ruiyuan Li
• Yuxuan Liang The Hong Kong University of Science and Technology Chinese Academy of Sciences
• Tianfu He
• Yu Zheng JD Intelligent Cities Research

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