Notice of Retraction: An optimal crew scheduling model for urban rail transit

Author(s):  
Zhou Feng ◽  
Xu Ruihua
CICTP 2017 ◽  
2018 ◽  
Author(s):  
Rudong Yang ◽  
Wenbin Wang ◽  
Wei Zhagn ◽  
Yulong Zhou

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Xing Zhao ◽  
Zhongyan Hou ◽  
Jihuai Chen ◽  
Yin Zhang ◽  
Junying Sun

In view of the conflict between the time-variation of urban rail transit passenger demand and the homogeneity of the train timetable, this paper takes into account the interests of both passengers and operators to build an urban rail transit scheduling model to acquire an optimized time-dependent train timetable. Based on the dynamic passenger volumes of origin-destination pairs from the automatic fare collection system, the model focuses on minimizing the total passenger waiting time with constraints on time interval between two consecutive trains, number and capacity of trains available, and load factor of trains. A hybrid algorithm which consists of the main algorithm based on genetic algorithm and the nested algorithm based on train traction calculation and safety distance requirement is designed to solve the model. To justify the effectiveness and the practical value of the proposed model and algorithm, a case of Nanjing Metro Line S1 is illustrated in this paper. The result shows that the optimized train timetable has advantage compared to the original one.


2013 ◽  
Vol 397-400 ◽  
pp. 2511-2516
Author(s):  
Yi Jun Li ◽  
Yu Fang

Urban rail transit plays an important role in daily life and optimal emergency scheduling determines social and economic benefits. To solve emergency scheduling problem of urban rail transit, a mathematical scheduling model is proposed in this paper. Based on the characteristic of urban rail transit, this scheduling model considers the benefits of both passengers and rail transit operator. Furthermore, on the basis of the model, three sorts of emergencies, such as closure of stations, outage of metro lines and surge of passenger flow are described and formulated, thus, model is solved through several GA operations. The testing results show that the model can accurately reflect the change of rail transit network during emergencies and quickly provide an effective emergency scheduling scheme.


CICTP 2020 ◽  
2020 ◽  
Author(s):  
Zhao Gao ◽  
Min Yang ◽  
Guoqiang Li ◽  
Jinghua Tai

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