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

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.

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Qin Luo ◽  
Yufei Hou ◽  
Wei Li ◽  
Xiongfei Zhang

The urban rail transit line operating in the express and local train mode can solve the problem of disequilibrium passenger flow and space and meet the rapid arrival demand of long-distance passengers. In this paper, the Logit model is used to analyze the behavior of passengers choosing trains by considering the sensitivity of travel time and travel distance. Then, based on the composition of passenger travel time, an integer programming model for train stop scheme, aimed at minimizing the total passenger travel time, is proposed. Finally, combined with a certain regional rail line in Shenzhen, the plan is solved by genetic algorithm and evaluated through the time benefit, carrying capacity, and energy consumption efficiency. The simulation result shows that although the capacity is reduced by 6 trains, the optimized travel time per person is 10.34 min, and the energy consumption is saved by about 16%, which proves that the proposed model is efficient and feasible.


2020 ◽  
Vol 146 ◽  
pp. 106594 ◽  
Author(s):  
Yunchao Qu ◽  
Huan Wang ◽  
Jianjun Wu ◽  
Xin Yang ◽  
Haodong Yin ◽  
...  

Energies ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 782
Author(s):  
Na Zhang ◽  
Zijia Wang ◽  
Feng Chen ◽  
Jingni Song ◽  
Jianpo Wang ◽  
...  

There are increasing traffic pollution issues in the process of urbanization in many countries; urban rail transit is low-carbon and widely regarded as an effective way to solve such problems. The passenger flow proportion of different transportation types is changing along with the adjustment of the urban traffic structure and a growing demand from passengers. The reduction of carbon emissions brought about by rail transit lacks specific quantitative research. Based on a travel survey of urban residents, this paper constructed a method of estimating carbon emissions from two different scenarios where rail transit is and is not available. This study uses the traditional four-stage model to forecast passenger volume demand at the city level and then obtains the basic target parameters for constructing the carbon emission reduction model, including the trip origin-destination (OD), mode, and corresponding distance range of different modes on the urban road network. This model was applied to Baoji, China, where urban rail transit will be available from 2023. It calculates the changes in carbon emission that rail transit can bring about and its impact on carbon emission reductions in Baoji in 2023.


2020 ◽  
Vol 12 (7) ◽  
pp. 2758 ◽  
Author(s):  
Chaoda Xie ◽  
Xifu Wang ◽  
Daisuke Fukuda

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.


2012 ◽  
Vol 253-255 ◽  
pp. 2020-2023
Author(s):  
Xue Jun Liu ◽  
Chuan Min Mi ◽  
Yuan Yuan Wang ◽  
Wei Vivien Shi

An analytical model and a solution procedure are proposed to optimize train size and headway for an urban rail transit system wherein passengers demand is assumed to be sensitive to fare and the level of service, in terms of load factor and waiting time. It is found that an optimal temporal headway and train size for different demand distribution that maximizes profit and minimize total system cost for the studied subway system can be efficiently solved.


2013 ◽  
Vol 361-363 ◽  
pp. 1963-1966
Author(s):  
Wei Zhu

An integrated assignment model for urban rail transit (URT) networks was proposed and discussed in four typical scenarios with the consideration of passenger difference between native and non-native. An overall algorithm framework for the model was also developed, which introduced three critical route choice models and combined them appropriately to different scenarios. A case study was performed on a real-scale network of Shanghai during the Expo 2010. The results revealed that the proposed model can deliver more appropriate solution to the assignment problem compared to the existing practice in the real world.


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