Time Differential Pricing Model of Urban Rail Transit Considering Passenger Exchange Coefficient

Authors

  • Qiushi Zhang School of Urban Rail Transit and Logistics, Beijing Union University
  • Jing Qi School of Tourism, Beijing Union University
  • Yongtian Ma School of Urban Rail Transit and Logistics, Beijing Union University
  • Jiaxiang Zhao School of Urban Rail Transit and Logistics. Beijing Union University
  • Jianjun Fang School of Urban Rail Transit and Logistics, Beijing Union University

DOI:

https://doi.org/10.7307/ptt.v34i4.4017

Keywords:

urban rail transit, time differential pricing, bi-level programming model, passenger exchange coefficient

Abstract

Passenger exchange coefficient is a significant factor which has great impact on the pricing model of urban rail transit. This paper introduces passenger exchange coefficient into a bi-level programming model with time differential pricing for urban rail transit by analysing variation regularity of passenger flow characteristics. Meanwhile, exchange cost coefficient is also considered as a restrictive factor in the pricing model. The improved particle swarm optimisation algorithm (IPSO) was ap-plied to solve the model, and simulation results show that the proposed improved pricing model can effectively re-alise stratification of fares for different time periods with different routes. Taking Line 2 and Line 8 of the Beijing rail transit network as an example, the simulation result shows that passenger flows of Line 2 and Line 8 in peak hours decreased by 9.94% and 19.48% and therefore increased by 32.23% and 44.96% in off-peak hours, re-spectively. The case study reveals that dispersing pas-senger flows by means of fare adjustment can effectively drop peak load and increase off-peak load. The time dif-ferential pricing model of urban rail transit proposed in this paper has great influences on dispersing passenger flow and ensures safety operation of urban rail transit. It is also a valuable reference for other metropolitan rail transit operating companies.

References

Currie G. Quick and effective solution to rail overcrowding: Free early bird ticket experience in Melbourne. Transportation Research Record. 2010;2146(1): 35-42. doi: 10.3141/2146-05.

Sharaby N, Shiftan Y. The impact of fare integration on travel behavior and transit ridership. Transport Policy. 2012;21: 63-70. doi: 10.1016/j.tranpol.2012.01.015.

Kamel I, Shalaby A, Abdulhai B. A modelling platform for optimizing time-dependent transit fares in large-scale multimodal networks. Transport Policy. 2020;92: 38-54. doi: 10.1016/j.tranpol.2020.04.002.

Yook D, Heaslip K. Determining appropriate fare levels for distance-based fare structure: Considering users’ behaviors in a time-expanded network. Transportation Research Record. 2014;2415(1): 127-135. doi: 10.3141/2415-14.

Borndörfer R, Hoang ND. Fair ticket pricing in public transport as a constrained cost allocation game. Annals of Operations Research. 2015;226(1): 51-68. doi: 10.1007/s10479-014-1698-z.

Zhang XQ, Liu D, Wang B. Pricing methods for express rail freight under multi-modal competition (in Chinese). Transportation System Engineering and Information. 2016;16(05): 27-32. https://kns.cnki.net/kcms/detail/detail.aspx?FileName=YSXT201605004&DbName=CJFQ2016 [Accessed 15th Oct. 2016].

Liu M, Wang J. Pricing method of urban rail transit considering the optimization of passenger transport structure (in Chinese). Journal of Transportation Systems Engineering and Information Technology. 2017;17(03): 53-59. https://kns.cnki.net/kcms/detail/detail.aspx?FileName=YSXT201703009&DbName=CJFQ2017 [Accessed 15th June 2017].

Cheraghalipour A, Paydar MM, Hajiaghaei-Keshteli M. Designing and solving a bi-level model for rice supply chain using the evolutionary algorithms. Computers and Electronics in Agriculture. 2019;162: 651-668. doi: 10.1016/j.compag.2019.04.041.

Hajiaghaei-Keshteli M, Fathollahi-Fard AM. A set of efficient heuristics and metaheuristics to solve a two-stage stochastic bi-level decision-making model for the distribution network problem. Computers & Industrial Engineering. 2018;123: 378-395. doi: 10.1016/j.cie.2018.07.009.

Liu XW. Research on urban rail transit fare optimization method based on elastic demand. PhD thesis. Beijing Jiaotong University; 2016.

Fallah Tafti M, Ghane Y, Mostafaeipour A. Application of particle swarm optimization and genetic algorithm techniques to solve bi-level congestion pricing problems. International Journal of Transportation Engineering. 2018;5(3): 261-273. doi: 10.22119/ijte.2018.47767.

Hao P, Cheng X. Application of particle swarm algorithm in railroad double-layer planning model solving (in Chinese). Computer Knowledge and Technology. 2017;13(26): 238-239+242. https://kns.cnki.net/kcms/detail/detail.aspx?FileName=DNZS201726106&DbName=CJFQ2017 [Accessed 15th Sep. 2017].

Zong HX. Research on cooperative flow limiting model and algorithm for multiple entry gates of subway during peak period. PhD thesis. Beijing Jiaotong University; 2020.

Zhang Y, Li X. Research on the calculation method of average distance of urban rail transportation (in Chinese). Heilongjiang Transportation Science and Technology. 2018;41(05):159-160. https://kns.cnki.net/kcms/detail/detail.aspx?FileName=HLJJ201805096&DbName=CJFQ2018 [Accessed 15th May 2018].

Beijing Institute of Transportation Development. Annual report on transportation development in Beijing. 2020. https://www.bjtrc.org.cn/List/index/cid/7.html [Accessed 1st July 2020].

Zhuang Y. Research on urban public transportation time differential pricing model. PhD thesis. Southeast University; 2016.

China Urban Rail Transit Association. Urban Rail Transit 2019 Annual Statistics and Analysis Report. 2020. https://www.camet.org.cn/tjxx/5133 [Accessed 7th May 2020].

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Published

12-07-2022

How to Cite

Zhang, Q., Qi, J., Ma, Y., Zhao, J., & Fang, J. (2022). Time Differential Pricing Model of Urban Rail Transit Considering Passenger Exchange Coefficient. Promet - Traffic&Transportation, 34(4), 609–618. https://doi.org/10.7307/ptt.v34i4.4017

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Section

Articles