scholarly journals Timetable Optimization of Rail Transit Loop Line with Transfer Coordination

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Rui-Jia Shi ◽  
Bao-Hua Mao ◽  
Yong Ding ◽  
Yun Bai ◽  
Yao Chen
Keyword(s):  
Waiting Time ◽  
Performance Indicators ◽  
Waiting Times ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Transit System ◽  
Urban Rail ◽  
Proposed Model ◽  
Timetable Optimization

In an urban rail transit system, it is important to coordinate the timetable of a loop line with its connecting lines so as to reduce the waiting time of passengers. This is particularly essential because transfer passengers usually account for the majority of the total passengers in loop lines. In this paper, a timetable optimization model is developed for loop line in order to minimize the average waiting time of access passengers and transfer passengers. This is performed by adjusting the headways and dwell times of trains on the loop line. A genetic algorithm is applied to solve the proposed model, and a numerical example is used to verify its effectiveness. Finally, a case study of a loop line in the Beijing urban rail transit system is conducted. Waiting times of the passengers and the number of waiting passengers are used as performance indicators to verify the optimization results in rush hours and non-rush hours. The results show that the average waiting times for the up-track and down-track are reduced by 3.69% and 2.89% during rush hours and by 11.60% and 11.47% during non-rush hours, respectively.

scholarly journals A Timetable Optimization Model for Urban Rail Transit with Express/Local Mode

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Renjie Zhang ◽  
Shisong Yin ◽  
Mao Ye ◽  
Zhiqiang Yang ◽  
Shanglu He
Keyword(s):  
Waiting Time ◽  
Optimization Model ◽  
Urban Rail Transit ◽  
Local Mode ◽  
Rail Transit ◽  
Long Distance ◽  
Urban Rail ◽  
Proposed Model ◽  
Distance Travel ◽  
Timetable Optimization

Nowadays, an express/local mode has be studied and applied in the operation of urban rail transit, and it has been proved to be beneficial for the long-distance travel. The optimization of train patterns and timetables is vital in the application of the express/local mode. The former one has been widely discussed in the various existing works, while the study on the timetable optimization is limited. In this study, a timetable optimization model is proposed by minimizing the total passenger waiting time at platforms. Further, a genetic algorithm is used to solve the minimization problems in the model. This study uses the data collected from Guangzhou Metro Line 14 and finds that the total passenger waiting time at platforms is reduced by 9.3%. The results indicate that the proposed model can reduce the passenger waiting time and improve passenger service compared with the traditional timetable.

Scenario-Based Integrated Assignment Model for Bi-Class User Rail Transit Networks

2013 ◽  
Vol 361-363 ◽  
pp. 1963-1966
Author(s):  
Wei Zhu
Keyword(s):  
Urban Rail Transit ◽  
Rail Transit ◽  
Transit Networks ◽  
Assignment Model ◽  
Urban Rail ◽  
Proposed Model ◽  
Scale Network ◽  
Algorithm Framework ◽  
Real Scale

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.

Disruption Management in Urban Rail Transit System

2018 ◽  
pp. 18-48
Author(s):  
Erfan Hassannayebi ◽  
Arman Sajedinejad ◽  
Soheil Mardani
Keyword(s):  
Waiting Time ◽  
Recovery Time ◽  
Control Strategies ◽  
Discrete Event ◽  
Urban Rail Transit ◽  
Disruption Management ◽  
Rail Transit ◽  
Transit Systems ◽  
Urban Rail ◽  
Proposed Model

The process of disruption management in rail transit systems faces challenging issues such as the unpredictable occurrence time, the consequences and the uncertain duration of disturbance or recovery time. The objective of this chapter is to adopt a discrete-event object-oriented simulation system, which applies the optimization algorithms in order to compensate the system performance after disruption. A line blockage disruption is investigated. The uncertainty associated with blockage recovery time is considered with several probabilistic scenarios. The disruption management model presented here combines short-turning and station-skipping control strategies with the objective to decrease the average passengers' waiting time. A variable neighborhood search (VNS) algorithm is proposed to minimize the average waiting time. The computational experiments on real instances derived from Tehran Metropolitan Railway are applied in the proposed model and the advantages of the implementing the optimized single and combined short-turning and stop-skipping strategies are listed.

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.

