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


  • 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




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


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.


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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, 34(4), 609–618. https://doi.org/10.7307/ptt.v34i4.4017