Collaborative Determination Method of Metro Train Plan Adjustment and Passenger Flow Control under the Impact of COVID-19

Fuquan Pan (), Jingshuang Li, Hailiang Tang, Changxi Ma, Lixia Zhang and Xiaoxia Yang
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Fuquan Pan: School of Civil Engineering, Qingdao University of Technology, Qingdao 266520, China
Jingshuang Li: School of Civil Engineering, Qingdao University of Technology, Qingdao 266520, China
Hailiang Tang: Qingdao Metro Operation Co., Ltd., Qingdao 266100, China
Changxi Ma: School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China
Lixia Zhang: School of Civil Engineering, Qingdao University of Technology, Qingdao 266520, China
Xiaoxia Yang: School of Civil Engineering, Qingdao University of Technology, Qingdao 266520, China

Sustainability, 2023, vol. 15, issue 2, 1-17

Abstract: Aiming at the problem of metro operation and passenger transport organization under the impact of the novel coronavirus (COVID-19), a collaborative determination method of train planning and passenger flow control is proposed to reduce the train load rate in each section and decrease the risk of spreading COVID-19. The Fisher optimal division method is used to determine reasonable passenger flow control periods, and based on this, different flow control rates are adopted for each control period to reduce the difficulty of implementing flow control at stations. According to the actual operation and passenger flow changes, a mathematical optimization model is established. Epidemic prevention risk values (EPRVs) are defined based on the standing density criteria for trains to measure travel safety. The optimization objectives of the model are to minimize the EPRV of trains in each interval, the passenger waiting time and the operating cost of the corporation. The decision variables are the number of running trains during the study period and the flow control rate at each station. The original model is transformed into a single-objective model by the linear weighting of the target, and the model is solved by designing a particle swarm optimization and genetic algorithm (PSO-GA). The validity of the method and the model is verified by actual metro line data. The results of the case study show that when a line is in the moderate-risk area of COVID-19, two more trains should be added to the full-length and short-turn routes after optimization. Combined with the flow control measures for large passenger flow stations, the maximum train load rate is reduced by 35.18%, and the load rate of each section of trains is less than 70%, which meets the requirements of COVID-19 prevention and control. The method can provide a theoretical basis for related research on ensuring the safety of metro operation during COVID-19.

Keywords: metro; train plan; passenger flow control; particle swarm genetic algorithm; COVID-19; train load rate (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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