Demand-responsive passenger flow control strategies for metro networks considering service fairness and passengers’ behavioural responses
mode - subway/metro, place - asia, place - urban, ridership - behaviour, ridership - demand, ridership - mode choice, ridership - perceptions, operations - performance, planning - methods, planning - surveys, planning - travel demand management
Operation management, Inflow control, MPEC, Departure time choice, Modal shift, Stochastic user equilibrium
This paper presents a methodology for developing demand-responsive passenger flow control strategies for oversaturated metro networks. A stated preference-off-revealed preference survey was conducted to capture passengers’ behavioural responses to the passenger flow control strategies, and revealed significant behavioural change intentions in terms of departure times and mode choices. Currently, such behavioural responses are often ignored, leading to deviations in estimating the size of a target group and performance of a proposed strategy. To address this issue, in this study, a mathematical programming with equilibrium constraints (MPEC) approach was developed to optimise demand-responsive passenger flow control strategies. The proposed MPEC model highlights the importance of balancing the operational efficiency of a metro system with service fairness perceived by passengers. In particular, a nested logit-based stochastic user equilibrium problem was included to accommodate potential changes in demand patterns driven by candidate passenger flow control strategies. Two empirical cases based on the Guangzhou and Beijing metros were used to demonstrate the effectiveness of the proposed model and solution algorithm. The results show that the inclusion of service fairness considerations does not contradict the pursuit of efficiency. Instead, dual emphases on service fairness and passengers’ behavioural responses help create a win–win situation for both metro operators and passengers. In the Guangzhou and Beijing metros, the highest section load rate decreased from 126.6% to 105.7% and from 122.98% to 106.87%, respectively, under the optimal passenger flow control strategies. The corresponding network load Gini coefficients improved from 0.278 to 0.259 and from 0.269 to 0.248, demonstrating the remarkable performance of this approach in regards to peak cutting and load balancing.
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Huan, N., Yao, E., & Zhang, J. (2021). Demand-responsive passenger flow control strategies for metro networks considering service fairness and passengers’ behavioural responses. Transportation Research Part C: Emerging Technologies, Vol. 131, 103335.