Analysis of Trip Distributions of Human Mobility Patterns and Their Transit Behaviors Using Mobile Call Detail Records
Kyaing*,, Ko Ko Lwin**, and Yoshihide Sekimoto**
*Department of Civil Engineering, Yangon Technological University (YTU)
Insein Road, Gyogone, Insein, Yangon 11011, Myanmar
**Institute of Industrial Science (IIS), The University of Tokyo, Tokyo, Japan
Rapid urbanization and modernization are increasing worldwide, including in Myanmar. Mobile call detail records (CDRs) provide new opportunities for measuring transport demands and problems in transportation planning. This research aims to analyze trip distributions and transit behaviors of mobile phone users based on their call activities. Origin-Destination (O-D) pairs were computed for the entire city, and the trip distributions help understand human mobility. It was found that zone-to-zone flow has the highest flow in commercial and industrial areas. Moreover, the logical assumptions were specified to extract the transit behaviors of users. The results indicate the degree of mode-to-mode transfer behaviors of users. Among the four categories of transit usage, only rail users do not transfer to other modes, having the lowest proportion, with other mode-to-other mode transfers having the highest proportion. The results were validated with the Person Trip Survey for Comprehensive Urban Transport Plan of the Greater Yangon. This study contributes significantly to the expansion of current and potential future transit systems, which can provide a new and improved transport system for Yangon City to meet its demands. This information is helpful in conducting disaster management and emergency preparedness in terms of trip distributions of human mobility patterns changing over space and time and the transit behaviors of the transferring mode in daily trips.
-  J. J. Hajek, “Optimal Sample size of Road-side-interview Origin-Destination surveys,”Ontario Ministry of Transportation and Communications, No.RR 208, 1977.
-  M. Kuwahara and E. C. Sullivan, “Estimating Origin-Destination Matrices from Roadside Survey Data,” Transportation Research Part B: Methodological, Vol.21, No.3, pp. 233-248, 1987.
-  R. M. Groves, “Nonresponse Rates and Nonresponse Bias in Household Surveys,” Public Opinion Quarterly, Vol.70, No.5, pp. 646-675, 2006.
-  E. Castillo, J. M. Menéndez, and P. Jiménezb, “Trip matrix and path flow reconstruction and estimation based on plate scanning and link observations,” Transportation Research Part B: Methodological, Vol.42, Issue 5, pp. 455-481, 2008.
-  K. Parry and M. L. Hazelton, “Estimation of origin-destination matrices from link counts and sporadic routing data,” Transportation Research Part B: Methodological, Vol.46, No.1, pp. 175-188, 2012.
-  T. Morimura and S. Kato, “Statical origin-destination generation with multiple sources,” 21st Int. Conf. on in Pattern Recognition (ICPR2012), 2012.
-  J. C. Herrera, D. B. Work, R. Herring, X. Ban, Q. Jacobson, and A. M. Bayen, “Evaluation of traffic data obtained via GPS-enabled mobile phones: The Mobile century field experiment,” Transportation Research Part C: Emerging Technologies, Vol.18, Issue 4, pp. 568-583, 2010.
-  E. N. Roslin and S. A. M. Shahadat, “A Conceptual Model for Full-Blown Implementation of Lean Manufacturing System in Malaysian Automotive Industry,” Proc. of the 2014 Int. Conf. on Industrial Engineering and Operations Management, pp. 1309-1315, 2014.
-  International Telecommunication Union, http://www.itu.int/en/ITU-D/Statistics/Documents/facts/ICTFactsFigures2013.pdf [accessed July 13, 2020]
-  M. C. González, C. A. Hidalgo, and A.-L. Barabási, “Understanding individual human mobility patterns,” Nature, Vol.453, No.7196, pp. 779-782, 2008.
-  C. Song, T. Koren, P. Wang, and A. L. Barabasi, “Modelling the scaling properties of human mobility,” Nature Physics, Vol.6, pp. 818-823, 2010.
-  J.-P. Onnela, J. Saramäki, J. Hyvönen, G. Szabö, D. Lazer, K. Kaski, J. Kertész, and A.-L. Barabási, “Structure and tie strengths in mobile communication networks,” Proc. of the National Academy of Sciences, Vol.104, No.18, pp. 7332-7336, 2007.
-  J. Reades, F. Calabrese, A. Sevtsuk, and C. Ratti, “Cellular census: Explorations in urban data collection,” IEEE Pervasive Computing, Vol.6, No.3, pp. 30-38, 2007.
-  K. K. Lwin, Y. Sekimoto, and W. Takeuchi, “Estimation of Hourly Link Population and Flow Directions from Mobile CDR,” ISPRS-Int. J. of Geo-Information, Vol.7, No.11, Article No.449, doi: 10.3390/ijgi7110449, 2018.
-  Kyaing, K. Lwin, and Y. Sekimoto, “Human Mobility Patterns for Different Regions in Myanmar Based on CDRs Data,” The 3rd Int. Conf. on Civil Engineering Research (ICCER2017), IPTEK J. Proc. Ser., No.6, 2017.
-  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, doi: 10.20965/jdr.2018.p0006, 2018..
-  Kyaing, K. Lwin, and Y. Sekimoto, “Estimation of trip generation in Yangon city by using CDRs data,” Proc. of the Eastern Asia Society for Transportation Studies, Vol.11, 2017.
-  G. Rose, “Mobile phones as traffic probes: Practices, prospects and issues,” Transport Reviews, Vol.26, No.3, pp. 275-291, 2006.
-  N. Caceres, J. P. Wideberg, and F. G. Benitez, “Review of traffic data estimations extracted from cellular networks,” Intelligent Transport Systems, Vol.2, No.3, pp. 179-192, 2008.
-  J. Doyle, P. Hung, D. Kelly, S. McLoone, and R. Farrell, “Utilising mobile phone billing records for travel mode discovery,” The Irish Signals and Systems Conf. (ISSC2011), 2011.
-  H. Ishizuka, N. Kobayashi, S. Muramatsu, and C. Ono, “Classifying the mode of transportation using cell tower alignments,” IPSJ SIG Technical Report, Vol.2015-MBL-74/2015-UBI-45, No.57, 2015.
-  H. Wang, F. Calabrese, G. Di Lorenzo, and C. Ratti, “Transportation mode inference from anonymized and aggregated mobile phone call detail records,” The 13th Int. IEEE Conf. on Intelligent Transportation Systems, pp. 318-323, 2010.
-  K. Lwin, Y. Sekimoto, and W. Takeuchi, “Development of GIS Integrated Big Data Research Toolbox (BigGIS-RTX) for Mobile CDR Data Processing in Disasters Management,” J. Disaster Res., Vol.13, No.2, pp. 380-386, 2018.
-  T. Shoyama, “Major Findings on Yangon Urban Transport and Short-Term Actions,” Yangon Urban Transport Master Plan of the Project for Comprehensive Urban Transport Plan of the Greater Yangon (YUTRA), Yangon, 2014.
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