TY - JOUR
T1 - Traffic zone division based on big data from mobile phone base stations
AU - Dong, Honghui
AU - Wu, Mingchao
AU - Ding, Xiaoqing
AU - Chu, Lianyu
AU - Jia, Limin
AU - Qin, Yong
AU - Zhou, Xuesong
N1 - Funding Information:
The authors wish to thank the State Key Laboratory of Rail Traffic Control & Safety, Beijing Jiaotong University and all team members who participated in the project. This work is supported by the National Natural Science Foundation of China (Grant No. 61104164 ), Beijing Science and Technology Nova Program (Grant Z1211106002512027 ) and the National Science and Technology Pillar Program (Grant No. 2014BAG01B02 ). We also would like to thank Ms. Lyn Long for her edition to the final paper.
PY - 2015/10/1
Y1 - 2015/10/1
N2 - Call detail record (CDR) data from mobile communication carriers offer an emerging and promising source of information for analysis of traffic problems. To date, research on insights and information to be gleaned from CDR data for transportation analysis has been slow, and there has been little progress on development of specific applications. This paper proposes the traffic semantic concept to extract traffic commuters' origins and destinations information from the mobile phone CDR data and then use the extracted data for traffic zone division. A K-means clustering method was used to classify a cell-area (the area covered by a base stations) and tag a certain land use category or traffic semantic attribute (such as working, residential, or urban road) based on four feature data (including real-time user volume, inflow, outflow, and incremental flow) extracted from the CDR data. By combining the geographic information of mobile phone base stations, the roadway network within Beijing's Sixth Ring Road was divided into a total of 73 traffic zones using another K-means clustering algorithm. Additionally, we proposed a traffic zone attribute-index to measure tendency of traffic zones to be residential or working. The calculated attribute-index values of 73 traffic zones in Beijing were consistent with the actual traffic and land-use data. The case study demonstrates that effective traffic and travel data can be obtained from mobile phones as portable sensors and base stations as fixed sensors, providing an opportunity to improve the analysis of complex travel patterns and behaviors for travel demand modeling and transportation planning.
AB - Call detail record (CDR) data from mobile communication carriers offer an emerging and promising source of information for analysis of traffic problems. To date, research on insights and information to be gleaned from CDR data for transportation analysis has been slow, and there has been little progress on development of specific applications. This paper proposes the traffic semantic concept to extract traffic commuters' origins and destinations information from the mobile phone CDR data and then use the extracted data for traffic zone division. A K-means clustering method was used to classify a cell-area (the area covered by a base stations) and tag a certain land use category or traffic semantic attribute (such as working, residential, or urban road) based on four feature data (including real-time user volume, inflow, outflow, and incremental flow) extracted from the CDR data. By combining the geographic information of mobile phone base stations, the roadway network within Beijing's Sixth Ring Road was divided into a total of 73 traffic zones using another K-means clustering algorithm. Additionally, we proposed a traffic zone attribute-index to measure tendency of traffic zones to be residential or working. The calculated attribute-index values of 73 traffic zones in Beijing were consistent with the actual traffic and land-use data. The case study demonstrates that effective traffic and travel data can be obtained from mobile phones as portable sensors and base stations as fixed sensors, providing an opportunity to improve the analysis of complex travel patterns and behaviors for travel demand modeling and transportation planning.
KW - Call detail record (CDR) data
KW - Mobile telephones
KW - Traffic semantic analysis
KW - Traffic zone attribute index
KW - Traffic zone division
KW - Travel patterns
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U2 - 10.1016/j.trc.2015.06.007
DO - 10.1016/j.trc.2015.06.007
M3 - Article
AN - SCOPUS:84940460451
VL - 58
SP - 278
EP - 291
JO - Transportation Research Part C: Emerging Technologies
JF - Transportation Research Part C: Emerging Technologies
SN - 0968-090X
ER -