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Travel Time Estimation and Urban Key Routes Analysis Based on Call Detail Records Data: A Case Study of Guangzhou City

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Machine Learning and Intelligent Communications (MLICOM 2019)

Abstract

Nowadays, the study of urban traffic characteristics has become a major part of city management. With the popularization of the mobile communication network, the call detail records(CDR) have become important resources for the study of urban traffic, containing abundant temporal and spatial information of urban population. To excavate the traffic characteristics of Guangzhou city, this paper focuses on two aspects: travel time estimation and the analysis of key routes in urban area. First, we propose a method of estimating urban travel time based on traffic zones division using the traffic semantic attributes. According to the features of users flow extracted from CDR, we determine the traffic semantic attributes of the areas covered by base stations. With these semantic attributes, we cluster the cell areas into several traffic zones using a K-means method with a weighting dissimilarity measure. Then travel time between different positions in Guangzhou is estimated using the key locations of traffic zones, with an accuracy of 67%. Furthermore, we depict the key routes of Guangzhou city utilizing a DBSCAN method with the users’ trajectories extracted from the CDR data. The results of obtained routes are validated by actual traffic conditions and provide some extra discoveries. Our works illustrate the effectiveness of CDR data in urban traffic and provide ideas for further research.

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Acknowledgement

The work is supported in part by NSFC (No. U1711263), Science, Technology and Innovation Commission of Shenzhen Municipality (No. JCYJ20170816151823313), States Key Project of Research and Development Plan (No. 2017YFE0121300-6) and MOE-CMCC Joint Research Fund of China (MCM20160101).

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Authors and Affiliations

  1. School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, 510006, China

    Weimin Mai, Shaohang Xie & Xiang Chen

  2. Key Lab of EDA, Research Institute of Tsinghua University in Shenzhen (RITS), Shenzhen, 518075, China

    Weimin Mai, Shaohang Xie & Xiang Chen

Authors
  1. Weimin Mai
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  2. Shaohang Xie
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  3. Xiang Chen
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Corresponding author

Correspondence to Xiang Chen .

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Editors and Affiliations

  1. Aeronautics and Astronautics of Nanjing University, Nanjing, China

    Xiangping Bryce Zhai

  2. Aeronautics and Astronautics of Nanjing University, Nanjing, China

    Bing Chen

  3. Aeronautics and Astronautics of Nanjing University, Nanjing, China

    Kun Zhu

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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Mai, W., Xie, S., Chen, X. (2019). Travel Time Estimation and Urban Key Routes Analysis Based on Call Detail Records Data: A Case Study of Guangzhou City. In: Zhai, X., Chen, B., Zhu, K. (eds) Machine Learning and Intelligent Communications. MLICOM 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 294. Springer, Cham. https://doi.org/10.1007/978-3-030-32388-2_55

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  • DOI: https://doi.org/10.1007/978-3-030-32388-2_55

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-32387-5

  • Online ISBN: 978-3-030-32388-2

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