Isaac Scientific Publishing

Frontiers in Management Research

Research on Passenger Flow Characteristics of Rail Station Based on Mobile Signaling Data

Download PDF (1116.5 KB) PP. 100 - 107 Pub. Date: October 8, 2018

DOI: 10.22606/fmr.2018.24004

Author(s)

  • Lilei Wang*
    School of Transportation and Logistics, Southwest Jiaotong University, No. 111 North Second Ring Road, Chengdu, Sichuan 610031, China

Abstract

As a kind of transport big data, mobile signaling data has been widely used in various fields of urban transport research with the characteristics of wide coverage, real-time dynamic and low acquisition costs. This paper constructs the method of extracting the travel information of the rail transit passenger flow on the basis of summarizing the shortage of the existing algorithms for recognizing the travel path of the rail transit. In the entering-station identification, signaling data generated by different signaling events with location area updating are used to identify key trajectory entering-points based on space-time sequences between adjacent key trajectory points and the location information of the rail transit cell. In the leaving-station identification, a method based on the principle of proximity was proposed which combined with the cell information near the orbital station. The results show that passenger flow characteristic information of rail transit can be obtained accurately according to the phone signaling data. The passenger flow distribution characteristics acquired can meet the nature of land use around the station.

Keywords

Urban rail transit, cell phone signaling, passenger flow characteristics

References

[1] Fei Yang, Zhenxing Yao and Peter J. Jin. Multi-mode trip information recognition based on wavelet transform modulus maximum algorithm by using gps and acceleration data. Transportation Research Record, 2015.

[2] Systems R E, Farradyne P B. Final evaluation report for the Capital-ITS operational test and demonstration program, 2007.

[3] White J, Ivan W. Extracting origin destination information from mobile phone data. Road Transport Information and Control,Eleventh International Conference on (Conf. Publ. No. 486). IET, 2002.

[4] Carlo Ratti, Riccardo M. Pulselli, Sarah Williams, Dennis Frenchman. Mobile landscapes using location data from cell-phones for urban analysis. Senseable City Laboratory Massachusetts Institute of Technology, 2007.

[5] Carlo Ratti, Pinelli Fabio, Hou Anyang. Space and time-dependant bus accessibility a case study in Rome. The 12th International IEEE Conference on Intelligent Transportation Systems. 2009, 10: 346-351.

[6] Calabrese F, Colonna M, and Lovisolo P. Real-time urban monitoring using cell phones a case study in rome. Intelligent Transportation Systems, Vol. 12, No. 1, 2011, pp. 141-151.

[7] Bi F M, Wang W K, Chen L. DBSCAN: Density-based spatial clustering of applications with noise. Journal of Nanjing University, Vol. 48, No. 4, 2012, pp. 491-498.

[8] Ester M, Kriegel H P, Xu X. A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise. International Conference on Knowledge Discovery and Data Mining. AAAI Press, 1996, pp. 226-231.