Integrated Train Timetabling and Rolling Stock Scheduling Model Based on Time-Dependent Demand for Urban Rail Transit

2017 ◽  
Vol 32 (10) ◽  
pp. 856-873 ◽  
Author(s):  
Yixiang Yue ◽  
Juntao Han ◽  
Shifeng Wang ◽  
Xiang Liu
Keyword(s):  
Urban Rail Transit ◽  
Time Dependent ◽  
Rolling Stock ◽  
Rail Transit ◽  
Train Timetabling ◽  
Model Based ◽  
Scheduling Model ◽  
Urban Rail ◽  
Dependent Demand

scholarly journals A COMPUTATION METHOD ON TIME-DEPENDENT ACCESSIBILITY OF URBAN RAIL TRANSIT NETWORKS FOR THE LAST SERVICE

Transport ◽  
2020 ◽  
Vol 35 (1) ◽  
pp. 26-36
Author(s):  
Yao Chen ◽  
Baohua Mao ◽  
Yun Bai ◽  
Zhujun Li ◽  
Jimeng Tang
Keyword(s):  
Urban Rail Transit ◽  
Time Dependent ◽  
Computation Method ◽  
Rail Transit ◽  
Practical Applications ◽  
Transit Networks ◽  
Urban Rail ◽  
Accessibility Problem ◽  
Transfer Stations ◽  
Label Setting Algorithm

Urban rail transit networks seldom provide 24-hour service. The last train is the latest chance for passengers. If passengers arrive too late to catch the last train, the path becomes inaccessible. The network accessibility thus varies depending on the departure time of passenger trips. This paper focuses on the computation method on the time-dependent accessibility of urban rail transit networks in order to facilitate the itinerary planning of passengers. A label setting algorithm is first designed to calculate the latest possible times for Origin–Destination (O–D) pairs, which is the latest departure times of passengers from the origins such that the destinations can be reach successfully. A searching approach is then developed to find the shortest accessible path at any possible departure times. The method is applied in a real-world metro network. The results show that the method is a powerful tool in solving the service accessibility problem. It has the ability to allow passengers to plan an optimal itinerary. Comparison analysis indicates that the proposed method can provide exact solutions in much shorter time, compared with a path enumeration method. Extensive tests on a set of random networks indicate that the method is efficient enough in practical applications. The execution time for an O–D pair on a personal computer with 2.8 GHZ CPU and 4GB of RAM is only 1.2 s for urban rail transit networks with 100 transfer stations.

scholarly journals Train timetabling for an urban rail transit line using a Lagrangian relaxation approach

2016 ◽  
Vol 40 (23-24) ◽  
pp. 9892-9913 ◽  
Author(s):  
Erfan Hassannayebi ◽  
Seyed Hessameddin Zegordi ◽  
Masoud Yaghini
Keyword(s):  
Lagrangian Relaxation ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Train Timetabling ◽  
Urban Rail

scholarly journals Urban Rail Transit Scheduling under Time-Varying Passenger Demand

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Xing Zhao ◽  
Zhongyan Hou ◽  
Jihuai Chen ◽  
Yin Zhang ◽  
Junying Sun
Keyword(s):  
Urban Rail Transit ◽  
Load Factor ◽  
Rail Transit ◽  
Time Interval ◽  
Train Timetable ◽  
Passenger Demand ◽  
Scheduling Model ◽  
Urban Rail ◽  
Proposed Model ◽  
Transit Scheduling

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.

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