single-dr.php

JDR Vol.13 No.1 pp. 6-13
(2018)
doi: 10.20965/jdr.2018.p0006

Paper:

Estimation of Originating-Destination Trips in Yangon by Using Big Data Source

Thein Aye Zin*,†, Kyaing*, Ko Ko Lwin**, and Yoshihide Sekimoto**

*Yangon Technological University (YTU)
Insein Road, Gyogone, Insein 11011, Yangon, Myanmar

Corresponding author

**Institute of Industrial Science (IIS), University of Tokyo, Tokyo, Japan

Received:
September 1, 2017
Accepted:
February 1, 2018
Published:
February 20, 2018
Keywords:
big data, mobile phone data, the movement of people, trip estimation, Yangon
Abstract

The ubiquitous massive mobile phone data generation presents new opportunities to determine the requirements of transportation, disaster management and public health care systems. Currently, data from mobile phone records can help in identifying the location of the users while they are making trips. Generally, this estimation is achieved using traditional data collection methods; however, these methods are difficult to apply in developing countries with rapidly growing cities owing to the high population and limitation in conducting a survey. Call detail records (CDRs) are used as base data because they are valuable data sources and can reduce the cost and time limitations. The aim of this study is to estimate origin-destination (OD) trips from each zone by using the CDRs. The OD trips are estimated by using the CDRs of one week taken from Myanmar Post and Telecommunication mobile operator for over 1.9 million users per day in Yangon, the economic center of Myanmar. The OD trips are estimated from CDRs based on the location of the base station in a limited time window and time frame. If the same mobile users is observed in two different the ones within the time limit, it is assumed that the mobile user is coming out from the first zone and the trips represents an originating trip. This trip would be the destination trip for zone where the mobile user enters. In this study, the originating (outgoing) and destination trips (incoming) from each township on a weekday and weekend are determined. These data are useful for infrastructure development and urban transportation planning.

Cite this article as:
T. Zin, Kyaing, K. Lwin, and Y. Sekimoto, “Estimation of Originating-Destination Trips in Yangon by Using Big Data Source,” J. Disaster Res., Vol.13, No.1, pp. 6-13, 2018.
Data files:
References
  1. [1] M. S. Iqbal, C. F. Choudhury, P. Wang, and M. C. González, “Development of Origin-Destination Matrices Using Mobile Phone Call Data,” Transportatio Research Part C. Emerging technologies, Vol.40, pp. 63-74, ISSN 0968-0908, 2014.
  2. [2] Y. Hasegawa, Y. Sekimoto, T. Kashiyama, and H. Kanasugi, “Transportation Melting Pot Dhaka: Road-link Based Traffic Volume Estimation from Sparse CDR Data,” The 1st Int. Conf. on IoT in Urban Space (Uro-IoT 2014), Rome, 2014.
  3. [3] L. Alexander, S. Jiang, M. Murga, and M. C. Gonzalez, “Origin-destination trips by purpose and time of day inferred from mobile phone data,” Transportation Research Part C, Vol.58, pp. 240-250, 2015.
  4. [4] P. Wang, T. Hunter, A. M. Bayen, K. Schechtner, and M. C. Gonzalez, “Understanding Road Usage Patterns in urban Areas,” Scientific report, Vol.2, 2011.
  5. [5] T. V. Mathew and K. V. K. Rao, “Introduction to transportation engineering.”
  6. [6] N. Caceres, J. P. Wiedeberg, and F. G. Benitez, “Deriving Origin-Destination Data from a Mobile Phone Network,” IET Intelligent Transport System, Vol.1, No.1, pp. 15-26, 2007.
  7. [7] D. Akin and V. Sisiopiku, “Estimating Origin-Destination Matrices Using Location Information from Cellular Phones,” Proc. NARSC RSAI, Puerto Rico, USA, 2002.
  8. [8] N. Caceres, J. P. Wiedeberg, and F. G. Benitez, “Deriving Origin-Destination Data from a Mobile Phone Network,” IET Intelligent Transport System, Vol.1, No.1, pp. 15-26, 2007.
  9. [9] F. Calabrese, M. Diao, G. Di Lorenzo, J. Ferreira Jr., and C. Ratti, “Understanding individual mobility patterns from urban sensing data: mobile phone trace example,” Transportation Research Part C, Vol.26, pp. 301-313, 2013.
  10. [10] F. Calabrese, G. Di Lorenzo, L. Liu, and C. Ratti, “Estimating Origin-Destination Flows using Mobile Phone Location Data,” IEEE Pervasive, Computing, Vol.10, No.4, pp. 36-44, 2011.
  11. [11] B. Patrick, “Potential of ‘passive’ mobile phone dataset to construct origin-destination matrix,” 4th Symposium of the European Association for Research in Transportation (HEART) September 9 to 11, 2015, Copenhagen, 2015.
  12. [12] S. Jiang, J. Ferreira Jr., and M. C. Gonzalez, “Activity-Based human mobility patterns inferred from mobile phone data: A case study of Simgapore,” IEEE Transactions on big data,TBD-2015-12-0163., 2013.
  13. [13] R. A. Becker, R. Cáceres, K. J. Hanson, and S. Isaacman, “Human Mobility Characterization from Cellular Network Data,” Princeton University, Princeton, NJ, USA, 2011.
  14. [14] “Project for Comprehensive Urban Transport Plan of the Greater Yangon (YUTRA),” ALMEC Corporation, December 2014.
  15. [15] K. Lwin, Y. Sekimoto, and W. Takeuchi, “Development of GIS integrated Big Data Research Toolbox (BigGIS-RTX) for mobile CDRs Data processing in disasters management,” Journal of Disaster Research, Vol.13, No.1, 2018.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, IE9,10,11, Opera.

Last updated on Dec. 13, 2018