JDR Vol.15 No.3 pp. 451-460
doi: 10.20965/jdr.2020.p0451


Measuring Traffic Congestion Based on the Taxi Operations of Traditional and On-Demand Taxis in Yangon

Moe Myint Mo*,†, Kyaing*, Ko Ko Lwin**, and Yoshihide Sekimoto**

*Yangon Technological University
Insein Road, Gyogone, Insein, Yangon 11011, Myanmar

Corresponding author

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

July 31, 2019
February 27, 2020
March 30, 2020
taxi trips, GPS, GIS, taxi travel time, traffic congestion

The current urbanization and motorization have caused a gradual negative impact on the existing transport infrastructure in Yangon City. Currently, the road network throughout Yangon operates at or above its desired capacity during the peak periods. At present, there are over 62,886 registered taxis operating in Yangon City. These taxis provide two different services to passengers: non-metered taxi (traditionally hailed on the street) service and metered taxi (on demand) service. Private cars and taxis constitute 70% of the modes of transport in Yangon City; this may lead to traffic congestion. However, there is lack of relevant data and taxi trip pattern information on how taxi service is related to traffic congestion. Therefore, studies on taxi surveying using Global Positioning Systems (GPS) need to be conducted, and investigations on the effect of taxi services on traffic congestion from these GPS data need to be performed. This study explores the comparison between hourly and daily trips’ frequencies as well as spatial and temporal variations of taxi trips between the two services. Field survey data collected through the GPS and Geographic Information System (GIS) were used to estimate the different taxi travel times that can be applied in predicting the occupied and vacant times in the study area. The specific objective of this research is to examine vacant taxi movement and stationary time (parking time and congestion time) of the two services to quantify the impact of taxi travel time on traffic congestion in Yangon. Moreover, by knowing how the two services vary in terms of operation, the main solution for reducing the congestion in Yangon City can be established. Further, the taxi stationary duration information is useful for knowing the taxi trip hotspot points in each township in Yangon. This may lead to support in defining proposed taxi stands in Yangon City.

Cite this article as:
M. Mo, Kyaing, K. Lwin, and Y. Sekimoto, “Measuring Traffic Congestion Based on the Taxi Operations of Traditional and On-Demand Taxis in Yangon,” J. Disaster Res., Vol.15 No.3, pp. 451-460, 2020.
Data files:
  1. [1] Z. Liao, “Real-time taxi dispatching using Global Positioning Systems,” Communications of the ACM, Vol.46, No.5, pp. 81-83, 2003.
  2. [2] B. Cule, B. Goethals, S. Tassenoy, and S. Verboven, “Mining train delays,” Proc. of the 10th Int. Conf. on Intelligent Data Analysis (IDA 2011), pp. 113-124, 2011.
  3. [3] L. Matias, J. Gama, J. Mendes-Moreira, and J. Freire de Sousa, “Validation of both number and coverage of bus schedules using AVL data,” Proc. of the 13th Int. Conf. on Intelligent Transportation Systems, pp. 131-136, 2010.
  4. [4] L. Moreira-Matias, C. Ferreira, J. Gama, J. Mendes-Moreira, and J. F. de Sousa, “Bus Bunching Detection by Mining Sequences of Headway Deviations,” Proc. of the 18th Industrial Conf. on Advances in Data Mining (ICDM 2018), pp. 77-91, 2012.
  5. [5] X. Li, G. Pan, Z. Wu, G. Qi, S. Li, D. Zhang, W. Zhang, and Z. Wang, “Prediction of urban human mobility using large-scale taxi traces and its applications,” Frontiers of Computer Science, Vol.6, No.1, pp. 111-121, 2012.
  6. [6] Z. Deng and M. Ji, “Spatiotemporal structure of taxi services in Shanghai: Using exploratory spatial data analysis,” Proc. of the 2011 19th Int. Conf. on Geoinformatics, pp. 1-5, 2011.
  7. [7] L. Liu, C. Andris, A. Biderman, and C. Ratti, “Uncovering taxi drivers mobility intelligence through his trace,” pp. 1-17, 2009.
  8. [8] Y. Yue, Y. Zhuang, Q. Li, and Q. Mao, “Mining time-dependent attractive areas and movement patterns from taxi trajectory data,” Proc. of the 2009 17th Int. Conf. on Geoinformatics, pp. 1-6, 2009.
  9. [9] J. Lee, I. Shin, and G. Park, “Analysis of the Passenger Pick-Up Pattern for Taxi Location Recommendation,” Proc. of the 2008 4th Int. Conf. on Networked Computing and Advanced Information Management (NCM2008), Vol.1, pp. 199-404, 2008.
  10. [10] S. Phithakkitnukoon, M. Veloso, C. Bento, A. Biderman, and C. Ratti, “Taxi-Aware Map: Identifying and Predicting Vacant Taxis in the City,” Proc. of the 1st Int. Joint Conf. on Ambient Intelligence (AMI’10), pp. 86-95, 2010.
  11. [11] K. I. Wong, S. C. Wong, and H. Yang, “Modeling urban taxi services in congested road networks with elastic demand,” Transportation Research Part B: Methodological, Vol.35, Issue 9, pp. 819-842, 2001.
  12. [12] H. Yang, M. Ye, W. H. Tang, and S. C. Wong, “Regulating taxi services in the presence of congestion externality,” Transport Research Part A: Policy and Practice, Vol.39, Issue 1, pp. 17-40, 2005.
  13. [13] H. Yang, S. C. Wong, and K. I. Wong, “Demand–supply equilibrium of taxi services in a network under competition and regulation,” Transport Research Part B: Methodological, Vol.36, Issue 9, pp. 799-819, 2002.

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

Last updated on Jul. 12, 2024