Model and Algorithm of Train Timetable Designing of Full-Length & Short-Turn Operation Mode in Urban Rail Transit

2020 ◽  
Vol 09 (04) ◽  
pp. 450-457
Author(s):  
媛媛 王
Keyword(s):  
Operation Mode ◽  
Urban Rail Transit ◽  
Full Length ◽  
Rail Transit ◽  
Train Timetable ◽  
Urban Rail

Robust optimization of train timetable and energy efficiency in urban rail transit: A two-stage approach

2020 ◽  
Vol 146 ◽  
pp. 106594 ◽  
Author(s):  
Yunchao Qu ◽  
Huan Wang ◽  
Jianjun Wu ◽  
Xin Yang ◽  
Haodong Yin ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Robust Optimization ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Two Stage ◽  
Train Timetable ◽  
Urban Rail

scholarly journals On the Pricing of Urban Rail Transit with Track Sharing Freight Service

2020 ◽  
Vol 12 (7) ◽  
pp. 2758 ◽  
Author(s):  
Chaoda Xie ◽  
Xifu Wang ◽  
Daisuke Fukuda
Keyword(s):  
Urban Rail Transit ◽  
Reduced Form ◽  
Rail Transit ◽  
Time Varying ◽  
Train Timetable ◽  
Passenger Rail ◽  
Urban Rail ◽  
Optimal Service ◽  
Increasing Demand ◽  
Timetable Problem

Transporting parcels on urban passenger rail transit is gaining growing interest as a response to the increasing demand and cost of urban parcel delivery. To analyze the welfare effects of different fare regimes when allowing parcel services on an urban rail transit, this paper models the optimal service problem where the transit operator chooses the number of trains and the departure intervals. By introducing a reduced form train timetable problem, the passenger train crowding model is extended to incorporate the effect of freight train scheduling. We show that the freight users are better off in the time-varying optimal fare regime, while passengers are worse off, and that the time-varying optimal fare regime calls for more trains than the optimal uniform fare regime. However, the reduction in passenger trains due to the introduction of freight service can eliminate the welfare gain from passenger time-varying fare. If the price elasticity of freight demand is relatively high, implementing road toll can generate welfare loss when rail transit is privately operated.

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.

scholarly journals Last train timetable optimization considering detour routing strategy in an urban rail transit network

2019 ◽  
Vol 52 (9-10) ◽  
pp. 1461-1479
Author(s):  
Yu Yao ◽  
Xiaoning Zhu ◽  
Hua Shi ◽  
Pan Shang
Keyword(s):  
Waiting Time ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Train Timetable ◽  
Routing Strategy ◽  
Urban Rail ◽  
Level Model ◽  
Upper Level ◽  
Urban Rail Transit Network ◽  
Timetable Optimization

As an important means of transportation, urban rail transit provides effective mobility, sufficient punctuality, strong security, and environment-friendliness in large cities. However, this transportation mode cannot offer a 24-h service to passengers with the consideration of operation cost and the necessity of maintenance, that is, a final time should be set. Therefore, operators need to design a last train timetable in consideration of the number of successful travel passengers and the total passenger transfer waiting time. This paper proposes a bi-level last train timetable optimization model. Its upper level model aims to maximize the number of passengers who travel by the last train service successful and minimize their transfer waiting time, and its lower level model aims to determine passenger route choice considering the detour routing strategy based on the last train timetable. A genetic algorithm is proposed to solve the upper level model, and the lower level model is solved by a semi-assignment algorithm. The implementation of the proposed model in the Beijing urban rail transit network proves that the model can optimize not only the number of successful transfer directions and successful travel passengers but also the passenger transfer waiting time of successful transfer directions. The optimization results can provide operators detailed information about the stations inaccessible to passengers from all origin stations and uncommon path guides for passengers of all origin–destination pairs. These types of information facilitate the operation of real-world urban rail transit systems.

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