single-dr.php

JDR Vol.19 No.2 pp. 316-324
(2024)
doi: 10.20965/jdr.2024.p0316

Paper:

Development of Reward-Based Crowd Management System and Field Evaluation of Safety and Profitability

Shogo Shimizu, Ryoji Hattori, Takayuki Kodaira, Daishin Ito, and Yoshie Imai

Information Technology R&D Center, Mitsubishi Electric Corporation
5-1-1 Ofuna, Kamakura, Kanagawa 247-0056, Japan

Corresponding author

Received:
October 6, 2023
Accepted:
December 5, 2023
Published:
April 1, 2024
Keywords:
congestion reduction, Shizuoka MaaS
Abstract

For railroad operators with a highly public nature, ensuring the safety of passengers and revitalizing the areas along the line are both tasks to be accomplished. To enhance passenger safety, it is necessary to keep congestion levels below a certain level on platforms and trains. Since traffic congestion generally occurs at certain times, such as in the morning, it is important to disperse traffic congestion during rush hours. In the past, railroad operators have encouraged passengers to voluntarily commute off-peak, but this has not worked as an incentive for passengers simply by improving comfort. Because of this failure, rail operators are considering a method of setting fare prices based on the time of day and congestion levels. However, past studies suggest that few users change their behavior because those who choose the time of day to use the railroads do not always coincide with the fare payers. Therefore, we devised a crowd management system in which coupons redeemable at stores along the rail line are given directly to the customers, encouraging voluntary off-peak commuting and stimulating the local economy. This system features dynamic updating of coupon ranks based on congestion forecast information from measured congestion levels to enhance the effectiveness of the service. The results of a three-month verification experiment that cooperated with Shizuoka Railway confirmed that 7.1% of passengers took congestion avoidance actions and that passengers who obtained coupons got off at a specific station an average of 29% more often.

Cite this article as:
S. Shimizu, R. Hattori, T. Kodaira, D. Ito, and Y. Imai, “Development of Reward-Based Crowd Management System and Field Evaluation of Safety and Profitability,” J. Disaster Res., Vol.19 No.2, pp. 316-324, 2024.
Data files:
References
  1. [1] S. Ding et al., “Improved Genetic Algorithm for Train Platform Rescheduling Under Train Arrival Delays,” J. Adv. Comput. Intell. Intell. Inform., Vol.27, No.5, pp. 959-966, 2023. https://doi.org/10.20965/jaciii.2023.p0959
  2. [2] Ministry of Land, Infrastructure, Transport and Tourism (MLIT), “Sandai toshi ken no shuyo kukan no heikin konnzatsu ritsu suii,” 2023 (in Japanese).
  3. [3] Y. Takahashi, “Chihou tetsudo shinko ni kansuru kousatsu,” The Japanese J. of Transportation Economics, Vol.55, pp. 153-162, 2012 (in Japanese). https://doi.org/10.32238/koutsugakkai.55.0_153
  4. [4] M. Lovrić et al., “Evaluating off-peak pricing strategies in public transportation with an activity-based approach,” Transportation Research Record, Vol.2544, Issue 1, pp. 10-19, 2016. https://doi.org/10.3141/2544-02
  5. [5] K. Uehara, F. Nakamura, and T. Okamura, “Intention of Railway Commuters to Departure Time Shifting by Incentive Measures,” Transport Policy Studies’ Review, Vol.11, No.4, pp. 2-9, 2009 (in Japanese). https://doi.org/10.24639/tpsr.TPSR_11R_11
  6. [6] N. J. Douglas, L. Henn, and K. Sloan, “Modelling the ability of fare to spread AM peak passenger loads using rooftops,” Proc. of the 34th Australasian Transport Research Forum (ATRF), September 28-30, 2011, Adelaide, Australia, 2011.
  7. [7] A. Halvorsen, H. N. Koutsopoulos, and J. Zhao, “Reducing Subway Crowding: Analysis of an Off-Peak Discount Experiment in Hong Kong,” Transportation Research Record, Vol.2544, Issue 1, pp. 38-46, 2016. https://doi.org/10.3141/2544-05
  8. [8] S. Saharan, S. Bawa, and N. Kumar, “Dynamic Pricing Techniques for Intelligent Transportation System in Smart Cities: A Systematic Review,” Computer Communications, Vol.150, pp. 603-625, 2020. https://doi.org/10.1016/j.comcom.2019.12.003
  9. [9] “Tetsudo unchin heno dynamic pricing wo kangaeru,” Sumitomo Mitsui Trust Bank, Research Monthly Report October 2021, 2021 (in Japanese).
  10. [10] “Tsukin konzatsu kanwa ni muketa jittai haaku chosa,” Tokyo Chamber of Commerce and Industry Transportation Committee, July 30, 2021 (in Japanese).
  11. [11] Y. Kaneko, A. Fukuda, J. Koda, and Y. Chiwaki, “Analysis of the Railway Fare Elasticity in the Tokyo Metropolitan Area,” Infrastructure Planning Review, Vol.21, No.1, pp. 175-181, 2004 (in Japanese). https://doi.org/10.2208/journalip.21.175
  12. [12] T. Fujita, “Chihouken ni okeru testudo juyo ni kansuru ichi kousatsu,” Transportation Studies, No.62, pp. 45-52, 2019 (in Japanese). https://doi.org/10.32238/koutsugakkai.62.0_45
  13. [13] I. Inoue, T. Kodaira, R. Hattori, and A. Harada, “Ginza-sen shibuya-eki konzatsudo no mieruka jissho jikken,” JREA, Vol.63, No.8, pp. 44300-44304, 2020 (in Japanese).
  14. [14] “Gunshu kaiseki gijutsu,” Mitsubishi Denki Giho, Vol.95, No.1, p. 77, 2021 (in Japanese).
  15. [15] “QES S 1001: 2007,” J. of Quality Engineering Society, Vol.15, No.6, pp. 20-28, 2007.

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

Last updated on Apr. 05, 2024