Machine Learning and Intelligent Communications. 4th International Conference, MLICOM 2019, Nanjing, China, August 24–25, 2019, Proceedings

Research Article

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

Download
232 downloads
  • @INPROCEEDINGS{10.1007/978-3-030-32388-2_55,
        author={Weimin Mai and Shaohang Xie and Xiang Chen},
        title={Travel Time Estimation and Urban Key Routes Analysis Based on Call Detail Records Data: A Case Study of Guangzhou City},
        proceedings={Machine Learning and Intelligent Communications. 4th International Conference, MLICOM 2019, Nanjing, China, August 24--25, 2019, Proceedings},
        proceedings_a={MLICOM},
        year={2019},
        month={10},
        keywords={Travel time estimation Urban key routes Call detail records},
        doi={10.1007/978-3-030-32388-2_55}
    }
    
  • Weimin Mai
    Shaohang Xie
    Xiang Chen
    Year: 2019
    Travel Time Estimation and Urban Key Routes Analysis Based on Call Detail Records Data: A Case Study of Guangzhou City
    MLICOM
    Springer
    DOI: 10.1007/978-3-030-32388-2_55
Weimin Mai, Shaohang Xie, Xiang Chen,*
    *Contact email: chenxiang@mail.sysu.edu.cn

    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.