A simulated annealing algorithm for first train transfer problem in urban railway networks

Kang, Liujiang; Zhu, Xiaomin

doi:10.1016/j.apm.2015.05.008

Cited by 46 publications

(16 citation statements)

References 42 publications

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“…Equations 15,16 and 17 show the additional constraints added to the model by using this method. 12 1 CC …”

Section: Converting the Problem To A Single Objective Problemmentioning

confidence: 99%

“…Synchronization decreases the waiting times of passengers transferring between lines of a network; thus, can lead to a higher level of service and encourages more people to use public transportation. Therefore, synchronization is a common objective which has gained much attention and has been studied in other modes of transportation too, e.g., urban railway networks [12,13].…”

Section: Introductionmentioning

confidence: 99%

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Synchronized Timetables for Bus Rapid Transit Networks in Small and Large Cities

et al. 2019

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The quality of public transportation service has major effects on people's quality of life. During frequency and timetable setting, as an important step of the public transportation planning process, there is an important and complicated issue of synchronization which can directly influence the utility and attractiveness of the system; thus,it should be taken into account during the whole planning process, especially during the frequency timetable setting step. In this paper, a mixed-integer nonlinear programming model is proposed that aims at setting timetables on a bus transit network with the maximum synchronization and the minimum number of fleet size. The proposed model is shown to be applicable for both small and large-scale transit networks by employing it for setting timetables on two samples of both sizes. A simple problem is solvedby GAMS Software where the obtained timetable is substantially reasonable. Moreover, the proposed model is used to set timetables through the genetic algorithm on Tehran BRT networks as a real-life instance; then the NSGA-II is used to obtain the Pareto optimal solutions of the problem for five different scenarios.The overall results show that the proposed model is efficient for setting timetables on transit networks of different sizes.

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“…Equations 15,16 and 17 show the additional constraints added to the model by using this method. 12 1 CC …”

Section: Converting the Problem To A Single Objective Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Synchronized Timetables for Bus Rapid Transit Networks in Small and Large Cities

et al. 2019

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“…Zhou et al [15] proposed a first and last train coordination optimization model considering transfer waiting time for the first and last train based on passengers route choice behaviors and designed the genetic algorithm. Kang et al [16] and Kang et al [17] established a first train coordination model and a last train mean-variance model; and both were solved via simulated annealing algorithm. Kang et al [18] developed a last train network transfer model to maximize the passenger transfer connection headways.…”

Section: Literature Reviewmentioning

confidence: 99%

Transfer Coordination for Metro Networks during the Start- or End-of-Service Period

Ning

Zhao

et al. 2018

Mathematical Problems in Engineering

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When travelling via metro networks during the start-or end-of-service period, transferring passengers may suffer a transfer failure. Accordingly, the synchronization timetabling problem necessitates consideration of transfer waiting time and transfer availability with respect to the first or last train. Hence, transfer train index (TTI) is formulated to identify the transfer train and calculate the transfer waiting time. Furthermore, two types of connection indexes, the last connection train index (LCTI) and the first connection train index (FCTI), are devised to distinguish transfer failure from transfer success, and the penalty constraints are implemented together to reflect the adverse effects of transfer failure. Then, a mixed integer programming model is developed to concurrently reduce transfer waiting time and improve transfer availability, which can be solved by CPLEX. Finally, a case study on Beijing metro network is made to verify the method. Experimental results show that our proposed model can yield synchronization solutions with significant reductions in both the average transfer waiting time and the proportion of transfer failure passengers.

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“…Produce values at the picked positions based on the Solution Generation Algorithm [38], while meeting constraints (8) and (9).…”

Section: Tabu Search Algorithmmentioning

confidence: 99%

Last-Train Timetabling under Transfer Demand Uncertainty: Mean-Variance Model and Heuristic Solution

Yang

Gao

et al. 2017

Journal of Advanced Transportation

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Traditional models of timetable generation for last trains do not account for the fact that decision-maker (DM) often incorporates transfer demand variability within his/her decision-making process. This study aims to develop such a model with particular consideration of the decision-makers' risk preferences in subway systems under uncertainty. First, we formulate an optimization model for last-train timetabling based on mean-variance (MV) theory that explicitly considers two significant factors including the number of successful transfer passengers and the running time of last trains. Then, we add the mean-variance risk measure into the model to generate timetables by adjusting the last trains' departure times and running times for each line. Furthermore, we normalize two heterogeneous terms of the risk measure to provide assistance in getting reasonable results. Due to the complexity of MV model, we design a tabu search (TS) algorithm with specifically designed operators to solve the proposed timetabling problem. Through computational experiments involving the Beijing subway system, we demonstrate the computational efficiency of the proposed MV model and the heuristic approach.

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A simulated annealing algorithm for first train transfer problem in urban railway networks

Cited by 46 publications

References 42 publications

Synchronized Timetables for Bus Rapid Transit Networks in Small and Large Cities

Synchronized Timetables for Bus Rapid Transit Networks in Small and Large Cities

Transfer Coordination for Metro Networks during the Start- or End-of-Service Period

Last-Train Timetabling under Transfer Demand Uncertainty: Mean-Variance Model and Heuristic Solution

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