International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume XLII-4/W19
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W19, 101–108, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W19-101-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W19, 101–108, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W19-101-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  23 Dec 2019

23 Dec 2019

SPATIAL AND TEMPORAL COMMUNITY DETECTION OF CAR MOBILITY NETWORK IN METRO MANILA

B. G. Carcellar III1,2, A. C. Blanco1, and M. Nagai2 B. G. Carcellar III et al.
  • 1Department of Geodetic Engineering, College of Engineering, University of the Philippines, Diliman, Philippines
  • 2Department of Construction and Environmental Engineering, Graduate School of Science and Technology for Innovation, Yamaguchi University, Japan

Keywords: Travel Pattern, Uber Movement, Spatial Graphs, Urban Structure

Abstract. Transportation Network Companies (TNCs) like Uber utilize GPS and wireless connection for passenger pickup, driver navigation, and passenger drop off. Location-based information from Uber in aggregated form has been made publicly available. They capture instantaneous traffic situation of an area, which makes describing spatiotemporal traffic characteristics of the area possible. Such information is valuable, especially in highly urbanized areas like Manila that experience heavy traffic. In this research, a methodology for identifying the underlying city structure and traffic patterns in Metro Manila was developed from the Uber trip information. The trip information was modelled as a complex network and Infomap community detection was utilized to group areas with ease of access. From Uber trip dataset, the data was segregated into different hours-of-day and for each hour-of-day, a directed-weighted temporal network was generated. Hours-of-day with similar traffic characteristics were also grouped together to form hour groups. From the results of the network characterization, hours-of-day were grouped into six hour groups; 00 to 04 hours-of-day in hour group 1, 05 to 07 hours-of-day in group 2, 08 to 12 hours-of-day in group 3, 13 to 15 in group 4, 16 to 19 in group 5, and 20 to 23 in group 6. Major roads as well as river networks were observed to be the major skeleton and boundaries of the generated clusters.