Predicting the Number of Passengers of MRT Jakarta Based on the Use of the QR-Code Payment Method during the Covid-19 Pandemic Using Long Short-Term Memory

Authors

  • Riyanto Jayadi Bina Nusantara University http://orcid.org/0000-0002-6656-8817
  • Taskia Fira Indriasari Bina Nusantara University
  • Charis Chrisna Institut Teknologi Sepuluh Nopember
  • Putri Natasya Fanuel Bina Nusantara University
  • Rayhana Afita Bina Nusantara University

DOI:

https://doi.org/10.26594/register.v8i2.2546

Keywords:

Machine Learning, Long Short-Term Memory, CNN, Prediction, QR code

Abstract

The trend of using public transportation has been rising over the last several decades. Because of increased mobility, public transportation has now become more crucial. In modern environments, public transportation is not only used to carry people and products from one location to another but has also evolved into a service company. In Jakarta, Mass Rapid Transit Jakarta (MRTJ) started to operate in late 2019. Recently, they updated their payment gateway system with QR codes. In this study, we predicted the hourly influx of passengers who used QR codes as their preferred payment method. This research applied machine learning to perform a prediction methodology, which is proposed to predict the number of passengers using time-series analysis. The dataset contained 7760 instances across different hours and days in June 2020 and was reshaped to display the total number of passengers each hour. Next, we incorporated time-series regression alongside LSTM frameworks with variations in architecture. One architecture, the 1D CNN-LSTM, yielded a promising prediction error of only one to two passengers for every hour.

References

M. Isradi, H. Dwiatmoko, M. D. R. Putri, R. Hidayatullah, and J. Prasetijo, “Analysis Of Effectiveness Service Of Public Transportation Mass Rapid Transit Or MRT Case Study Lebak Bulus–Bundaran HI.”

A. F. Dahlan and A. Fraszczyk, “Public Perceptions of a New MRT Service: a Pre-launch Study in Jakarta,” Urban Rail Transit, vol. 5, no. 4, pp. 278–288, 2019.

C. Zhong et al., “Variability in regularity: Mining temporal mobility patterns in London, Singapore and Beijing using smart-card data,” PLoS One, vol. 11, no. 2, p. e0149222, 2016.

W. Setyaningsih, “MRT Jakarta Passengers Reach More Than 19 Million,” BeritaJakarta.ID, Jakarta, 29-Nov-2019.

I. Atmawidjaja, “Bolstering financial inclusion in Indonesia How QR Codes can drive digital payments and enable financial inclusion,” 2018.

C. Shuran and Y. Xiaoling, “A New Public Transport Payment Method Based on NFC and QR Code,” in 2020 IEEE 5th International Conference on Intelligent Transportation Engineering (ICITE), 2020, pp. 240–244.

P. Suebtimrat and R. Vonguai, “An Investigation of Behavioral Intention Towards QR Code Payment in Bangkok, Thailand,” J. Asian Financ. Econ. Bus., vol. 8, no. 1, pp. 939–950, 2021.

L.-Y. Yan, G. W.-H. Tan, X.-M. Loh, J.-J. Hew, and K.-B. Ooi, “QR code and mobile payment: The disruptive forces in retail,” J. Retail. Consum. Serv., vol. 58, p. 102300, 2021.

K. Mulia, “Indonesia imposes standard QR code, fixed fees for e-wallets,” Tech in Asia, 2020. [Online]. Available: https://www.techinasia.com/indonesia-imposes-standard-qr-code-fixed-fees-wallets. [Accessed: 08-Jan-2020].

A. Kim, “RFi Group Insight - Asia: QR Code payments shaping into relevance,” RFi Group, 2017. [Online]. Available: https://www.rfigroup.com/rfi-group/news/rfi-group-insight-asia-qr-code-payments-shaping-relevance. [Accessed: 10-Aug-2020].

Bank Indonesia, “Indonesia Payment Systems Blueprint 2025 Bank Indonesia?: Navigating the National Payment Systems in the Digital Era,” 2019.

S. Das, A. Dey, A. Pal, and N. Roy, “Applications of artificial intelligence in machine learning: review and prospect,” Int. J. Comput. Appl., vol. 115, no. 9, 2015.

K. Agrawal, K. Maldanna, and G. N. Raj, “Taxi Demand Prediction System Using Machine Learning.”

Y. Liu, Z. Liu, and R. Jia, “DeepPF: A deep learning based architecture for metro passenger flow prediction,” Transp. Res. Part C Emerg. Technol., vol. 101, pp. 18–34, 2019.

T. Cristóbal, J. J. Lorenzo, and C. R. García, “Using data mining to improve the public transport in Gran Canaria Island,” in International Conference on Computer Aided Systems Theory, 2015, pp. 781–788.

F. Toqué, M. Khouadjia, E. Come, M. Trepanier, and L. Oukhellou, “Short & long term forecasting of multimodal transport passenger flows with machine learning methods,” in 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), 2017, pp. 560–566.

X. U. Haitao, P. Jiaxue, N. Zheng, and G. Ying, “Short-term BRT Passenger flow prediction with a deep learning method,” Int. J. Simulation--Systems, Sci. Technol., vol. 17, no. 40, pp. 1–6, 2016.

C. Yang, F. Yan, and S. V Ukkusuri, “Unraveling traveler mobility patterns and predicting user behavior in the Shenzhen metro system,” Transp. A Transp. Sci., vol. 14, no. 7, pp. 576–597, Aug. 2018.

X. Xu, L. Xie, H. Li, and L. Qin, “Learning the route choice behavior of subway passengers from AFC data,” Expert Syst. Appl., vol. 95, pp. 324–332, Apr. 2018.

C. Espinoza and B. Bustos, “Assessing the public transport travel behavior consistency from smart card data,” Transp. Res. Procedia, vol. 32, pp. 44–53, Jan. 2018.

S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Comput., vol. 9, no. 8, pp. 1735–1780, 1997.

P. McCullagh, “Generalised Linear Models,” in Breakthroughs in Statistics: Methodology and Distribution, S. Kotz and N. L. Johnson, Eds. New York, NY: Springer New York, 1992, pp. 543–546.

T. Chen and C. Guestrin, “Xgboost: A scalable tree boosting system,” in Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, 2016, pp. 785–794.

L. Breiman, “Random forests,” Mach. Learn., vol. 45, no. 1, pp. 5–32, Oct. 2001.

K. Simonyan, S. Dieleman, A. Senior, and A. Graves, “WaveNet,” arXiv Prepr. arXiv1609.03499v2, pp. 1–15, 2016.

Additional Files

Published

2022-12-26

How to Cite

[1]
R. Jayadi, T. F. Indriasari, C. Chrisna, P. N. Fanuel, and R. Afita, “Predicting the Number of Passengers of MRT Jakarta Based on the Use of the QR-Code Payment Method during the Covid-19 Pandemic Using Long Short-Term Memory”, Register: Jurnal Ilmiah Teknologi Sistem Informasi, vol. 8, no. 2, pp. 142–155, Dec. 2022.

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