Time Differential Pricing Model of Urban Rail Transit Considering 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 applied to solve the model, and simulation results show that the proposed improved pricing model can effectively realise 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, respectively. The case study reveals that dispersing passenger flows by means of fare adjustment can effectively drop peak load and increase off-peak load. The time differential 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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