JDR Vol.15 No.3 pp. 426-436
doi: 10.20965/jdr.2020.p0426


Analysis of Bus Operation at Peak Hours Using Bus GPS Data: A Case Study of YBS-36

Thet Htun Aung*,†, Kyaing*, Ko Ko Lwin**, and Yoshihide Sekimoto**

*Department of Civil Engineering, Yangon Technological University
Insein, Yangon 11011, Myanmar

Corresponding author

**Institute of Industrial Science, The University of Tokyo, Tokyo, Japan

August 1, 2019
February 18, 2020
March 30, 2020
optimization of bus-stop locations, on-board survey, check-point survey

The use of public buses constitutes the primary daily transportation mode for commuters inside the city of Yangon. The efficiency of the public bus transportation service is important to the local government in terms of public safety and energy saving. The main objective of this study is to understand the current public bus transportation problems in Yangon and to propose a new improved method for the allocation of bus stops. In this study, an on-board survey was conducted to collect bus-passenger counts. Moreover, a check-point survey was carried out to determine the passenger volume at each bus stop and to decide whether the bus stop should be relocated. Finally, a geographic information systems (GIS) model was developed to determine the optimized bus-stop locations based on the passenger volume and on various public-facility locations (such as offices and shopping centers). This study aims to support the Yangon Bus Service (YBS), a major bus transportation service in Yangon city – Myanmar, to optimize its bus network.

Cite this article as:
T. Aung, Kyaing, K. Lwin, and Y. Sekimoto, “Analysis of Bus Operation at Peak Hours Using Bus GPS Data: A Case Study of YBS-36,” J. Disaster Res., Vol.15 No.3, pp. 426-436, 2020.
Data files:
  1. [1] R. Sankar, J. Kavitha, and S. Karthi, “Optimization of Bus Stop Locations Using GIS as a Tool for Chennai City,” Map India Conf., 2003.
  2. [2] A. D. Nagne and B. W. Gawali, “Transportation Network Analysis by Using Remote Sensing and GIS a Review,” Int. J. of Engineering Research and Applications, Vol.3, Issue 3, pp. 70-76, 2013.
  3. [3] E. M. Delmelle, S. Li, and A. T. Murray, “Identifying bus stop redundancy: A gis-based spatial optimization approach,” Computers, Environment and Urban Systems, Vol.36, Issue 5, pp. 445-455, 2012.
  4. [4] A. Ceder, B. Golany, and O. Tal, “Creating bus timetables with maximal synchronization,” Transportation Research Part A: Policy and Practice, Vol.35, Issue 10, pp. 913-928, 2001.
  5. [5] C. Sun, W. Zhou, and Y. Wang, “Scheduling Combination and Headway Optimization of Bus Rapid Transit,” J. of Transportation Systems Engineering and Information Technology, Vol.8, Issue 5, pp. 61-67, 2008.
  6. [6] H. Li and R. L. Bertini, “Optimal Bus Stop Spacing for Minimizing Transit Operation Cost,” Proc. of the 6th Int. Conf. of Traffic and Transportation Studies Congress (ICTTS), pp. 553-564, 2008.
  7. [7] “Bus Stop Optimization Policy (Pilot),” Foursquare Integrated Transportation Planning & Jacobs Engineering, 2014, [accessed October 23, 2018]
  8. [8] “Bus Stop Optimization Study,” Passero Associates, 2015, [accessed October 23, 2018]
  9. [9] A. M. El-Geneidy, J. G. Strathman, T. J. Kimpel, and D. T. Crout, “Effect of Bus Stop Consolidation on Passenger Activity and Transit Operations,” Transportation Research Record: J. of the Transportation Research Board, Vol.1971, pp. 32-41, 2006.
  10. [10] Z. Huang and X. Liu, “A Hierarchical Approach to Optimizing Bus Stop Distribution in Large and Fast Developing Cities,” ISPRS Int. J. of Geo-Information, Vol.3, No.2, pp. 554-564, 2014.
  11. [11] M. N. Ibrahim and Y. Ismail, “Estimation of Bus Stops Spacing on Public Transport Routes in Kano Metropolis Using Minibus Stop Time Interval,” Int. J. of Engineering and Science Invention, Vol.2, Issue 9, pp. 36-44, 2013.

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

Last updated on May. 19, 2024