Disruption Management in Urban Rail Transit System

2016 ◽  
pp. 420-450 ◽  
Author(s):  
Erfan Hassannayebi ◽  
Arman Sajedinejad ◽  
Soheil Mardani
Keyword(s):  
Waiting Time ◽  
Recovery Time ◽  
Control Strategies ◽  
Discrete Event ◽  
Urban Rail Transit ◽  
Disruption Management ◽  
Rail Transit ◽  
Transit Systems ◽  
Urban Rail ◽  
Proposed Model

The process of disruption management in rail transit systems faces challenging issues such as the unpredictable occurrence time, the consequences and the uncertain duration of disturbance or recovery time. The objective of this chapter is to adopt a discrete-event object-oriented simulation system, which applies the optimization algorithms in order to compensate the system performance after disruption. A line blockage disruption is investigated. The uncertainty associated with blockage recovery time is considered with several probabilistic scenarios. The disruption management model presented here combines short-turning and station-skipping control strategies with the objective to decrease the average passengers' waiting time. A variable neighborhood search (VNS) algorithm is proposed to minimize the average waiting time. The computational experiments on real instances derived from Tehran Metropolitan Railway are applied in the proposed model and the advantages of the implementing the optimized single and combined short-turning and stop-skipping strategies are listed.

scholarly journals Multiperiod Transfer Synchronization for Cross-Platform Transfer in an Urban Rail Transit System

Symmetry ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1665
Author(s):  
Nan Cao ◽  
Tao Tang ◽  
Chunhai Gao
Keyword(s):  
Waiting Time ◽  
Transfer Problem ◽  
Urban Rail Transit ◽  
Mixed Integer ◽  
Rail Transit ◽  
Multiple Time ◽  
Transit System ◽  
Passenger Demand ◽  
Urban Rail ◽  
Cross Platform

Transfer synchronization is an important issue in timetable scheduling for an urban rail transit system, especially a cross-platform transfer. In this paper, we aim to optimize the performance of transfer throughout the daily operation of an urban rail transit system. The daily operation is divided into multiple time periods and each time period has a specific headway to fulfill time varied passenger demand. At the same time, the turn-back process of trains should also be considered for a real operation. Therefore, our work enhances the base of the transfer synchronization model taking into account time-dependent passenger demand and utilization of trains. A mixed integer programming model is developed to obtain an optimal timetable, providing a smooth transfer for cross-transfer platform and minimizing the transfer waiting time for all transfer passengers from different directions with consideration of timetable symmetry. By adjusting the departure time of trains based on a predetermined timetable, this transfer optimization model is solved through a genetic algorithm. The proposed model and algorithm are utilized for a real transfer problem in Beijing and the results demonstrate a significant reduction in transfer waiting time.

scholarly journals Dynamic Origin-Destination Matrix Estimation Based on Urban Rail Transit AFC Data: Deep Optimization Framework with Forward Passing and Backpropagation Techniques

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Yuedi Yang ◽  
Jun Liu ◽  
Pan Shang ◽  
Xinyue Xu ◽  
Xuchao Chen
Keyword(s):  
Travel Demand ◽  
Urban Rail Transit ◽  
Estimation Methods ◽  
Accurate Estimation ◽  
Rail Transit ◽  
Travel Demand Management ◽  
Transit System ◽  
Passenger Flow ◽  
Urban Rail ◽  
Proposed Model

At present, the existing dynamic OD estimation methods in an urban rail transit network still need to be improved in the factors of the time-dependent characteristics of the system and the estimation accuracy of the results. This study focuses on predicting the dynamic OD demand for a time of period in the future for an urban rail transit system. We propose a nonlinear programming model to predict the dynamic OD matrix based on historic automatic fare collection (AFC) data. This model assigns the passenger flow to the hierarchical flow network, which can be calibrated by backpropagation of the first-order gradients and reassignment of the passenger flow with the updated weights between different layers. The proposed model can predict the time-varying OD matrix, the number of passengers departing at each time, and the travel time spent by passengers, of which the results are shown in the case study. Finally, the results indicate that the proposed model can effectively obtain a relatively accurate estimation result. The proposed model can integrate more traffic characteristics than traditional methods and provides an effective and hierarchical passenger flow estimation framework. This study can provide a rich set of passenger demand for advanced transit planning and management applications, for instance, passenger flow control, adaptive travel demand management, and real-time train scheduling.

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